Cover page

SKIM

Sea surface KInematics Multiscale monitoring

taking the pulse of oceans from Equator to polar seas

The SKIM Team:

Fabrice Ardhuin (LOPS),	Erik van Sebille (UU),	Justin Stopa (LOPS),
Sophie Cravatte (LEGOS),	Oyvind Breivik (Met.No),	Peter Brandt (GEOMAR),
Frédéric Nouguier,	Alexis Mouche	Bertrand Chapron (LOPS),
Christine Gommenginger,	Yevgeny Aksenov &	George Nurser (NOC),
Anton Korosov,	Johnny A. Johannessen &	Laurent Bertino (NERSC),
Marion Tissier &	Ad Reniers (T.U. Delft),	Lotfi Aouf (Meteo-France),
Clément Ubelmann (CLS),	Céline Tison &	Gérald Dibarboure (CNES),
Fabrice Collard &	Lucile Gaultier (OceanDataLab),	Alberto Naveira-Garabato (U. Southampton)
Melisa Menendez (EHI-UC),	Eléonore Stutzmann (IPGP),	Frederic Dias (Univ. College Dublin),
Ernesto Rodriguez (JPL),	Will Perrie &	Hui Shen (BIO, Halifax),
Dany Dumont, &	Cédric Chavanne (UQAR),	Rosemary Morrow (LEGOS)
Shane Elipot (RSMAS),	Pierre-Marie Poulain (OGS),	Francisco Ocampo-Torres (CICESE),
Michel Campillo (ISTerre),	Agnieszka Herman (U. Gdansk),	Tarmo Soomere (Estonian Acad. Sciences),
Joana Staneva (HZG),	Jean-Raymond Bidlot (ECMWF),	Alvaro Semedo (UNESCO-IHE),
Luigi Cavaleri &	Alvise Benetazzo (ISMAR),	Jordi Isern-Fontanet (ICM),
Jamie Shutler (Univ. Exeter),	Déborah Idier (BRGM),	Peter Sutherland (LOPS),
Harald Johnsen &	Geir Engen (NORUT),	Chris Bean (Dublin Inst. Adv. Studies),
Romain Husson &	Estelle Obligis (CLS)	Danièle Hauser (LATMOS).

About this document: This proposal has been prepared by the above-mentioned team (see Annex F for detailed affiliations and organization). Thalès Alenia Space and CNES have contributed to the definition of the space and ground segments and cost evaluation. All this work has been coordinated by the Lead investigator who is ready to act as the ESA MAG chair in the event the mission is selected for further studies:

Fabrice Ardhuin, Senior scientist at CNRS

Laboratoire d'Océanographie Physique et Spatiale

(Joint research unit of Univ. Brest, Ifremer, CNRS and IRD)

rue Dumont d'Urville 29200 Plouzané

e-mail: ardhuin@ifremer.fr

Acknowledgements:

Besides the SKIM team members, we would like to thank the following experts for their technical support in the preparation of this proposal:

Jean-Claude Lalaurie, Juliette Lambin, Denis Carbonne, CNES radar team (DCT/SI/IP-TR), for mission design, product and ground segment definition based on CFOSAT heritage

Eric Caubet, Nicolas Thiry & Patrice Laurenti, Thales Alenia Space, France for the instrument definition and space segment design;

Marie-José Lefevre, CNES (phase 0 team) for overall review of the proposal

Yury Yurovsky and the whole MHI Sebastopol group for sharing their Ka-band Doppler analysis

Jonathan Gula and Pierrick Penven for ocean current simulations over the Agulhas current

Georgy Manucharyan (Caltech) and Dimitris Menemenlis (JPL/NASA) for providing ocean circulation model output and processing tools

Yi Chao for providing ocean circulation model output and processing tools

Jiping Xie for the NERSC/TOPAZ OSSE calculations

Mickael Accensi for assembling ocean circulation datasets from various sources and testing the SKIMsimulator

The CMEMS global forecasting center, for providing ocean circulation model output and feedback on this proposal

The PIRATA team for providing real-time ocean current data in the tropical Atlantic

Denise Guillerm and Colette Kerbrat for assistance

Funding for this support was provided by CNES in the frame of the VASCO (Velocity Assessment of Surface Currents in the Ocean) phase 0 study

Cover: multi-scale illustrations of currents and waves

- upper left: divergence of surface current from modeled (CMEMS global 1/12° model) and simulated SKIM Level 3a product, in the equatorial Atlantic..

- upper center: R/V Antea servicing the tropical Atlantic array PIRATA, photo courtesy Jacques Servain.

- upper right: impact of waves from the March 11 2008 “Johanna” storm at Le Conquet, France ((c) Steven Lamarche).

- lower left: aerial photograph from an Aerosonde UAV off Antarctica showing stormy seas with ice and waves in pancake ice http://ciresblogs.colorado.edu/antarcticuavs/2012/10/02/aerial-photos/

- lower center: modeled currents in Fram Strait (courtesy G. Manucharyan & D. Menemenlis) with overlaid SKIM footprint locations for a single pass

- lower right: Simulated Level3b (multiple-pass) SKIM product in Fram Strait: several satellite passes on the same day lead to high resolution and accuracy in the Marginal Ice Zone.

CONTENTS

1.Cover page	1

2.Executive Summary	7

3.Science and mission objectives, requirements and justification	9

3.1.Mission objectives and rationale	9

3.1.1.General context	9

3.1.2. Knowledge and observational gaps: SKIM objectives	10

3.1.3. Short-term scientific goals	11

3.1.4 Longer-term scientific goals	18

3.1.5. Beyond science: applications and societal impact	20

3.2. Mission duration and relation to other missions	21

3.3. Geophysical variables and data products	21

3.3.1. Geophysical variables	21

3.3.2. Data products (From Level 1b to level 4)	23

3.4. Development status of retrieval algorithms	23

3.4.1. Total surface current or ice drift	23

3.4.2. Directional wave spectrum	25

3.4.3. Exploratory developments	26

3.5 Relevance to ESA EO strategy	26

3.5.1. ESA Living Planet program	26

3.5.2. Climate Change Initiative (CCI)	26

3.6. Relevance to other programs	26

3.7. Compliance with the ESA EE9 selection criteria	28

4.Mission assumption and technical requirements	29

4.1. Introduction	29

4.2. Observation techniques	30

4.2.1. Measurement sequence: definition of beams, modes, cycles...	30

4.2.2. Doppler centroid and surface velocity vector	31

4.2.3. Ocean wave spectra	52

4.2.4. Measurements over sea ice	56

4.2.5. Delta-K processing	58

4.3. Observation requirements	59

4.3.1. Geometry and observing conditions	59

4.3.2. Revisit	59

4.3.3. Incidence angles	60

4.3.4. Coverage	60

4.3.5. Resolution	60

4.3.6. Accuracy	61

4.3.7. Calibration and validation	61

4.3.8. Summary of SKIM mission requirements	62

5. Proposed mission architecture	64

5. 1. General concept	64

5.2.Space segment	64

5.2.1. SKIM payload	65

5.2.2.Instrument technical solution	65

5.2.3. Satellite architecture	85

5.3. Ground segment	93

5.3.1. Overview of the architecture	93

5.4. Mission analysis and operational concept	95

6. Programmatic elements	97

6.1. Instrument outline Design, Development and Verification plan	97

6.2. Satellite development plan	97

6.3. Space segment cost breakdown	101

Annex A : Changes compared with the 2016 SKIM proposal	102

A.1. Science objectives	102

A.2. Instrument design	102

A.3. Performance and error analysis: from SRL3 to SRL4 and 5	102

Annex B: Mission performance	103

B.1. Methodology: science to level 2 product needs and level 1B requirements	103

B.2. Simulation / performance modelling tools	103

B.2.1. “SKIMSimulator”	103

B.2.2 “SeaMotion Radar”	104

B.3 Outline of algorithm to be used for geophysical retrievals	116

B.3.1 Surface velocity (primary processing with pulse-pair)	116

B.3.2 Wave spectra and G	118

B.4 Quantitative examples of output simulator	118

B.5. Quantitative results from simulator proving required performance	119

Annex C: Elements for a Draft Mission Requirements Document	121

C.1. Introduction	121

C.2. Mission background and justification	122

C.2.1. Knowledge and observational gaps: why SKIM makes a difference	122

C.3. Mission objectives	122

C.3.1. Short-term scientific goals	122

C.3.2 Longer-term scientific goals	131

C.3.3. Beyond science: applications and societal impact, particular case of CMEMS	133

C.4. Mission requirements	133

C.5. Observation techniques and requirements	135

C.6. Data products and usage	136

C.6.1. Direct data usage and level4 products	138

C.6.2. Model verification and calibration	138

C.6.3. Data assimilation	139

C.7. Synergies and international context	143

C.7.1. Synergies with Sentinels 1, 2 & 3	143

C.7.2. Synergies with nadir altimeters	144

C.7.3. Synergies with SWOT	144

C.7.4. Other aspects and possible additional payloads	144

Annex D: acronyms	145

Annex E: references	146

Annex F: SKIM team and community for research and applications	153

F.1 Identified user groups by application fields	153

F.1.1 marginal ice zones: currents, ice & waves	153

F.1.2 Ocean circulation	154

F.1.3 Ocean waves, including wave-current interactions	154

F.1.4 coastal hazards & Coastal engineering	154

F.1.5 air-sea interactions & marine meteorology	155

F.1.6 ambient seismic and acoustic noise : solid earth and upper atmosphere	155

F.1.7. coastal ecosystems	156

F.1.8. wave physics, statistics & applied mathematics	156

F.1.9 climate change	156

F.1.10 Marine energy & ocean engineering	156

F.1.11 Remote sensing	157

F.2 Curriculum vitae of all team members	158

F.3 Institutions profiles	181

Annex G : additional electronic information	186

G.1 Recent publications	186

G.2 Modeled “truths” and simulated SKIM L2 / L3 products	186

G.3 Other links	186

Annex H: Accompanying letter from the French national space agency	187

List of tables

Table 3.1: Geophysical parameters derived from SKIM measurements. See section 4 for justification of SRL and error budget (in particular tables 4.1 and 4.5).	22

Table 4.1: Summary of expected errors for L2 velocity product based on the preliminary algorithms proposed in this proposal in the case of the SKIM2 configuration (open burst, 8 beams).	32

Table 4.2: Summary of expected errors for L2 velocity product based on the preliminary algorithms proposed in this proposal in the case of the SKIM3 configuration (closed burst, 8 beams).	32

Table 4.3: Single shot Signal to Noise Ratio in the Line of Sight.	33

Table 4.4: Pointing budget in elevation for nadir and offset beams, in flight, at 3 sigma.	37

Table 4.5: Pointing budget in azimuth, at system level, at 3 sigma.	38

Table 4.6: Summary of effective resolutions and errors of L3a (single swath) and L3b (multiple swaths over time) surface velocity products for the SKIM2 set-up. The effective resolution is the wavelength at which the along-track spectrum of the cross-track current component crosses the noise level.	51

Table 4.7: Summary of mission requirements	63

Table 1: Summary of main instrument parameters.	69

Table 2: Single shot Signal to Noise Ratio in the Line of Sight.	77

Table 3: Instrument data rate and volume (per orbit)	79

Table 4: Payload Mass-Power-Volume budgets	80

Table 5: SKaR CEU TRL definition	84

Table 6: SKaR TRL definition as of June 2017.	84

Table 7: SKIM launcher compatibility	85

Table 8: Nominal performance of the Proteus150 platform	87

Table 9: Satellite mass budget.	90

Table 10: Satellite power budget.	90

Table 1: Timing of satellite development milestones	97

Table B.1: Summary of effective resolutions and errors of L3a (single swath) and L3b (multiple swaths over time) surface velocity products for the SKIM2 set-up. The effective resolution is the wavelength at which the along-track spectrum of the cross-track current component crosses the noise level.	120

Table C.1: Mission requirements summary	134

Table C.2: Instrument parameters.	135

Table C.3: Instrument on-board processing (for all beams)	135

Table C.4: Instrument performances	136

Table C.5: Geophysical parameters derived from SKIM measurements. See section 4 for justification of SRL and error budget (in particular tables 4.1 and 4.6).	137

Executive Summary

New data needed for urgent challenges - Because the ocean surface is the interface between ocean, atmosphere and land, the ocean currents play an important role in defining the fluxes of heat, momentum, carbon, water, etc. between Earth System components. The ocean surface velocity combines surface currents (mainly driven by winds, density gradients, and tides), with wave-induced drift, known as Stokes drift. This total velocity transports surface heat, salt and everything that is in the upper ocean, natural or man-made, including microplastics. While largely improving over the passed satellite era, there is still a false sense of knowledge on ocean currents and waves. Satellite altimeters have been around for over 20 years, but along-track sea level anomaly and significant wave height are not enough to characterize the multi-scale motions of the oceans.

This is particularly true in the tropics where geostrophy explains a small fraction of surface currents and does not give access to the very important divergence and upwelling, the structure of which is poorly known. This is also true at the ice edge. The Arctic is going through a major regime change, with a widening Marginal Ice Zone (MIZ). A strong coupling is expected at the ice edge between ocean currents, ice, waves and atmosphere that is very difficult to observe today. It is urgent to add kinematic variables (surface current, ice drift, waves) that can be resolved at the relevant scales, smaller than those at which dynamic variables (sea level, wind stress) are available today.

Marginal Arctic seas are opening to more storms. Like everywhere else in the world, the spectral distribution of wave energy is required to investigate extreme sea levels. This spectral distribution is only available for long swells today with wave mode SARs, and it is a particular challenge to observe shorter waves that dominate in semi-enclosed seas, from the Mediterranean to the Baltic or Beaufort seas. Such a detailed sea state measurement would have far-ranging applications from ocean remote sensing with other missions to solid Earth monitoring with better known microseism sources.

What is SKIM and what it will measure - SKIM will open the era of satellite Doppler oceanography, combining nadir and near-nadir radar measurements. It will measure total ocean surface velocity vector (primary goal), using a Doppler technique that is more direct than altimetry for accessing surface currents, and ocean wave spectra (secondary goal). Planned as a five-year explorer experiment, this mission will address central questions for the understanding of our evolving climate, from the tropical current systems, to the rapidly evolving Arctic marginal ice zone. SKIM will demonstrate the maturity of Doppler oceanography, and pave the way for future Doppler missions. The proposed mission uses a high-resolution Ka-band Doppler altimeter, measuring at nadir and rotating off-nadir beams (0, 6 and 12° incidence angles). The expected accuracy on horizontal current velocity is 0.1 m/s, at a resolution of about 40 km. Flying on the 12 day-cycle Sentinel 1 (S1) orbit with a shift in longitude, SKIM will have a swath of 270 km that will allow overlap with S1 data and an ideal synergy for currents and wave sampling. SKIM will cover the world ocean up to 82° N, with daily coverage at latitudes beyond 70°, and an average of three passes per cycle at the Equator. This includes the Southern Ocean and both Arctic and Antarctic marginal seas. SKIM will image ocean current features, including eddies of diameter 80 km and more, a considerable extension of planned satellite altimeter missions capabilities. SKIM will also provide directional wave spectrum down to wavelengths of 20 m, and full azimuth coverage without direction ambiguity: these two aspects are clear improvement on existing and planned missions, giving a global coverage of oceans and regional seas.

From 2016 to 2017: a matured community and proposal - A multidisciplinary research community on ocean surface currents has emerged in recent years, thanks in part to the Globcurrent project, the development of analysis and processing techniques for swath altimetry in the framework of the SWOT mission, a better understanding of near-nadir Ka-band properties, the preparation for new types of spectral wave data from CFOSAT, and growing concern over microplastics and other contaminants. This particular context gave birth to the concept of the “Sea surface KInematics Multiscale monitoring” mission. Starting a few months before the 2016 EE9 call deadline, work towards the present proposal took a new dimension when the first end-to-end performance analysis showed that SKIM could be an effective follow-on of both SWOT for small-scale ocean currents and CFOSAT for waves, as well as a pathfinder for Doppler oceanography from space. Doppler oceanography has the unique capability to reveal the complex structures of equatorial upwellings and dynamics of marginal ice zones. The general design of SKIM was laid out in the 2016 proposal. The present proposal adds an end-to-end performance and error analysis, leading to adjustments in the measurement cycle, number of beams, etc., and a completely renewed proposing team, contributing to a mature proposal.

Innovation in SKIM lies in the combination of an improved altimeter design (high pulse repetition frequency, SAR mode, Ka-band), its integration with a proven rotating beam system inherited from the CFOSAT mission, and the use of Doppler measurements for both currents (Doppler centroid) and waves (Doppler modulation in range). The mission design also includes the capability for promising but less mature processing based on a Delta-K technique. SKIM could also carry an AIS receiver.

Mission objectives: SKIM objectives combine direct and short-term goals with a mature community and scientific basis, with a scientific readiness level or “SRL” of at least 4, including most steps needed for the demonstration of end to end performance (SRL 5),

- monitoring tropical currents and the structure of equatorial upwellings,

- observing dynamics and kinematics at the rapidly evolving marginal ice zones,

- providing sea state parameters relevant for coastal hazards and extreme waves,

- allowing a better understanding of other remote sensing data (e.g. L-band radiometry from SMOS, nadir altimetry, scatterometer winds…) thanks to spectrally resolved sea states and joint wave and current measurements.

Longer term goals appear more exploratory today (SRL 3) but will be achievable by 2025,

- observing wind stress vector fields over oceanic mesoscales

- characterizing strong submesoscale surface velocities

- mapping sources of microseisms for solid Earth monitoring

SKIM and the Earth Observation challenges - SKIM is primarily an “ocean mission” but it addresses challenges highlighted in ESA’s living planet programme (ESA-SP-1329/2), beyond the ocean. SKIM directly addresses C1 “Regional and seasonal distribution of sea-ice mass and the coupling between sea ice, climate (...)”, O2 "Mesoscale and submesoscale circulation", O4 "Physical and biogeochemical air-sea interaction processes", O5 “Sea level changes from global to coastal scales (...)”. SKIM will also contribute to the "Global Trends" strategy (improved modelling of air-sea coupling, oceanic vertical mixing and carbon uptake), and is related to the "increased exposure to disasters" (new observing capability for environmental monitoring and management). SKIM contributes to stimulating European economic growth and technological innovation, bringing unique new technological capabilities to augment existing international high resolution observing systems (e.g. Sentinel-1 & Sentinel-3, SWOT), supporting the evolution of European and international missions. More importantly, SKIM looks at the ocean in the context of the whole Earth system from the center of the core – hence the support of solid Earth scientists.

Time to fly: why EE9 is the right time for SKIM - After exploratory Doppler analysis of Envisat and Sentinel 1, SKIM will further pioneer the use of directional Doppler analysis to be the first mission capable of a global monitoring of surface vector currents, consistently derived with directional wave data. Coming at a time of transition in the Arctic, SKIM will be the first mission to measure currents and ice drift in the Marginal Ice Zone. SKIM will also constrain the coming atmosphere-wave-ocean coupled systems. This first space-borne mission for ocean kinematics will also demonstrate and assess different possible methods for surface current retrieval. This data will also help understand the Ka-band interferometric data from SWOT and provide a useful nadir altimetry beam, complementing the altimetry constellation. With unprecedented detailed measurement of directional sea state properties, useful for monitoring and correcting sea state biases, SKIM can become the prototype of future satellite altimeters, jointly measuring sea level, waves and currents.

Science and mission objectives, requirements and justification

Mission objectives and rationale

General context

The ocean surface velocity combines surface currents, driven by winds, pressure gradients, and surface or internal tides, with wave-induced Stokes drift, and rapid velocity fluctuations with the wave orbital velocities (of the order of 1 m/s). This total multi-scale velocity field redistributes heat, freshwater, ice, and everything that is in the upper ocean, natural or man-made, including microplastics.

Because the ocean surface is the interface between ocean, atmosphere and land, currents play an important role in defining the fluxes of heat, momentum, carbon, water, etc. between Earth System components. For this reason, ocean surface currents are a key variable in tropical ocean monitoring systems (Cravatte et al. 2016). Currents are also the main cause of ocean wave variability at scales under 200 km (Ardhuin et al. 2017b). Wave observations from space are currently limited to very few parameters, and have limited spatial and temporal resolution that cannot fully resolve the variability of the sea state.

Resolving finer scales of the ocean circulation (e.g. Klein and Lapeyre 2009) is a key challenge for quantifying vertical fluxes of nutrients and carbon, and the budget of mechanical energy of the ocean. As a result, the scientific community and space agencies have devoted much effort to go beyond nadir altimetry. An important result is that a wide swath is necessary to monitor the smaller ocean structures that evolve more rapidly. Interferometric SAR technology will be used to produce a 120 km wide swath for the Surface Water Ocean Topography (SWOT) mission (see Figure 3.1).

Figure 3.1: Simulated ocean currents over the Gulf Stream (Courtesy D. Menemenlis, JPL) and associated SKIM and SWOT simulated observations for a single satellite pass. The SWOT simulated current assumes that the current is in geostrophic equilibrium with the sea surface height (SSH), which introduces small scale noise associated to ageostrophic motions.

Instead of ocean topography, one can measure velocities directly. The feasibility of such measurements was demonstrated at global scales by Chapron et al. (2005) with Envisat ASAR: the Doppler centroid provides a measurement of a mean ocean surface velocity, in the line of sight, as detected by a radar. Although noisier, this is equivalent to Across-Track Interferometry (Romeiser et al. 2014). This Doppler information then contains a “Doppler wave bias” (UWB) that is proportional to the surface Stokes drift velocity US along the look direction (Chapron et al. 2005, Martin et al. 2016, Nouguier et al. submitted). For long-term averaged currents (e.g. Rouault et al. 2010), a proxy for UWB may be given by the wind component in range. To get a higher temporal resolution, this proxy is not sufficient. Indeed, for a given wind speed, waves introduce +/- 40% (rms) variations in US (Ardhuin et al. 2009) and thus UWB. Numerical wave models today have a relative root mean square accuracy of the order of 20% for US (Rascle and Ardhuin 2013). A clear solution is thus to measure the necessary ocean wave properties needed to correct for UWB. The design of SKIM builds on this strategy: measuring both the full vector current and the full directional wave spectrum. Wave data also allow a large range of scientific and societal applications.

3.1.2. Knowledge and observational gaps: SKIM objectives

Ocean observation from space has revolutionized the understanding of large scale ocean dynamics, i.e. the forces that set the ocean in motion. A particular role was been played by the following techniques.

- altimetry measures sea level anomalies relative to a mean dynamic topography, or sea level relative to the geoid (Bruinsma et al. 2013). There are difficulties near the coast and over sea ice, and at wavelengths under 100 km (e.g. Dibarboure et al. 2014), that translate into noisy estimates of the geostrophic part of currents, especially near the Equator.

- gravimetry measures the geoid at wavelengths larger than 100 km, which can provide a mean dynamic topography at the same scale (e.g. Rio et al. 2014).

- scatterometry gives access to the surface wind speed vector.

The main balance of forces at the mesoscale and larger scales (50 km and more) involves the Coriolis force and the pressure gradient associated with the sea level slope. Hence ocean surface currents can be diagnosed from the sea level (e.g. Ducet et al. 2000, Penven et al. 2014). Yet, the actual kinematics contain higher resolution ageostrophic motions and high frequency signals that are only observed by High-Frequency radars within 300 km of coasts (e.g. Gurgel et al. 1999), or sparse near-surface drifters (Lumpkin and Pazos 2007, Elipot et al. 2016). Other remote sensing techniques using visible image processing have been able to produce displacement vectors.

One method that uses kinematics is the monitoring of sea ice drift from radiometer data or SAR (e.g. Kwok et al. 1990), but even this technique breaks down in the Marginal Ice Zone (MIZ) where ice floes are broken up by wave action, as shown on Figure 3.2.

Figure 3.2: Left: in Fourier space, the spectrum represents the distribution of the surface elevation variance in wave frequencies and directions at a single point of the ocean. When integrated over the spectral dimensions this variance is proportional to the wave height squared. Center: Operational wave models today resolve kilometer-scale variation in wave heights, with little validation data at that scale. The only comparable data are radar and optical imagery from, for example, Sentinel 1 and Sentinel 2 (right).

With the expected widening of the MIZ in the Arctic (e.g. Aksenov et al. 2017), this expanding and important region of the world ocean will not be well monitored in terms of currents by existing and planned satellite missions. This poor monitoring is also true for the equator and coastal regions.

Identified knowledge gaps where progress is needed and where SKIM could make a difference include (non exhaustively),

- boundary currents at the edge of the Marginal Ice Zone and current-waves-ice interactions,

- equatorial currents and wind-driven circulation near the Equator,

- western boundary current and circumpolar current dynamics,

- wave-current interactions: upper ocean mixing, drift, sea states,

- air-sea fluxes: wind stress vector τ, energy flux τ ∙ U from wind to ocean circulation,

- nearshore wave properties and extreme coastal sea levels,

- directional wave properties and microseism / microbarom sources,

- remote sensing: waves affect a wide range of measurements (sea level, salinity, wind, ocean color).

These are detailed below, and associated to science and mission objectives. They include objectives for which SKIM alone can provide a full monitoring with a reasonable revisit time near the equator (and a shorter time at higher latitudes), over the lifetime of the mission. For other objectives related to faster evolving processes, SKIM can provide essential data required to correct model biases and systematic errors. SKIM will also be a pathfinder for future satellite altimetry missions that may want to add off-nadir measurements to fulfill these goals.

We have separated the scientific goals into short-term objectives for which clearly identified questions are addressed by active research communities, and longer term emerging objectives for which the ground-breaking nature of SKIM should lead to important progresses that still have to be anticipated by a wider scientific community and/or for which the algorithms are yet to be developed.

3.1.3. Short-term scientific goals

Because of the fast evolution of the Arctic, with dramatic changes that can be monitored during a space mission lifetime, and the easy to achieve fast sampling around 75°N, we first focus on the Arctic marginal ice zone, after defining SKIM observables in the context of upper ocean dynamics.

3.1.3.1. UPPER OCEAN DYNAMICS: CURRENT, MIXING, DRIFT & WIND WORK

Defining the total current and its components

The total surface current vector (TSCV), denoted U below, with a norm U, is defined from the average drift velocity of water parcels. This Lagrangian mean drift is U = UE+US , the sum of a quasi-Eulerian current (Jenkins 1989) UE and a Stokes drift US (Stokes 1849). US is the drift motion due to waves that arises from a correlation between the displacement and velocity field. Using linear wave theory, the Stokes drift is readily computed from the directional wave spectrum (Kenyon 1969, see also Peureux et al. 2017). US at the sea surface is of the order of 1.0 to 1.8 % of the wind speed, typically larger than the local wind-induced quasi-Eulerian current known as the Ekman current (e.g. Ardhuin et al. 2009), unless a strong stratification is present (see figure 3.3).

Figure 3.3: Near-surface modeled profiles of quasi-Eulerian UE and Stokes US velocities. Left: snapshot of UE = (UE,VE) with a 20 m deep mixed layer, Center: without stratification. In these two panels, depth is normalized by the significant wave height Hs due it is role in upper ocean mixing (Agrawal et al. 1992). Right: average profiles over 3 months, during the LOTUS3 experiment (Price et al. 1987) and modelled with a variable stratification. Symbols indicate measured values which presumably are UE but rather correspond to UE + US/2 (hence the question mark). Dashed line correspond to a model without Stokes-Coriolis effect. Adapted from Rascle and Ardhuin (2009).

US is the part of the total current that is most surface-intensified, so that the associated depth-integrated transport is typically less than a third of the Ekman transport, and it is in fact canceled out by the transport driven by the Stokes-Coriolis effect (Hasselmann 1970, Xu and Bowen 1994, Rascle and Ardhuin 2009). In contrast, UE is expected to be fairly uniform close to the surface with horizontal and vertical modulation by Langmuir circulations. This UE is thus comparable to the velocity of drifters drogued at 15 m (Collard et al. 2010). The Lagrangian current U is closely related to coastal High-Frequency radar measurements (e.g. Gurgel et al. 1999, Ardhuin et al. 2009). At large space and time scales (larger than the Rossby radius of deformation and twice the inertial period), UE is in geostrophic balance: i.e. the sea level deforms to produce a pressure gradient that balances the Coriolis force. UE may thus be estimated from satellite altimeter data. However, time series at smaller time scales are generally dominated by near-inertial oscillations for mid to high latitudes, e.g. these oscillations have a period of 33 hours at a latitude of 21°N ( Figure 3.4).

Figure 3.4: Snapshot (left) and time series (right, dashed lines) of near-surface current, provided by the CMEMS 1/12° global ocean model at two tropical locations in the Atlantic ocean and the 50°N station PAPA in the Pacific, showing the importance of mesoscale currents and near-inertial motions. Overlaid solid lines are measured time series at (21°N,23°W) and (0°N, 23°W) are from the PIRATA moorings (Bourles et al. 2008).

The upper ocean surface mixing that controls the other component of UE (Sutherland et al. 2014) is believed to be largely controlled by Langmuir circulations or LCs (e.g. Noh 2004, Cavaleri et al. 2012). The source of energy of LCs is the stretching of ocean turbulence by the Stokes drift, which is of the order of the wind stress times the Stokes drift (Kantha and Clayson 2004).

Energy fluxes

A first component of the air-sea energy flux that can be estimated from SKIM is the work of the wind on the surface current which was discussed above and is a key term in the ocean energy budget (e.g. Ferrari and Wunsch 2009). A possible retrieval of wind stress is discussed in 3.1.4.1 below, otherwise a bulk formula approximation τ≃Cd U10 U10 will be used (e.g. Edson et al. 2013). The turbulent kinetic energy produced by breaking waves and the Stokes drift straining of turbulence are two other important fluxes. The sea state is an important component of atmospheric analysis and prediction models (e.g. Uppala et al. 2005) and operational coupled forecast models (e.g. Breivik et al. 2015). Key aspects are the determination of momentum and heat fluxes at the air-sea interface, and the proper parameterization of wave-related upper-ocean mixing which controls the diurnal cycle of ocean surface properties and air-sea fluxes. Measurements of the surface Stokes drift vector together with the wind stress vector will provide a direct estimate of the flux of energy to Langmuir turbulence (e.g. Ardhuin and Jenkins, 2006; Belcher et al. 2012; McWilliams 2016).

3.1.3.2. PROCESSES AT THE MARGINAL ICE ZONE

The Arctic Ocean is changing rapidly with thinner, less compact, more fragmented and mobile sea ice (Rampal et al., 2009). The Marginal Ice Zone is predicted to become the dominant Arctic ice province in the summer by the 2050s, as illustrated in figure 3.5. How fast this will actually happen is not clear. Recent experiments in the Beaufort sea are already showing an unprecedented dominance of pancake ice in October (Thomson et al. 2017). The scientific community must therefore be ready to observe and understand these changes.

Figure 3.5: Left: Fraction of Arctic sea ice covered by the MIZ (sea ice concentration between 15 and 80%), as projected for the 21st century monthly mean (solid) in winter (December–February; blue lines) and summer (June–August; red lines). The shading denotes one standard deviation (Aksenov et al., 2017). Inset: changes in MIZ width in in summer (June–September, red line) and winter (February–April, blue line) 1979–2011 from satellite data (thick: mean, thin: mean+/- one std, dashed: trend, adapted from Strong and Rigor, 2013). Right: Surface ocean currents modulus (solid red line) and sea ice (solid green line), along with ice fraction across the MIZ (blue dashed line), adapted from Rynders (2017).

As the MIZ expands, ocean waves propagate further into the sea ice cover, breaking more ice and inducing greater lateral melt and more vigorous stirring of the upper ocean from air drag and floe motion (Zhang et al., 2015). This can enhance ice melting, deepening, and freshening of the oceanic mixed layer and its warming from the solar insolation, resulting in enhanced ice melt and a strong positive feedback to the sea ice decline (Tsamados et al, 2015).

Upwelling at the sea ice edge can generate eddies and ice edge oceanic jets (e.g., Bulczak et al., 2015). A related phenomenon is the fast-moving sea ice, so-called ice jets (see figure 3.2 and 3.5). Possible causes of the ice jets are the atmospheric and oceanic jets and variation in the surface roughness sea ice near the ice edge (Heorton et al., 2014) or granular dynamics of sea ice in MIZ (Feltham 2005; Rynders 2017). An example simulated jets with strong acceleration of the ocean currents and sea ice drift in the MIZ are shown in Figure 3.2. The jets can interact with waves through complex coupling mechanisms, including mixing by Langmuir circulations, with a major effect on the near-surface mixing and heat balance.

Over the last 20 years, significant wave heights (Hs) in the Arctic have increased at the rate of 7% to 10% per decade, with Hs doubling in the current century (Francis et al, 2011; Khon et al, 2014; Stopa et al, 2016; Thompson et al, 2016; Aksenov et al, 2017). This could result in an increased wave-induced sea ice breakup and a higher wave energy flux towards the Arctic coastline, accelerating coastal erosion, permafrost collapse and potentially increasing methane release (Overeem et al, 2011; Thomson et al, 2016). Jones et al (2009) detected increased erosion of the Alaskan Coast during 1970s-1990s and 2000s, with average erosion rates exceeding 25 m/year.

Given today’s poor knowledge of ice edge processes, there is a need to have a basin-scale monitoring of currents and waves in both open water and in the marginal ice zone.

3.1.3.3. EQUATORIAL CURRENTS AND WIND-DRIVEN CIRCULATION

Near the Equator, equatorial geostrophy only gives an estimate of a small part of the surface current variability, and in particular does not resolve time-dependent divergent currents near the equator. Short period and nonlinear processes often dominate and so far can only be observed by in-situ techniques (Schlundt et al. 2014). For example, Tropical Instability Waves (TIWs) with a near-30 day period often dominate the meridional velocity at the equator (V component in Figure 3.4). The chaotic nature of the TIW generation does not allow us to reproduce strength and phases of these waves in forced ocean simulations. However, also in assimilation models, the constraints provided by assimilated data (sea surface height and temperature, as well as profiles of temperature and salinity) are often too weak to produce agreement between simulated currents and currents observed by in-situ methods (e.g. Figure 3.4). The zonal component, particularly at lower frequency, is often better reproduced in ocean models as it is dominantly forced by the wind and generally follows equatorial geostrophy. However, surface current products purely derived from observations (sea surface height, temperature, and wind stress) still often fail in reproducing zonal and meridional velocity components in the equatorial ocean and the use of these products is of limited value (Schlundt et al. 2014).

Equatorial Kelvin and Rossby waves play a key role in the basin-scale adjustment of the thermocline during El Niños or for the establishment of the seasonal Wyrtki jet in the Indian Ocean. The divergent current associated with these waves (which displace the thermocline and eventually influence the surface temperature and atmosphere above) are not resolved by the “equatorial geostrophy.” Similarly, the cross-equatorial surface transport (which for example plays an important role in the seasonal heat balance of the Indian Ocean) is currently only estimated indirectly from the wind.

Figure 3.6: Distribution of near-surface drifters on February 6, 2017. Colors correspond to countries of origin. Note the lack of drifters near the Equator due to their wind-driven divergence away towards the tropics.

Drifters have helped improve on altimeter-derived currents, but they are sparse in divergent flow regions like the equatorial or coastal upwelling regions (Figure 3.6). This is why, the Tropical Pacific Observing System report recommends a “Continuation of technological developments to measure ocean surface currents remotely” as it is crucial in the tropics for forecasting systems (Cravatte et al. 2016). Besides their importance for the analysis of the forcing of El Nino events, surface currents are also needed for the tropical heat budget which now relies on sparse surface drifters (Foltz et al. 2003, Hummels et al. 2013).

Combining surface currents from SKIM and sub-surface velocities from moorings or drifters would provide information on the poorly known vertical shear. Collard et al. (2008) demonstrated a monitoring of equatorial currents using the Doppler centroid from Envisat wave mode, giving the line of sight component of the velocity, as shown in Figure 3.7.

A Doppler-based satellite mission should provide equatorial current vectors every 10 days at most to allow the monitoring and analysis of these features. Even with this revisit time, the collection of high resolution (scales of 100 km and larger) snapshots of the total surface current will give access to the equatorial divergence fields, giving a first measurement of the patchy and very narrow Equatorial upwelling. This will help understand the generation of the sea surface temperature (SST) anomalies that influence the atmospheric response.

Figure 3.7: average surface velocity for July 2004, 2005 and 2006, from Envisat (Collard et al. 2008).

3.1.3.4. COASTAL AND WESTERN BOUNDARY CURRENTS

Strong coastal currents are poorly monitored today, because altimeters are less accurate in coastal regions (as a result of e.g. fewer measurements closer than 10-25 km from the coast, stronger tidal signals, strong land-sea contrasts in atmospheric corrections) and because ageostrophic effects are generally strong close to the coast. It is for example difficult to resolve exchanges between the open ocean and the shelf from existing current datasets. In several regions, such as the eastern Pacific or west coast of India, those exchanges can yield surface hypoxia by bringing in subsurface anoxic waters, with very detrimental effects for ecosystems and fisheries (e.g. Naqvi et al. 2009).

Ageostrophic currents are also a key component in frontal regions associated with western boundary currents (e.g. Gulf Stream, Brazil, Kuroshio, Agulhas). Down-front wind-stress and buoyancy fluxes (cooling, evaporation) can intensify boundary current instabilities and ageostrophic cross-frontal circulation. This has important impacts on the cross-frontal exchange of heat, freshwater and nutrients (eg Oguz et al., 2015). For example, the cross-frontal transport of cold, fresh Antarctic Surface waters into the subantarctic zone is thought to be the main factor controlling the water mass properties of mode and intermediate waters formed here (Holte et al., 2013), yet the net ageostrophic transport is difficult to estimate accurately. In the Agulhas current, a choke point of the conveyor belt that exchanges heat and freshwater between the Indian and Atlantic oceans (Beal et al. 2011), it is also difficult to recover the full magnitude of the current (Rouault et al. 2010) with altimetry and hydrography alone, in spite of drifter data used in the mean dynamic topography (Rio et al. 2014).

3.1.3.5. HYDRODYNAMIC COMPONENTS OF COASTAL HAZARDS

The response of coastal environments to natural and anthropogenic forcing factors (including climate change) depends on the characteristics of the forcing agents (i.e. sea-surface height, currents, waves). Extreme hydrodynamic events generally occur due to a combination of high astronomical tide, large storm surge and energetic nearshore waves, with a strong sensitivity on the dominant wave period and directional distribution, as schematized in Figure 3.8. This is exacerbated by sea level rise due to global change and/or local subsidence.

Figure 3.8: Schematic of hydrodynamic components that contribute to coastal flooding and erosion

The monitoring of these hydrodynamic components that are responsible for coastal erosion and flooding, as well as the monitoring of sea surface kinematics for navigation and activities at sea, is limited to a few floating buoys measuring waves, a few current meters (mostly non-permanent) and tidal gauges measuring still water level. There are about 100 wave buoys along the 100,000 km of European shorelines (one in 1000 km), most of which only report two or three parameters, and no information on the directional distribution of wave energy. In terms of data availability the situation is much worse in the developing world. In addition, regional seas and complex coastlines (e.g. nearshore European Seas and channels) are strongly influenced by micro and mesoscale processes and in-situ measurements cannot be extrapolated to adjacent nearshore areas. As a result, the understanding of storm events on shorelines and the assessment of derived risks and impacts relies heavily on numerical models of waves and storm surges (e.g. Masselink et al. 2016). Coupling of ocean and atmosphere circulation with wave models is a topic of active research and badly needs more validation data (e.g. Kerr et al. 2013, Ardhuin et al. 2017b). Numerical models have their largest errors within 200 km from the coasts (e.g. Figure 3.9), due to, among other factors, inaccurate directional wave spectra and errors in coastal winds (Roland and Ardhuin 2014).

Figure 3.9: Left: Normalized rms difference between modelled Hs and satellite altimeter measurements for the year 2009 (Rascle and Ardhuin 2013). These errors give a misleading impression of the state of knowledge of the sea state: other parameters are much more uncertain (Stopa et al. 2016b), including directional spread (bottom: at WMO buoy 62069, France, model is red symbols, data is black line) and frequency content, as illustrated by the height of waves with periods larger than 10 s (right: Hs10 at buoy EURO offshore of Rotterdam, the normalized rms error is 50%).

Focusing on coastal flooding, waves can produce not only wave run-up and overtopping (due to individual waves and infragravity waves) but also wave set-up (mean sea level rise due to wave breaking). At larger scales waves also modify wind stress and storm surges (Mastenbroek et al. 1993, Bertin et al. 2012).

Accurate measurement of the shape of the directional wave spectrum from SKIM implies the beginning of a paradigm shift for the different approaches to achieve coastal erosion problems and coastal engineering applications. For example, mean direction and spread are a key parameters to determine the extreme sea level at the shoreline. Figure 3.10 shows 3m high fluctuations in sea level due to infragravity (IG) waves: the magnitude of these IG waves varies strongly with the wave spectrum shape (e.g. Herbers et al. 1994).

Figure 3.10: Left: Example of measured sea level on a steep cliff showing large variation of the sea level at a time scale of 5 minutes (From Sheremet et al. 2014). These are infragravity waves whose amplitude is generally proportional to the square root of the significant wave height and a the period of the energetic waves. Right: Once these two factors are normalized, the run-up predicted by state of the art models still varies by a factor 2 with changes in the directional spread (half-width of directional wave spectrum, in colors) and also a factor 2 or more with the frequency width (from Guza and Feddersen 2012).

Resolving wave spectra is unprecedented down to 20 m wavelength, and effectively resolves waves in all marginal seas as shown in figure 3.11.

Figure 3.11: Average fraction of wave energy in January 2014 resolved when using a cut-off wavelength of 150, 70 and 20 m. Note that low values in the Arctic and other ice-covered regions are an artefact of the model that does not properly represent wave-ice interactions. January 2014 was very stormy in the North Atlantic.

By itself, remote sensing from a single satellite that passes every 4 days over a 5-year long mission, is not enough to resolve all storm events. Yet, this gives 500 scenes with varying hydrodynamic conditions that can be used to learn about physical processes and correct model parameterizations (e.g. Ardhuin et al. 2010, Roland and Ardhuin 2014), or perform a statistical correction and downscaling of model results (e.g. Camus et al. 2011, Hegermiller et al. 2017).

3.1.3.6. EXTREME WAVES AND WAVE-CURRENT INTERACTIONS

The statistical distribution of wave heights and the occurrence of “freak” waves, much larger than their neighbors, is a fascinating subject. As presently understood, for a given value of the significant wave height Hs, the extreme individual wave height Hmax is a function of the shape of the full frequency-directional spectrum as illustrated in Figure 3.12. Fedele et al. (2016) showed that observed freak waves could be explained by the constructive interference of elementary waves (the wave spectrum that SKIM can measure) enhanced by second-order bound nonlinearities which can be predicted theoretically but are sensitive to the spectrum shape (e.g. Janssen 2009, Fedele 2015). Earlier research emphasized the role of a nonlinear phenomenon known as “modulational instability” to explain rogue waves. That theory had been demonstrated in laboratories for waves travelling all in the same direction, but it is very sensitive to the width of the directional spectrum (Waseda et al. 2009).

Figure 3.12: Sea state and extreme waves during the “Draupner event”. The modeled directional wave spectrum (left) was used as boundary condition to a phase-resolving (HOS) non-linear wave model. The center panel shows the HOS results (squares) compared to theoretical estimates of the inverse probability distbution (Nh = 1 / P (crest height > x SWH). The right panel illustrates examples of the HOS model time series at third order (blue) and second order (red) compared to the measured time series in black (adapted from Fedele et al. 2016).

Wave focusing by currents may produce other effects in a weakly (Belibassakis 2007) or more strongly nonlinear regime (Waseda et al. 2009, Janssen and Herbers 2009). There is thus a strong need for more accurate measurements of directional wave spectra in the ocean, together with currents, especially in shallow and intermediate water depths. Indeed, extreme waves observed in the North Sea or off the west coast of Ireland are intermediate water or even shallow water depth waves, with the non-dimensional product of wavenumber and depth (kD) ranging from 1 to 2.

Hence a comprehensive and accurate global record of detailed angular and spectral wave energy distribution combined with surface currents would considerably advance our knowledge of extreme individual wave heights. Such a record is not available now. The Sentinel-1 class of satellite-based synthetic aperture radar (SAR) measurements has a global monitoring capability (thanks to the wave mode, Hasselmann et al. 2012) with sparse coverage but high resolution. Still, the SAR imaging mechanism is often strongly nonlinear, with a distorted image of the ocean wave field, of particular concern for mapping extreme events. The Sentinel-2 class of satellite-based sun-reflected optical observations will provide information at single-wave resolution, revealing wave-current interactions and local wave enhancement (Kudryavtsev et al. 2017), but these snapshots are limited to coastal zones and favorable sun and instrument geometry.

3.1.3.7. OCEAN REMOTE SENSING

Just like improving geodetic reference frames has a cross-cutting impact on all Earth Observations, a better knowledge of ocean waves will benefit all ocean remote sensing. Munk (2009) underlined the “inconvenient sea truths” given by the slope statistics derived from the POLDER mission. As he put it, the dataset “says nothing about time and space scales” and is thus very difficult to interpret and use for microwave applications for which the brightness temperature and backscatter are a function of the spectral content of ocean waves. Measuring the shortest possible components of the wave directional wave spectrum, together with slope statistics given by backscatter different azimuths and direction is necessary to further establish the quantitative modelling of ocean waves for remote sensing applications to altimetry (sea state bias), radiometry (for L-band roughness corrections), ocean color (surface reflectance).

3.1.4 Longer-term scientific goals

3.1.4.1 Wind stress vector

SKIM will provide new measurements, at nadir and off-nadir, of Ka-band backscatter and Doppler, that are uniquely related to air-sea fluxes of momentum and mechanical energy. These fluxes are primarily controlled by the wind stress vector. Several studies have highlighted that scatterometers can provide a better proxy to the wind stress than the wind speed (e.g. Weissman and Graber 1999, Portabella and Stoffelen, 2009). However, scatterometers wind direction mostly comes from the alignment with the wind of the short gravity-capillary waves that contribute to the ocean backscatter. Given the large body of evidence that the wind stress can actually be at an angle that is intermediate between the directions of the dominant and intermediate waves and that of the wind (e.g. figure 3.13), SKIM combined measurements offer unique opportunities for taking this aspect into account when estimating the wind stress vector.

Figure 3.13: Example of measured wave spectra and differences between the measured wind direction (blue) and stress direction (magenta), with large differences when the wave direction (green) is oblique (from Potter et al. 2015).

Resolving 20 m long waves, and likely shorter components, while measuring the wind direction from the azimuthal modulation of the Doppler and backscatter at 6° and 12° incidence, with wind speed estimates from the nadir beam, SKIM will enable to more precisely document this variation in direction. Moreover, theory suggests a strong parallel between surface wind stress and nadir and near-nadir backscatter measurements, because both are formed through partial integration of the wave slope distribution (e.g. Plant 1982, Kudryavtsev et al., 2014). The unique combination of SKIM measurements shall thus be a great occasion to revisit wind stress vector estimates.

To date, only aircraft Ku-band altimeter backscatter data, collected over open-ocean extensive near-surface measurements acquired by the research vessels, indicate a more highly correlation with u*, the friction velocity, than with the 10-m wind speed (Vandemark et al., 1997). In this study, the authors reported that a single parameter mapping between altimeter signals and u* shows excellent agreement for u* ranging from 0.0 to 0.4 m/s, without the need for other variables usually used in bulk formulae:current, stratification, or other ancillary data. This suggests that Ku-band altimeter backscatter data would be better used for direct wind stress derivation rather than for wind speed. After Nouguier et al. (2016) extensive comparisons between Ku- and Ka-band GPM measurements, It is expected that this conclusion easily translates to Ka-band nadir and near-nadir measurements.

3.1.4.2. MICROSEISMS AND SOLID EARTH

Since the pioneering work of Shapiro et al. (2005), the use of microseism signals generated by ocean waves has been the source of many innovations in the monitoring of faults and volcanoes. Microseisms are particularly strong at periods 2 to 10 s. Their generation by ocean waves is now well understood (Longuet-Higgins 1950, Hasselmann 1963, Ardhuin et al. 2015) and the dominant source is associated to the presence of waves travelling in opposite directions. The separation of such opposing wave trains will not be possible with CFOSAT/SWIM, but is possible in principle with SKIM using the combination of Doppler and backscatter modulations. This possibility has not been demonstrated yet and we expect difficulties associated to noise. Figure 3.14 below shows the large contrast between the wave heights, that can be measured today with altimeters, and the source of microseisms which is only available by numerical modelling.

Figure 3.14: Generation of the May 2002 seismic noise burst. (a) An extra-tropical storm (track indicated by red dots) and hurricane “Alma” produce swells with nearly opposite direction where (b) the wave induced pressure perturbation is maximum. (Reproduced from Obrebski et al. 2012).

Such a modeling of microseisms from wave models has been validated across the globe by Stutzmann et al. (2012) but it still presents large errors that probably come from the modeled wave spectra and the seismic propagation models at these periods.

An independent verification of the magnitude and spatial distribution of strong microseisms sources would allow more refined analyses. For example this would improve the time resolution of changes in seismic wave velocities estimated from microseism correlations. These velocity changes are used to monitor volcanic activities and stress relaxation in faults. A better microseism model may also help reveal fainter and yet unknown signals, possibly up to gravitational waves (Mulargia et al. 2016).

3.1.5. Beyond science: applications and societal impact

The potential of ocean wave observations for societally relevant problems is well established. SKIM particularly benefits identified GEO Societal Benefit Areas: Infrastructure and Transportation Management (shipping), Energy and Mineral Resources Management (marine renewables, oil & gas).

Surface current is one of the essential climate variable in the GCOS 2016 Implementation Plan (GCOS-200) due to its role in ocean transport and redistribution of mass, heat, freshwater, carbon and other properties. Because of their significance in advecting passive particles, knowledge of ocean currents is also important for applications such as oil spill and marine debris response, search and rescue operations, and ship routing. Currents, particularly tidal currents, can also modify storm surge impacts and sea level changes.

Climatologies of currents and sea state are needed for a wide range of design and planning, from fish farms to wind turbines or LNG terminals. Spectral wave data is particularly critical for assessing the profitability of marine renewable energy and numerical models have typically large biases in energy flux estimations due to spectral shape errors (e.g. Perignon 2017). Direct uses of ocean waves and wave climate monitoring for societal applications are also described by Le Traon et al (2015), “Knowledge of ocean waves and wave climate are needed for various applications within offshore operations/engineering, fisheries, ship routing and coastal/harbour management. Over the past five years, there have been significant advances to develop and make accessible harmonized set of altimeter and SAR wave products. These have been invaluable for numerical wave modeling and for applications (Lefèvre et al. 2006, Ardhuin et al. 2009).”

Several programs have evaluated user requirements, in particular the FP7 GMES-PURE (Partnership for User Requirements Evaluation). A first set of requirements was then derived for Marine and Atmosphere Services (http://gmes-pure.eu). Out of 530 “user requirements records” gathered, 108 point to wind, waves or currents products requirements, and they are related to four pre-defined “service areas”: “Coastal and marine environment”, “Maritime safety”, “Weather, climate and seasonal forecasting”, and “Marine resources”. This shows that “sea state” information is a key requirements for all marine-related societal needs. Building on these requirements, the Copernicus Marine Environment Monitoring Service (CMEMS) provides regular and systematic reference information on the physical state, variability and dynamics of the ocean and marine ecosystems for the global ocean and the European regional seas. Adding wave effects in operational ocean circulation models is planned for the next few years in order to improve upper-ocean mixing (e.g. Janssen 2011).

3.2. Mission duration and relation to other missions

The first criterion determining the mission duration is the time scale of the phenomena / processes of interest. Most of the scales of interest here (weekly, seasonal, inter-annual) are compatible with the lifetime of a LEO satellite mission, and SKIM will thus provide key observations. Two exceptions are longer natural oscillations such as the North Atlantic Oscillation (NAO), and the still unknown time scale of the rapid regime shift in the Arctic. In this case, no single satellite mission is long enough to give the 15 years minimum needed to study decadal variability.

SKIM will be the first Doppler-based ocean mission, and could be followed by American (WaCM, Bourassa et al. 2016, Chelton et al. 2017) or other European missions. SKIM objectives are targeted at time scales under a few years, including observing the rapidly evolving Arctic and Antarctic Marginal Ice Zones. A minimum of 5 years is likely needed to at least sample one major “climatic” event such as an El Nino event. Therefore, we believe that a minimum duration of 5 years is needed, ideally extended to 7-10 years. Over 5 years SKIM will allow an assessment of the potential of Doppler oceanography and the anticipation of technological challenges for a future generation of missions, in a long-term context.

The closest measurement existing before SKIM is provided by the Sentinel 1 (S1) radar imagery constellation. At the time of writing, Doppler processing on S1 still suffers from poorly corrected platform attitude. Yet, solutions are anticipated. This will provide means to derive Doppler and wave parameters that can yield valuable information on one component (in the line-of-sight) of the total surface current vector. SKIM does not require S1, but it will benefit from the presence of S1-C and/or S1-D for validation purpose, and to enhance sampling and production of combined products. We have thus phased the orbit of SKIM to that of S1 to either have overlaps that provide match-up datasets enhancing the value of both missions. A different shift of the orbital plane could be used after CAL-VAL to maximize the combined coverage of SKIM and the S1 constellation.

The EE9 planning allows SKIM to launch after the Surface Water Ocean Topography mission, scheduled for a 2021 launch, with some possible overlap. This would extend the monitoring of currents at scale that cannot be resolved by nadir altimetry. The SWOT main instrument, KaRIN, is a Ka-band radar interferometer, operating at incidences between 1 and 4°.

SWOT is dedicated to mesoscale-to-submesoscale geostrophic or quasi-geostrophic currents from sea surface height measurements. SWOT has a limited capacity for making wave observation, that data being dedicated to sea level corrections (Peral et al, 2015). Should SWOT and SKIM fly at the same time, direct comparisons of both observations at cross-over would greatly benefit both missions, in particular for the separation of geostrophic and ageostrophic flows (see Figure 3.1) and for cross-calibrations.

3.3. Geophysical variables and data products

3.3.1. Geophysical variables

The geophysical variables needed to develop the science and applications described in section 3.1.2, and which are derivable from SKIM observations, are summarized in Table 3.1. A high-level description of corresponding algorithms is provided in section 3.4, and additional details on these algorithms and their accuracy are provided in section 4 and the references listed in Table 3.1. The total surface velocity vector has been attributed a SRL between 4 and 5 because it was demonstrated that the Doppler centroid could provide surface currents (Chapron et al. 2005, but this was in C-band with other incidence angles). For Ka band at 6 to 12° incidence, this has been simulated numerically (this proposal) and analytically (Nouguier et al., submitted), demonstrated with airborne data (AirSWOT, Nouguier et al., submitted), and a retrieval algorithm has been simulated with an error budget (Ardhuin et al., submitted).

Variable1 . Many other variables not listed here will be derived from the spectrum E(k,θ), as weighted integrals or moments: peak and mean periods, mean direction and spread, and similar parameters for each of the wave partitions, see Annex C, table C.5. These include the equivalent pressure spectrum at long wavelengths P2(K=0,f) that comes in the secondary microseism source (Hasselmann et al. 1963, Ardhuin & Herbers 2013). ,2 . The “Doppler wave bias” (UWB) is used here to link UGD and UR abut is not a geophysical parameter per se.	description	Units	Accuracy (goal)	SRL	References
UGD	Geophysical Doppler velocity	m/s	0.1 m/s over 6 km footprint, 1024 pulses	4-5	This proposal
UR=UGD-UWB	Total surface current, radial component, 12° beams	m/s	0.15 m/s over 6 km footprint, 1024 pulses	4-5	Chapron et al. (2005) Martin et al. (2016) Ardhuin et al. (subm.)
U=(U,V)	Total surface current vector	m/s	0.1 m/s at 80 km wavelength or 40 km pixel (L3a)	4-5	Ubelmann et al. (subm.)
UE=(UE,VE)	Quasi-Eulerian current vector	m/s	0.1 m/s at 80 km wavelength	4	Chapron et al. (2005)
Ui=(Ui,Vi)	Ice drift velocity vector	m/s	0.1 m/s at 40 km wavelength (L3a)	3	Ardhuin et al. (subm.)
Us=(US,VS)	Surface Stokes drift vector	m/s	0.4 cm/s or 10%, whichever is greater for radial components	4-5	Ardhuin et al. (subm.)
E(k,θ)	Ocean wave spectrum	m3	20% for partitions with a mean square slope > 10-4. Minimum wavelengh Low,min = 20 m.	4	Jackson et al. (1985) Caudal et al. (2014)
Hs	significant wave height	m	50 cm or 8%, whichever is greater	8 for nadir 4 for other	Grelier et al. (2016)
σ0	normalized radar cross section	unitless	0.1 dB resolution of modulations at 20 m wavelength	5	Jackson et al. (1985) Hauser et al. (2017)
mssshape	mean square slope	unitless	0.001	3	Nouguier et al. (2016)
U10N	Neutral wind speed at 10 m height	m/s	2 m/s or 10%	3	to be defined, based on Nouguier et al. (2016)
τ	wind stress vector	Pa	to be defined	2	to be defined
SSH	sea surface height	m	3.54 cm RMS (1 s average)	8	see below

Table 3.1: Geophysical parameters derived from SKIM measurements. See section 4 for justification of SRL and error budget (in particular tables 4.1 and 4.5).

The SSH error budget is derived from SARAL/AltiKa. Verron et al. (2015) give a detailed view of the AltiKa SSH error decomposition. Prandi et al. (2015) demonstrated a total error of 3.4 cm RMS. SKIM inherits from AltiKa, with two notable differences. The SKIM altimeter range noise level is lower due to its high PRF and Delay-Doppler capability (on par with AltiKa or better, as per fig 4.4 in section 4.2.2.1). However, there is a larger wet troposphere error on SKIM because its payload does not include a radiometer: the additional error is approximately 1 cm RMS if the methodology of Stum et al. (2011) is used to replace the radiometer correction and has already been applied to existing missions. Our 3.54cm RMS estimate includes all errors from path delay corrections and sea state bias. This is probably conservative as we expect to produce a better sea state bias correction for SKIM thanks to co-located full sea state information that is not available on AltiKa.

3.3.2. Data products (From Level 1b to level 4)

Level-1b data products: calibrated and geo-referenced backscatter and Doppler waveforms at time of acquisition during a given day. See section 5.2 (ground segment). Each of the parameters described in Table 3.1 may form a Level-2 data product organised on the same grid as the Level-1b products. When appropriate, parameters will be accompanied by uncertainty estimates at the product resolution.

Level-3a products: accumulations of individual Level 2 products, on a single swath.

Level 3b products: gridded global maps of surface current vectors, or, for very high latitudes (75 to 82°N), wave parameters too. Due to the general rapid evolution of sea states we will not produce multi-passes wind or wave products.

Level 4 products will be based on either Level 2 or Level 3 products, when additional modelling is applied to them or additional data is combined. These level 4 products will not be described in detail here and they are only mentioned to explain the link between products and applications.

3.4. Development status of retrieval algorithms

Algorithm details are given in section 4. Here we summarize key aspects on the particularly novel parameters that define the SKIM mission. The logical connection between products is schematized in figure 14. The most novel parameter to be derived from SKIM is the total surface current U. Our baseline algorithm uses a standard pulse-pair processing (Zrnic 1977) to obtain velocities U(r) and backscatter σ0 (r) as a function of range. These are the starting points for deriving current and wave parameters. These are obtained by combining echo for pulses send and received by a single beam, forming a cycle of measurements. We will call macrocycle the sequence of cycles that use all beams, and megacycle the sequence of cycles corresponding to a full turn of the beams.

Figure 3.15: General flowchart for surface mean velocity (current and ice drift) and ocean wave parameters. Note that the 1D wave spectra provide a key contribution (in red) to the surface mean velocity. See section 5 for more detailed flowchart.

3.4.1. Total surface current or ice drift

Level 1 and Level 2a. The Doppler centroid over the footprint is our line of sight velocity ULOS, which is a level 1 product. This is transformed into a level 2a geophysical Doppler velocity radial velocity, projected on the mean sea surface by correcting for the incidence angle θi , by removing the solid Earth motion and platform attitude effects that are combined here in a non-geophysical UNG velocity,

UGD=(ULOS - UNG)/sin(θi)

The correct estimation of UNG requires a very accurate measurement of platform attitude, which is the source of recent problems on Sentinel 1. Our algorithm for UNG uses the rotation of the beams over 360°, over which any error in attitude restitution gives a very particular signature on ULOS. This technique, already used on QuikScat, is expected to yield errors under 1 cm/s (see 4.2.2.1).

In the long term we expect that UGD can be directly assimilated in a couple ocean circulation + wave model. Before such a model is ready, UGD has to be transformed into a total surface current radial component UR . This is our level 2b current product.

Level 2b. Several investigations using C-band radar at incidence angles 20 to 40° have shown that UGD combines UR with a wave-induced bias UWB (Chapron et al., 2005; Mouche et al. 2008; Martin et al. 2016, Kang et al. 2016). Previous applications on Envisat used a wind-based proxy (CDOP, Mouche et al. 2008) for UWB. The details of the Doppler and backscatter at Ka to C band and incidence angles 0 to 60° has just been reviewed by Nouguier et al. (submitted) using physical optics, and is broadly consistent with the 2-scale model of Chapron et al. (2005). This bias involves the correlation of surface slopes and velocity which happens to be proportional to the radial component US,R of the wave-induced Stokes drift in the case of linear waves in deep water. Since US,R is also a level 2b product, it is convenient to write,

UWB =G US,R

where the gain factor G is fairly stable for average wind speeds of 7 m/s (G ~ 25 for Ka band and incidence angles less than 15°), that can be retrieved from the ratio of σ0 and Doppler spectra. The two-scale model of Chapron et al (2005) gave G=- ∂σ0/∂θi /[ 2σ0 tan(θi)], and the Kirchoff approximation of Nouguier et al. (submitted, see appendix G) gives similar values (Figure 3.16).

Figure 3.16: G factor for a wave spectrum given by Elfouhaily et al. (1997), for a fully developed sea state and wind speed of 5 to 11 m/s. left: C band, appropriate for Envisat and Sentinel 1, right: Ka band for SKIM.

The validity of this relationship in the case of Ka band and 6 to 30° incidence angle is demonstrated using data acquired with the AirSWOT airborne system (section 4, figure 4.1). This analysis allows to translate experience acquired with C-band (at higher incidence angles) to SKIM.

These are confirmed by simulations of the moving sea surface discussed in section 4. These two aspects establish a SRL level of 4 for the possibility to retrieve current. In practice this requires an estimation of G and US,R. As detailed in section 4, these are obtained from the level 1 and level 2 wave products. A particular concern is that US,R is given by waves in all directions, which requires to combine other cycles, which are measured at other locations. In section 4 we present a method that gives an estimate US,R,v1 (see also Ardhuin et al. submitted). This introduces an error εWB = US,R - US,R,v1, of the order of 8 to 20 cm/s, with a magnitude that depends on the gradients of US and the distances between different footprints of cycles. This is expected to be the largest source of error in the current level 2b product. As detailed in Ardhuin et al. (2017), the gradients of US are controlled by currents, due to wave-current interactions. This is why we expect important progress by taking into account this co-variance of U and Us via assimilation in a coupled model.

Level 3a and 3b. From radial components, the vector fields U and US are produced using an optimal interpolation technique adapted from the processing of coastal HF radars (Kim et al. 2008), and using a decomposition into rotational and divergent parts of the velocity field (Yaremchuck and Sentchev 2009). The processing of the level 3b data will probably evolve with the adaptation of dynamic interpolation method introduced for altimetry data (Ubelmann et al. 2016). When multiple passes are combined in level 3b, we expect that the rapidly varying components (Us but also the part of the current that correlates with the wind) will have to be treated separately. Specific processing will have to be developed, following experience with OSCAR or Globcurrent data-based surface current products (e.g. Bonjean and Lagerloef 2002).

3.4.2. Directional wave spectrum

Level 2. Each measurement cycle gives a modulated velocity u(r) and backscatter σ0(r) as a function of range, in the azimuth θ that correspond to the look direction. As detailed in section 4 and Ardhuin et al. (subm.), the spectra Cuu(k,θ) and Cσσ(k,θ) and co-spectrum Cuσ(k,θ) of these two spatial series yields the surface elevation spectral density E(k,θ) and E(k,θ+π). On CFOSAT/SWIM, only Cσσ is available, which only gives E(k,θ)+E(k,θ+π). This separation of waves of opposite direction is called “ambiguity removal”. The ratio Cσσ / Cuu gives the G factor that comes in the wave bias for the current.

These retrievals rely on the “matching wavefront technique”. The modulation strength depends on the ratio of ocean wave wavelength (L=2π/k) and azimuthal integration distance Ly (e.g. eq. 2 in Jackson et al. 1985b). SWIM uses the Hs measured on the nadir beam to calibrate the transfer function between Cσσ(k,θ) and E(k,θ)+E(k,θ+π). SKIM will rely on tabulated transfer functions between CUU(k,θ) and E(k,θ)+E(k,θ+π). The σ0 modulation transfer function (MTF), i.e. Cσσ(k,θ)/[E(k,θ)+E(k,θ+π)] depends also on wind speed.

At present the algorithm based on Cσσ(k,θ) is well developed for the SWIM instrument on CFOSAT (Tison et al. 2014). Caudal et al. (2014) have given an extension using the measured Doppler modulation. These are now known in Ka-band from the airborne AirSWOT instrument (section 4) and platform-based measurements (Yurovski et al. 2017b). A useful product derived from the spectrum is the series of spectral partitions (e. g. Gerling 1992), as illustrated in figure 3.17.

Figure 3.17: Example of wave spectrum from NDBC buoy 41013 (33°26N, 77°44W). (a) 2D spectrum, with thick black line separating two partitions (b) 2D smoothed spectrum, and (c) 1D spectrum. (from Portilla et al. 2009)

Ambiguity removal

At present we propose to assign all the wave energy to one of the two directions θ or θ+π. This operation, following what is done on Envisat and Sentinel 1 level 2 data, is done for each spectral wave partition. We acknowledge that such a separation is sensitive to noise, and may cause errors (See examples in Collard et al. 2005, and discussion in Portilla and Cavaleri 2016). The sign of the geophysical part of the Doppler centroid and ancillary data from modeled wind vectors and wave spectra will be used to flag potential problems in separating E(k,θ) and E(k,θ+π).

Particular cases are all the conditions under which there is indeed wave energy at the same wavenumber k, in both directions θ and θ+π. These are the conditions under which microseisms are generated (e.g. Figure 3.14). They can be organized in three broad classes (Ardhuin et al. 2011):

- broad spectrum at high frequency: not a concern for S1 given the azimuthal cut-off, but possibly an issue for SKIM (although the amount of energy in opposing direction is probably under 2% for wavelengths > 10 m, with minimal consequences for estimates of UWB),

- effect of coastal reflection: usually under 2% of opposing waves, see Ardhuin and Roland (2012), with little impact for UWB,

- interaction of a remote swell with a wind sea or local swell.

That last class (as in Figure 3.14), is of particular interest for microseism sources and there is thus a strong incentive to go beyond “ambiguity removal” and to actually partition the energy into the two opposite directions. However, swells generally have a very weak Stokes drift component compared to wind seas. We thus expect a minimal impact on UWB estimates. This aspect and associated algorithms will be clarified in phase A. One possibility is to use the consistency in partition properties across and along the swath, or to use ancillary swell statistics (e.g. Portilla 2017).

3.4.3. Exploratory developments

For near vertical incidence geometry, i.e. less than 15°, and using high-frequency microwave active instrument, i.e. larger than 15 GHz (Ku and Ka-band radar), backscatter and detected motions from the sea are mostly governed by scalar (non polarized) quasi-specular reflections (e.g. Chapron et al., 1994, Mouche et al., 2008, Nouguier et al., 2016, Nouguier et al., submitted). The radar cross section is determined by the slope statistics which are related to the wind speed (Nouguier et al., 2016) and probably wind stress and stress direction. Besides the directionality of the backscatter as a function of azimuth, we can use the stronger directional dependence of the cospectrum of Doppler and backscatter.

3.5 Relevance to ESA EO strategy

3.5.1. ESA Living Planet program

SKIM is primarily an “ocean mission” but it addresses challenges highlighted in ESA’s living planet programme (ESA-SP-1329/2), beyond the ocean only. SKIM directly addresses C1 “Regional and seasonal distribution of sea-ice mass and the coupling between sea ice, climate (...)”, O2 "Mesoscale and submesoscale circulation", O4 "Physical and biogeochemical air-sea interaction processes", O5 “Sea level changes from global to coastal scales (...)”. SKIM will also contribute to the "Global Trends" strategy (improved modelling of air-sea coupling, oceanic vertical mixing and carbon uptake), and is related to the "increased exposure to disasters" (new observing capability for environmental monitoring and management). SKIM contributes to stimulating European economic growth and technological innovation, bringing unique new technological capabilities that augment existing international high resolution observing systems (e.g. Sentinel-1 & Sentinel-3, SWOT), supporting the evolution of European and international missions. More importantly, SKIM looks at the ocean in the context of the whole Earth system, hence the participation of solid Earth scientists.

3.5.2. Climate Change Initiative (CCI)

The CCI is a major programme aiming at putting together a long-term archive of high-quality Earth observations for climate research. The “Sea Ice” CCI project already includes Sea Ice Drift and SKIM can complement the coverage over the MIZ. ESA has identified the “sea state” as one of the Essential Climate Variables (ECVs) to be covered by the CCI+ program. At the time of writing the ESA call has not been issued yet, but we anticipate that this will cover waves and some wind and current data. SKIM data will thus fit into that framework.

3.6. Relevance to other programs

CLIC: The Climate and Cryosphere (CliC) project is one of the core projects of the World Climate Research Program (WCRP), and the focal point for climate science related to the cryosphere, its variability and change, and interaction with the broader climate system. CliC has identified as a central question “the present mean states, natural variability, and recent trends in sea-ice mass, seasonality of coverage and other characteristics in both hemispheres”. With a capability to measure near and in the ice, SKIM can provide critical data.

COWCLIP: the monitoring of waves and storm surges is the topic of a working group of the WMO-IOC Joint Commission on Oceanography and Marine Meteorology. Further the question of the wave climate evolution is now organized within the WCRP-sponsored COWCLIP project (Hemer et al. 2010). All major international research programs have recognized the importance of sea state variables (waves, wind, surface currents).

GCOS: Surface current and sea state and are identified as essential climate variables in the GOCS 2016 Implementation Plan (GCOS-200). GCOS-200 emphasizes that surface currents variability result in convergences/divergences, spiralling eddies, and filaments which all contribute to vertical motions. Parameterized wind stress and heat flux depend upon the speed of the near-surface wind relative to the moving ocean surface. Surface currents impact the steepness of surface waves, and are thus important for generating accurate marine sea state forecasts. The existing surface current observations include moorings and land-based HF-radars which are local, frequent, but limited in coverage. Surface geostrophic currents at global scale are derived from Lagrangian drifting buoys and from the combination of satellite altimeter and gravity data. Drifters give fast timescales (hourly observations) but with irregular spatial coverage. According to Action O12 in GCOS-200 there is a need for ocean surface current observations for climate services and marine operational systems.

Observations of sea state are required for estimates of wave and swell, and air-sea fluxes. Sea state is best known for its impacts on marine safety, marine transport, coastal erosion, and damage to coastal and offshore infrastructure. Waves also impact beach erosion, storm-related water damage, surface albedo, and transport of surface particles, including for example larvae and contaminants such as oil. Waves can also modify the growth or decay of sea ice. Sea state is typically observed locally from moored buoys while global coverage is provided by satellite altimeters and Synthetic Aperture Radar (from which a global archive of directional swell-wave spectra has been developed). Some wave information can also be inferred from coastal radar, coastal stations and specialized drifting buoys; and observations are provided from some Voluntary Observing Ships and oil platforms. GCOS-200 action O34 states a clear need for improved delivery and quality of Sea State Fields, based on satellite missions with calibration and validation from the in situ networks.

GOOS: (http://www.ioc-goos.org/). In a similar way, the “Global Ocean Observing System” (GOOS) has defined a set of “Essential Ocean Variables” (EOV): Surface Currents, Sea State, Sea Level, Ocean Surface Vector Stress are among those (Figure 3.18). As a new instrument concept, SKIM does not appear in those documents: however it would clearly fill a gap in our monitoring of air-sea characteristics, providing the global, continuous, homogeneous and timely coverage that satellite observation only can achieve.

Figure 3.18: EOV presentation (Essential Ocean Variables: a common focus for Sustained Global Ocean Observing, presentation at the 2016 Ocean Science Meeting). GODAE-OceanView: is a research program focusing on assimilation and integration of data to provide users with global information and forecasting of the ocean state. Schiller et al. (2015) describes the challenges ahead. They include coupled ocean/atmosphere models, and thus the characterization of fluxes and interfaces are key goals. This is high on the agenda of the Copernicus Marine Environment and Monitoring System (CMEMS). See also section 3.1.5 above. Prospects from future mission concept are further mentioned, “Ocean surface current mission. Extensive work has been performed in the area of ocean current mapping using radar and very encouraging results have been achieved using both Doppler anomaly and along-track interferometry derived from SAR instruments (Chapron et al. 2005).”

JCOMM: the Joint Tech. Commission for Oceanography and Marine Meteorology, is an intergovernmental body of experts that contribute to the coordination of oceanographic and marine meteorological observing, data management and services, combining the expertise, technologies and capacity building capabilities of the meteorological and oceanographic communities. The creation of this commission results from a general recognition that worldwide improvements in coordination and efficiency may be achieved by combining the expertise and technological capabilities of World Meteorological Organization (WMO) and UNESCO's Intergovernmental Oceanographic Commission (IOC). Worldwide marine meteorological and oceanographic communities are working in partnership under the umbrella of JCOMM in order to respond to interdisciplinary requirements for met/ocean observations, data management and service products. These cover: maritime safety services; waves and coastal hazards; sea ice; and ocean forecasting.

The Changing Arctic Ocean. The UK NERC five-year programme “The Changing Arctic Ocean: Implications for marine biology & biogeochemistry” runs over the period 2017-2022. The overarching goal of the programme is to understand how change in the physical environment (ice and ocean) will affect the large-scale ecosystem structure and biogeochemical functioning of the Arctic Ocean, to project potential major impacts for future ecosystem services. With high-resolution detection of the ocean currents and sea ice drift, SKIM will contribute to the attribution of the key physical mechanisms impacting the Arctic ecosystems.

3.7. Compliance with the ESA EE9 selection criteria

EE9 selection criterion	How SKIM answers to this criterion
1. Relevance to the ESA research objectives for Earth Observation– [...] reference must be made to the general and specific objectives and scientific challenges set forth in the document Earth Observation Science Strategy for ESA. How scientific advances anticipated from the mission contribute to addressing major societal issues?	SKIM addresses challenges O2 "Mesoscale and submesoscale circulation", O4 "Physical and biogeochemical air-sea interaction processes", O5 “Sea level changes from global to coastal scales (...)”, and C1 “Regional and seasonal distribution of sea-ice mass and the coupling between sea ice, climate (...)”.
2. Need, usefulness and excellence– this must take account not only of scientific requirements and/or the importance of a mission viewed as a precursor but also the extent to which the requirements, including those of space/time sampling, can be met by the proposed mission.	- Equatorial currents and upwelling, key components of the coupled ocean-atmosphere system, can be uniquely monitored by SKIM. - The Marginal Ice Zone is a “terra incognita” for remote sensing, except for ice concentration. SKIM will add waves and current/ice drift data in this critical region.
3. Uniqueness and complementarity – this must take account of other (i.e. not space) means of addressing the mission requirements as well as the activities and plans of other national and international bodies for space missions.	SKIM will be a pathfinder for Doppler oceanography, revealing important processes that could not be observed before (e.g. equatorial divergence, ice jets). It can be a precursor to the wider swath WaCM mission proposed in the USA (Bourassa et al. 2016).
4. Degree of innovation and contribution to the advancement of European Earth Observation capabilities – this relates to technical/industrial aspects as well as to user interests.	SKIM will use the original conical scanning solution of the SWIM instrument with opportunities for upgrades. SKIM will also use novel European FPGA technology (e.g. NanoXplore)
5. Feasibility and level of maturity – this encompasses technical and scientific readiness, as well as the status of the associated user community within ESA member states and the maturity of its requirements.	Hardware of the space components only require minor modifications from the SWIM instrument, and TRLs of all components are at least 5.
6. Timeliness – this must take account not only of the timeliness of a mission from the point of view of user needs but also with regard to implementation constraints.	The rapid evolution of the Arctic marginal ice zone, is the key driver for the mission timing.
7. Programmatics – in addition to the considerations of dev. schedule, cost, risk, etc., this addresses the implications of possible cooperation with other bodies, including synergies with other national and international developments, and taking account of the planned availability of relevant data from other obs. systems.	The possible combination with SWOT and Sentinel 1 would be a plus, but is not required.

Mission assumption and technical requirements

4.1. Introduction

The principles of ocean surface measurements with a rotating real aperture radar (RAR) were laid out by Jackson (1981, 1985a), with a focus on measuring directional ocean wave spectra. Such a capability was implemented and demonstrated on several airborne experiments (Jackson et al., 1985b; Hauser et al., 1992; Hauser et al., 2003), and compared to synthetic aperture radar (SAR) airborne and satellite measurements (Vandemark et al., 1994). The first “wave spectrometer” in orbit will be SWIM, on the CFOSAT mission, due for launch in August 2018 (Hauser et al., 2017).

Caudal et al. (2014) have further demonstrated the benefits of using complex radar signals, with an airborne demonstration. With the preparation of the present proposal, Nouguier et al. (submitted) have exposed the measurement principles of both mean and modulation properties of the joint intensity and Doppler signals expected for SKIM, as illustrated in Figure 4.1. This same paper contains the demonstration of these principles with airborne Ka-Band data at incidence angles 5 to 20°. This demonstration uses the AirSWOT radar system (Moller et al. 2011). Further insight is provided by the platform-based experiments of Yurovsky et al. (2017a,b) with a Ka-band Doppler radar.

Figure 4.1: Illustration of (a) backscatter (b) Doppler modulations in Single Look Complex SAR images in Ka band recorded on AirSWOT flight #16201, 7 February 2017 over the Gulf of Mexico. (c) Shows the theoretical expected Doppler centroid and normalized factor G, according to Nouguier et al. (submitted) and (d) shows the Doppler modulation in a SKIM-like unfocused SAR geometry (after 400 m integration in azimuth).

SKIM shares many common aspects with other radar systems, including non-rotating ones. In particular, the mean residual Doppler in RAR mode is identical to the classical Doppler centroid used by SAR systems. A geophysical interpretation of Doppler estimates can start from the very large analysis experience from Envisat and Sentinel 1 Doppler centroid frequencies (fDC ) at incidence angles 20 to 50°. We show below that these are easily translated to Ka-band at 6 and 12° incidence.

We thus present here a generalization to Doppler data of the RAR processing and algorithms from level 0 to level 3 products. Data will be processed to produce multiple looks within a footprint, to efficiently reduce uncorrelated speckle contribution and help remove the ambiguity in wave propagation direction (i.e. Eq. 9 and Fig. 18 in Caudal et al., 2014 for Ku-band airborne measurements), following a procedure done in SAR imagery (e.g. Engen and Johnsen 1995).

More important, the Doppler centroid analysis will more directly benefit from the joint intensity and Doppler measurements, to estimate and interpret the line of sight velocity more precisely. Dominated by quasi-specular reflections, that velocity contains a so-called amplified Stokes drift vector (Chapron et al., 2005), related to the “mean slope velocity”, which is signed with the short wave propagation direction (close to the wind direction). Because the full directional slope-elevation spectrum (although on a larger scale) is simultaneously retrieved, we will be able to more consistently correct this residual wave-motion velocity to obtain a total surface current. This is further made robust by the use of different incidence and azimuthal views.

Besides this “baseline” algorithm, complementary “Delta-K” measurements will also be used, not only for the backscatter power as in Alpers and Hasselmann (1978), but for its time evolution. Namely, ocean echoes obtained at two different radar wavelengths will be filtered around a resulting prescribed “beat-wave” frequency, to more precisely infer its time evolution and related phase speed. This processing will benefit from the expected large sensitivity to detect surface waves using Ka-band low-incidence angle measurements. In principle, the analysis becomes similar to a coastal radar (e.g. Barrick 1972), comparing the measured phase speed from the expected linear phase speed without currents, with proper averaging over the entire footprint to reduce the uncertainty on these individual phase speed estimates.

4.2. Observation techniques

4.2.1. Measurement sequence: definition of beams, modes, cycles...

Before we proceed we must introduce the notions of cycle, macro-cycle, and mega-cycle.

- a cycle is the succession of pulses on the same beam: 1024 pulses in open mode and 145 pulses in closed mode. In both cases the pulse repetition frequency (PRF) is constant at 32 KHz.

- a macro-cycle is the succession of cycles on all consecutive beams (1 to 8), with a duration of 293 ms in open mode and 76 ms in closed mode.

- a mega-cycle is the succession of Na macro-cycles, where Na is the number of azimuths scanned by the rotating beams. The pattern of measurement is periodic and repeats itself at each mega-cycle.

We have performed our evaluation with two modes

- an “open burst” mode with a succession of 1024 pulses over a 36.6 ms cycle. To minimize de-correlation over the footprint, we have chosen Na=60, giving a rotation speed of 3.14 rpm (one turn in 17.5 s).

- a “closed burst” mode with a succession of 145 pulses over a 9.5 ms cycle, Na=150, a beam rotation speed of 5.26 rpm (one turn in 11.5 s). The different chronograms are shown in Figure 4.2.

Figure 4.2: Transmission and reception chronograms for the open (left) and closed (right) burst modes. Note that the cycle duration (36.6 and 9.5 ms) is larger than the number of pulses divided by the PRF, to allow for the travel time of pulses and switching from Tx to Rx. This duration is an average and changes with the beam incidence.

The choice of number of beams, incidence angles, and beam rotation speed is optimized to fill the measurement swath and improve on the effective resolution of level 3 products. The 8 beams are thus positioned as shown on Figure 4.3.

The choice of mode can also be used to better fill the swath (in the case of shorter cycles in closed mode) at the price of a higher noise level for each cycle, a higher data rate, but a lower power consumption associated to the Tx/Rx switch. The trade-off between spatial resolution and accuracy is summarized by the effective resolutions (see section on level 3.a below).

We have particularly evaluated 3 instrument configurations:

- SKIM1: 6-beam set-up used in the 2016 SKIM proposal

- SKIM2: 8-beam set-up with “open burst mode”

- SKIM3: 8-beam set-up with “closed burst mode”

We have found that SKIM2 generally performs better than SKIM1. SKIM3 is marginally worse than SKIM2 in terms of formal error (see Figure 4.25), but may have benefits for resolving narrow long-shore currents such as the Agulhas. The present SKIM design allows for the use of both SKIM2 and SKIM3 with a possible change once in flight.

Figure 4.3: Pattern of beams (1 is nadir, 4 and 7 are at 6°, the others at 12° incidence) and patterns of footprints on the ocean with, center: SKIM2 (open bursts), right: SKIM3 (closed bursts). Each color is a different beam. The background color show simulated currents velocities off the Oregon coast, courtesy of Y. Chao, previously used by Fu and Ubelmann (2014) for the evaluation of the SWOT mission performance. The SKIM swath width is 280 km.

4.2.2. Doppler centroid and surface velocity vector

In this section we consider 4 sources of error in the estimation of the velocity field:

- errDC the instrument error on the Doppler centroid estimation, of the order of 6 to 12 cm/s (rms) over a single cycle (for 1024 pulses, 2.6 times as much for 146 pulses) depending on azimuth direction. This is for a single beam.

- errPA, the error on the platform attitude restitution, less than 1 cm/s

- errWB the error on the wave bias correction. This contains a spatial interpolation error, because data from neighboring cycles have to be combined. This is of the order of 5 to 10 cm/s rms (away from coastlines), depending on sea state and current conditions. Additional errors due to uncertainties in the wave spectrum and G factor have yet to be investigated in detail, and we have thus assumed that they give at most a comparable error of 5 to 10 cm/s rms.

- errMAP the mapping error in going from radial velocity components to gridded fields: this only applies to Level 3 products.

The first three errors are for a full cycle, and they have been evaluated based on detailed simulations in a wide range of ocean conditions. The results are summarized in Table 4.1. The last error depends on the structure of the current and chosen resolution. It is generally smaller for L3a (snapshot given by a single swath) than for L3b (which includes time evolution between swaths). For L3a, our algorithms give a resolved wavelength Lr ~ 90 km, or a resolution dx = Lr/2 ~ 45 km (see Table 4.5), with a corresponding accuracy of the order of 0.1 m/s.

Region:	Equator	Fram (open water)	Fram (ice)	Gulf Stream	Oregon coast
Total L2 error (12° beams) NB: mission goal is 0.15 m/s	0.08 m/s	0.10 m/s	< 0.20 m/s	0.16 m/s	0.16 m/s
errDC (rms)	< 0.1 m/s	< 0.1 m/s	< 0.1 m/s	< 0.1 m/s	< 0.1 m/s
errPA (rms)	< 0.01 m/s	< 0.01 m/s	< 0.01 m/s	< 0.01 m/s	< 0.01 m/s
errWB (rms) interpolation only	0.02 m/s	0.05 m/s	TBD	0.09 m/s	0.08 m/s
errWB (rms) total	0.05 m/s	0.08 m/s	TBD	0.15 m/s	0.13 m/s

Table 4.1: Summary of expected errors for L2 velocity product based on the preliminary algorithms proposed in this proposal in the case of the SKIM2 configuration (open burst, 8 beams).

Region:	Equator	Fram (open water)	Gulf Stream	Oregon coast
Total L2 error (6° / 12°) in m/s	0.36 / 0.17	0.36 / 0.18	0.37 / 0.20	0.42 / 0.22
errDC (rms)	0.35 / 0.16	0.37 / 0.16	0.35 / 0.16	0.37 / 0.16
errPA (rms)	< 0.01 m/s	< 0.01 m/s	< 0.01 m/s	< 0.01 m/s
errWB (rms) interpolation only	0.02 / 0.02	0.05 m/s	0.06 / 0.06	0.07 / 0.07
errWB (rms) total	0.04 / 0.04	0.08 m/s	0.13 / 0.13	0.14 / 0.14

Table 4.2: Summary of expected errors for L2 velocity product based on the preliminary algorithms proposed in this proposal in the case of the SKIM3 configuration (closed burst, 8 beams).

As detailed below, the largest level 2 errors (before mapping), are errWB and errDC and there is a trade-off in the choice of mode (closed short burst or open longer burst), between the Doppler error errDC and the mapping error errMAP. The overall system performance is thus estimated on level 3a products with the effective Leff resolution that we define as the wavelength above which the coherence between modeled velocity fields and true velocity fields is above 0.5. The values of Leff vary with the observed conditions (current intensity, wave height …) and the instrument settings.

Although the SKIM3 configuration has much more noise due to the shorter pulses, this is largely compensated by the larger number of cycles.

Besides the root mean square values of the errors, their correlation scales can have important impact in level 3 errors. We have taken errDC to be uncorrelated from cycle to cycle, and found that the correlation distance for the interpolation part of errWB is of the order of 25 km (e.g. figure 4.16).

These error levels are explained and justified in detail in the following subsections.

Illustration of the L2 error is provided using interpolated surface drifters velocity field co-located with AirSWOT flight # 172803 on 2016/02/07 over a “hammer-head” shaped eddy in the Gulf of Mexico during the LASER experiment (Figure 4.4). 108Hz corresponds to the expected Doppler shift for the given mean wave field. A rms difference equivalent to 0.13m/s surface velocity is observed for the current in the range direction.

Figure 4.4: Left : interpolated surface drifters velocity field co-located with AirSWOT flight # 172803 on 2016/02/07. Colors indicates the in situ surface current velocity under the flight swath. Arrows indicates the surface current direction over the interpolated drifters area. Right : transect of surface drifters derived from Ka-Band Doppler shift and related measured AirSWOT Doppler shift around 12 degrees incidence, showing the expected sensitivity to currents of the Doppler centroid for SKIM-type incidence. (From Nouguier et al., submitted)

4.2.2.1. INSTRUMENT PERFORMANCES

Table 4.3 gives single shot signal to noise ratio. The 68% percentile of the Vandemark et al. (2004) model is used for the values of NRCS over oceans. More details are given in section 5.2.2.6.

Beams	Nadir	6°	12°
Sigma naught (dB)	11	9.9	6.6
SNR RAR (dB)	33	14	7.6
SNR SAR (dB)	43	17	10

Table 4.3: Single shot Signal to Noise Ratio in the Line of Sight.

The high nadir beam SNR will provides excellent altimetric performances (Figure 4.5) similar to the AltiKa altimeter. Thanks to delay-Doppler capability and the high PRF used, SKIM also benefits from the very thin strip-shaped footprint and it is not affected by Doppler aliasing.

Figure 4.5: Theoretical Altimeter range noise (MLE3).3 cycles per second, burst of 32 pulses and 32 bursts per cycle are considered for SKIM (96 looks).

4.2.2.2. LEVEL 1: PULSE PAIR AND ATTITUDE CORRECTIONS

The surface velocity measurement uses the line-of-sight Doppler component ULOS estimated from the Centroïd of the Doppler spectrum. This velocity is dominated by the relative velocity UNG of the antenna footprint on the ocean and the instrument on the satellite platform. A very accurate restitution of that component is needed to access the geophysical Doppler velocity. For 12° incidence, an error of 4 Hz on fDC translates to about 10 cm/s for horizontal surface velocity. The SKIM concept takes advantage of the rotation of the antenna beam with a very clear pattern that allows to correct for errors due to platform attitude. This aspect is detailed below.

Pulse pair processing is a well-known technique (Zrnic, 1977) used in meteorological radar and implemented in the Cloud Profiling Radar of EarthCare mission. This method can be also used for ocean current measurement (Rodriguez et al., 2014). Amplitude and phase measurement of the mean radar signal are obtained by forming the cross-correlation product of two successive echoes (eq. PP.1). The measured phase is proportional to the velocity of the illuminated scatterers (eq. PP.2). Main concern is to ensure a good correlation between the two echoes whose coherence depends on the Doppler spread within the illuminated area (RAR mode). That is why the 32 kHz PRF is selected. The Doppler bandwidth is around 15 kHz (maximum at 90° azimuth angle). Therefore, very accurate Doppler measurements can be provided.

The measured amplitude is proportional to the mean signal power weighted by the correlation coefficient. These measurements also give wave spectra (see §4.2.3). After range compression, the cross-correlation product of two successive echoes are summed coherently over the full cycle (1023 pairs for the open mode) and for each range gate.

The telemetry provides both amplitude and phase of the mean signal at the finest range resolution with an efficient noise reduction.

<S(t) S*(t+PRI)> = ρ PS eiΦ ,

|<S(t) S*(t+PRI)>| = ρ PS,

Φ = arg[<S(t) S*(t+PRI)>].	(PP.1)

Noise of fLOS Doppler estimates

The line of sight velocity measured by the radar is given by the phase of the pulse pair,

ULOS = λ Φ / (4 π PRI). (PP.2)

The maximum amplitude of the Doppler velocity that can be measured, corresponding to a phase of the complex signal between +/-π, is +/- 67m/s.

The accuracy of this radial velocity (instrument random noise) is generally expressed as follows (Doviak and Zrnic 1993),

σv=λ PRF (1-ρ2)0.5/[(4 π) (2N)0.5 ρ],	(PP.3)

where N is the number of independent pulse pairs. ρ = ρth ρgeo ρtemp is the correlation between pulses in a pair. The factor λ PRF /(4π) is higher than 20 m/s so that a massive averaging is required to obtain a good accuracy for ocean currents. However, a PRF around 30 kHz is optimal considering that a very good correlation between pulses is required (factor√(1-ρ)2/ρ close to 1 for SKIM off-Nadir beams).

Pulse to pulse correlation contributions (Meneghini and Kozu 1990) include ρth that is the decorrelation due to the thermal noise. For SNR higher than 10 dB a very good correlation is obtained (>0.9). The term ρgeo corresponds to the geometric correlation due to different observation angle between two pulses (Doppler spread). The impact of the antenna rotation is negligible in our case. The selection of a high PRF for SKIM enables to ensure a good correlation around 0.7 in the worst azimuth direction (side looking view, azimuth 90°). Finally ρtemp is related to the temporal decorrelation of the surface (ms order) during two successive pulses observation. In our case, this term is negligible (30 µs between two observations).

Figure 4.6 shows the predicted radial velocity accuracy over the SKIM beam footprint as a function of the antenna azimuth (0 is along-track, 90° is cross-track) .

Figure 4.6: Instrument rms noise errDC on radial velocity measurement (left: linear scale, right: log scale). Accuracy of a few cm/s is achievable with N=1024 pulses (open burst, solid) at 12° incidence.

These theoretical results have been tested with the SCALIAN Seamotion-Radar simulator (Pinel et al. 2014, see also Annex B.2.2), and are illustrated in figure 4.7. This numerical model (described in Annex B.1.1) provides synthetic I/Q signals, over which the pulse-pair analysis previously described can be performed.

The RMS errors of the estimated average horizontal Doppler velocity were derived considering a noise-free instrument. The remaining velocity fluctuations result from various contributions:

- Width of the Doppler distribution in a resolution cell

- Finite duration of the analysis window (32 ms)

- Motion of the spot on the surface (orbital speed + antenna rotation) during the analysis window

- Correlation between the range samples

- Surface topography and kinematics

The corresponding effects interact in a non-trivial way, as shown in Figure 4.6 where the velocity measurement RMS error is plotted as a function of the azimuth angle of the antenna, for 5 simulation configurations with increasing realism:

- Configuration 1: flat surface, non-rotating antenna

- Configuration 2: flat surface, rotating antenna

- Configuration 3: “frozen” surface, rotating antenna

- Configuration 4: moving surface, rotating antenna

Figure 4.7: RMS error on the estimated horizontal Doppler velocity as a function of the antenna azimuth angle (0°=along-track), for 4 simulation configurations.

In Configuration 1, the error estimated through numerical simulation is close to that obtained from the previously introduced theoretical formulas, except in the vicinity of the along-track direction, where it exhibits a narrow peak. The curve shape (green circles) essentially results from the azimuthal variation of the Doppler spectrum width in a resolution cell, increasing from a minimum in the along-track direction to a maximum in the across-track direction. A counteracting effect is yet limiting the amplitude of this variation: in the along-track direction, the spot translation leads to significant temporal overlapping between the range samples and subsequent reduction of Neff. In the across-track direction, to the contrary, very small temporal overlap occurs, the platform motion being merely parallel to the range gates, thus preserving a much larger Neff . An optimum tradeoff between those effects occurs at about 6° in azimuth.

Configuration 2 differs from the previous by the rotation of the antenna. At the considered rotation rate of 3 trs/min, it has only a minor effect on the measurement precision.

In Configuration 3, a “frozen” sea surface is considered (topography of a fully developed wind sea with U10=7 m/s, without time evolution). The error shows a gradual increase from the along- to the across-track direction. We can propose the following explanation: due to the surface topography, the reflectivity exhibits an inhomogeneous azimuthal distribution in a resolution cell, leading to a shift of the Doppler centroid. The amplitude of this shift, scaled by the azimuthal variation of the radial velocity, has a maximum in the across-track direction. It directly contributes to the fluctuations of the estimated Doppler velocity,, thus increasing the RMS of the estimate..

Finally, Configuration 4 accounts for the motion of the sea surface. Somewhat surprisingly, it reduces the error, at all azimuth angles. It seems that the slight evolution of the surface during the analysis window decreases the correlation in range and thus increases Neff . The present error estimations were obtained for a unique sea state (fully developed wind sea, with U10=7 m/s. The wind is blowing against the instrument look direction). While the study should be extended to a variety of environmental conditions in the future, it readily appears that the sea state has little influence on the Doppler velocity error and our numerical simulations validate the theoretical results of figure 4.6.

The narrow uncertainty peak in the along-track direction, which is not reproduced by the theoretical formula, seems to arise from the longer time correlation, which compares to the 32 ms averaging cycle. Due to the overlap between the areas swept by neighbouring range gates during one cycle, this leads to increased correlation in range. Although the Doppler spread and the correlation between pulses in a pair are optimal, the resulting redundancy in the measurements sampled along the swath slightly increases the standard deviation of their average. The best precision is then obtained at an azimuth angle of a few degrees, corresponding to a tradeoff between the involved effects (i.e. Doppler spread, pulse-to-pulse correlation and Doppler measurements correlation in range). More simulations are clearly required to better describe the error curve shape in the vicinity of along-track direction.

Attitude restitution and estimation of UNG

As explained previously, the Doppler frequency in the line-of-sight is equal to

fLOS = fNG + sin(θi)fGD

The frequency fNG contains non-geophysical contributions and shall be estimated precisely. All geophysical quantities are projected on the Earth surface, hence the sin(θi) factor.

This frequency depends on platform and Earth parameters (attitude, horizontal and vertical velocities, see Hansen et al., 2011)

with:

- λ the wavelength of the radar signal,	Vsat the spacecraft velocity

- γ the elevation angle,	α the azimuth angle,

- ε equal 1 if in [0,π], -1 if in [π, 2π],

- ωe the angular rate of the Earth,	ω the angular rate of the spacecraft,

- Ψ the orbit inclination,	β the angular position on the orbit,

- e the eccentricity of the orbit,	p the argument of the perigee.

Nadir beam	Offset beam
Errors	Knowledge	Errors	Knowledge
Elevation error at SKIM level in flight	Fixed bias	0.04	0.04	0.04	0.04
Variable bias	0.01	0.00	0.06	0.00
RSS on orbit	0.02	0.02	0.03	0.03
Total	0.05	0.05	0.05	0.05
Elevation error at platform level	Fixes bias	0.06	0.03	0.06	0.03
RSS on orbit	0.16	0.03	0.16	0.03
Total	0.17	0.05	0.17	0.05
Elevation error at orbit level	Fixes bias	0.00	0.00	0.00	0.00
RSS on orbit	0.00	0.00	0.00	0.00
Total	0.00	0.00	0.00	0.00
Elevation error – in flight	Fixed bias	0,07	0,05	0,07	0,05
Variable bias (rotation)	0,01	0,00	0,06	0,00
RSS on orbit	0,16	0,04	0,16	0,05
Total	0,17	0,06	0,18	0,07

Table 4.4: Pointing budget in elevation for nadir and offset beams, in flight, at 3 sigma.

The pointing budget of SKIM (based on SWIM heritage) is estimated in the Tables 4.4 and 4.5. The pointing knowledge is good for wave retrieval but it is far from sufficient to estimate fNG. Even with mis-knowledges of the order of 0.01° in elevation and 0.01° in azimuth, the radial velocity error would not be acceptable (several m/s). In consequence, we propose a signal processing solution (on-ground), using the full rotation of the SKIM beams. The frequency signal over a complete rotation (or several) is used to estimate the Doppler centroid by matching with the theoretical function. The matching provides first the azimuth and elevation pointings; these angles are then extrapolated to Doppler frequency.

Fixed antenna	Rotating antenna
6°	12°	6°	12°
Azimuth error at SKaR level	RSS	0.19	0.14	0.21	0.16
total	0.29	0.24	0.36	0.31
Azimuth error at platform level	RSS	0.04	0.04	0.04	0.04
total	0.07	0.07	0.07	0.07
Azimuth error at system level	RSS	0.19	0.14	0.21	0.17
total	0.31	0.27	0.39	0.35

Table 4.5: Pointing budget in azimuth, at system level, at 3 sigma.

The algorithm is based on the matching of the measure Doppler phase with the theoretical Doppler function fNG over one rotation. The maximum likelihood estimator is used through a Nelder-Mead minimization. It enables to retrieve the elevation and the azimuth angles. They are then injected in the theoretical Doppler function to get fNG for each measurement. The following assumptions are made,
- the antenna rotation speed is well known and stable (consistent with SWIM PFM),

- the satellite attitude is constant at the scale of few seconds (a specific study – not shown here – gives a requirement of stability better than 10-4 degree/s),

- the azimuth sampling uses a macrocycle [0°, 6°, 12°, 12°, 6°, 12°, 12°, 12°] (only the five 12° beams are used and recomposed over one rotation),

- the geophysical current is weak in average over one rotation (this is tested below)
The algorithm has been tested on a simulated data with a Monte-Carlo approach (200 iterations). Noises are added on the simulated Doppler frequency. The noise sources are:

- the estimation error due to signal coherency: the estimation noise is an unbiased Gaussian noise with a standard deviation given by (PP.1) where ρ the decorrelation due to the thermal noise (SNR) and the geometry, given by

ρ≈1/(1+1/SNR)×0.52(Vsat sinα)/Dant_az

with Dant_az the antenna length in azimuth, and SNR = 7.6 dB. In our case, N is high because the averaging is performed in range over the entire swath and in time over Nimp pulses (Nimp=1024 for a single cycle in open mode, this does not change much in closed mode given the larger number of cycles with different azimuths).

- the residual wave contribution at the footprint scale (~1m/s at the maximum)
Modeled by a random uniform noise in [-1, 1] m/s or by a WAVEWATCH III maps from some typical cases: Oregon and Equator at 23W.

The algorithm is the following:

- Generation of a noisy Doppler frequency for a given pointing (γ+Δγ, α+Δα) over a complete rotation (Δγ and Δα being the misknowledge pointing in azimuth and elevation).
- Nelder-Mead optimization on (γ, α) between the averaged observed frequencies for a restituted complete rotation and the theoretical Doppler Centroid function.
The algorithm test several (Δγ,Δα) couples with Δγ within 0° and 2° and with Δα within 0° and 5° to check the impact of the pointing budget.

Figure 4.8 show the mean error and the standard deviation obtained on each Monte-Carlo simulation. There is no impact of the initial knowledge errors on the pointing angles. The convergence of the algorithm is reached easily with very accurate results. The angles are restituted with no bias and very low standard deviation values. The estimation errors on the azimuth and elevation angles are very good. The pointing budget is sufficient to get the required accuracy on the final estimation. We have verified (not shown here) that a platform stability of 10-4 deg/s is enough.

Figure 4.8: Left: Error estimation of the azimuth pointing with respect to azimuth and elevation knowledge errors. Center: Error estimation of the elevation pointing with respect to azimuth and elevation knowledge errors. Right: Maximum velocity error over one rotation with respect to azimuth and elevation knowledge errors.
The impact of the geophysical current was quantified on two examples with currents dominated by small scale (Oregon) or large scale (equator at 23 W) variability. The simulations used one month of simulated waves and currents. At each one hour time step, a Monte-Carlo of 200 iterations is performed. The final platform attitude error errPA was always under 2.5 cm/s.

Figure 4.9: Snapshots of modelled fields (mean square slopes, wind speed, Stokes Drift and current) in storm conditions off the Oregon coast.

Figure 4.10: Maximum velocity error over one rotation with respect to complete 2D rotations of the antenna (all WW3 run times of month 2014/09 are considered).

A final issue for the estimation of fNG is the variation of antenna pointing during the rotation. These variations are due to the antenna geometry and the thermo-elastic effect. To keep the estimation accuracy, the antenna variations have to be modeled and injected in the theoretical function. First analysis states for a sinus shape model added to a random noise. It will be consolidated in phases A and B to obtain the final retrieval algorithm.

4.2.2.3. Level 2a: geophysical velocity

The line-of-sight velocity also contains a geophysical Doppler velocity UGD, that is interpreted as	a horizontal motion,

UGD= (ULOS - UNG ) / sin(θi)

UGD is our level 2a surface velocity product which could be usefully assimilated in coupled models with waves and currents. Because the relevant assimilation methods are still to be developed and because many users will want a useful product without using a model, that velocity must then be processed to provide a level 2b “Total Surface Current”, that we define as a the Lagrangian Mean radial velocity UR at the sea surface, where the mean is over the phase of surface waves.

4.2.2.4. LEVEL 2b: TOTAL RADIAL SURFACE VELOCITY

The velocity UR is directly comparable to radial components measured by HF radars. To obtain it, an important wave-induced bias UWB must be removed (Chapron et al. 2005). This wave-induced bias UWB is well understood (Figure 4.11): it is caused by the co-variation of line-of-sight (nearly vertical) velocity w, and radar cross-section 𝜎0 at the scale of ocean waves (Thompson et al. 1991, Chapron et al. 2005). UWB can be expressed as a function of the “mean slope velocity” introduced by Nouguier et al. (submitted) with a value

UWB ≃ < w 𝜎0> / <𝜎0 > ≃ G U∞S,R , (WB1)

In this expression G is a gain factor and U∞S,R is the deep water surface Stokes drift velocity for the same wave spectrum. When a current UR is present we get

UR = rUGD – UWB. (WB2)

The factor r ≃1 in (WB2) appears in the Kirchoff approximation (Nouguier et al. submitted) and is due to the fact that only “detected” surface elements contribute to the Doppler signal.

Figure 4.11: Two-scale schematic of the Doppler bias induced by the correlation of surface slopes with velocities towards the radar (from Chapron et al. 2005).

The most common wind speeds U10=7 m/s gives G=25 at the incidence θi = 12°, while U∞S,R is of the order of 1.3% of U10 in the wind direction, and zero in cross-wind (Figure 3.16 above, Nouguier et al. submitted). Hence UWB is of the order of 33% of the wind speed. This magnitude of G was verified by Yurovski et al. (2017b) using a Ka-band Doppler radar mounted on a research platform. His results are illustrated in figure 4.12).

Figure 4.12: Variation of Doppler-derived velocity with the wave spectrum third moment m3 ≃ US/ (1.5 (2π)3 g). Figure courtesy of Y. Yurovski, based on data acquired at the Katsiveli platform in the Black Sea (Yurovski et al. 2017a,b). Note that the estimation of G is biased low by 20% due to a normalization by the non-directional spectrum.

It is important to note that the radial component U∞S,R is the projection in the local azimuth of the vector

,	(WB3)

where E(k,θ) is the directional wave energy spectrum. The integral over wavenumbers can be performed over each measurement cycle (defined below) giving a practical estimation

(WB4)

In eq. (WB4) the contributions from neighboring directions θ' are

,	(WB5)

where F(kmax,θ) is the relative contribution of unresolved spectral components (see e.g. Ardhuin et al. 2009). With a cut-off at 20 m wavelength, corresponding to a wave frequency of 0.3 Hz, we have F(kmax,θ) ≃ 1, with little variability. That contribution is expected to be highly correlated to the contribution at kmax, and of the order of 50% of the total, as shown on Figure 4.13. This is why it is necessary to resolve waves with 20 m wavelength.

Figure 4.13: Global average of the spectra fnE(f) with n=3, 0, 2, and 4, based on a WAVEWATCH III January 2014 hindcast (Rascle & Ardhuin 2013).

Given that the wave spectrum varies both along the sea surface and with directions, we have to interpolate either in spectral space (from θ to θ') or in physical space (x and y). Simulations indicate that the physical space variations are less severe than the spectral directions, and we thus use measurements of other cycles (different locations) but at the same direction θ'. The sum in eq. (WB3) is thus a sum over other cycles, and we use those that are nearest to θ and closest in space. In practice they can belong to several mega-cycles around the local cycle.

The variations of US,1D(θ’) over the distance r(θ’), which is typically less than 50 km, is mostly due to the effect of currents on waves (Ardhuin et al. 2017). We have performed realistic high-resolution simulations in a wide range of conditions (Oregon shelf, Gulf Stream, equatorial currents, Fram strait, Agulhas current3 All the model outputs are available at ftp://ftp.ifremer.fr/ifremer/ww3/HINDCAST/OTHER/SKIM/ . These simulations are discussed in more detail in Annex B.2, see also Ardhuin et al. (submitted). ).

Assuming a constant G, we have repeated many simulations (such as the example in Figure 4.14), and found that the error errWB on the estimation of UWB , has a negligible bias and a standard deviation that is of the order of,

std(errWB) ≃ G x ε x std(US) x ( r2 / 20 km ) (WB5)

where G is the factor shown in figure 16. ε is a non-dimensional factor which, in our simulations, ranges from 0.10 (in the case of the Equator near 23 W) to 0.18 (in the Gulf Stream case). std(US) is the standard deviation of the Stokes drift magnitude over the region that contribute to US,R using eq. (WB3). The distance

r2 =√[∑ (r(θ’) cos(θ-θ’))2 / (Na/2+1)]

is the root mean square distance between the position of the footprint for azimuth θ and the footprints for θ’ needed for the estimate of US,R, weighted by cos(θ’ - θ). Hence, in the open ocean r2 is completely specified by the geometry of the footprints, itself given by the rotation speed of the horn plate and the number of beams, and r2 is the same from one megacycle to the next.

Figure 4.14: Use of cycles in azimuth θ’ for the estimation of the integrated parameters at the location (black square) of cycle with direction θ. The background color shows the magnitude of US, and the bottom spectra are at the location of different cycles, with wave energy plotted in the direction from which it arrives. In each spectra the red arrow is direction θ, and the black and grey arrows show θ’ and θ’+π. The distance r(θ’) is a source of error.

It should be noted that r2 is less for the 6° beams compared to the 12° beams, because their ground tracks are more curved and located at the center of the swath: cycles with other directions are on average nearer. This gives a lower value of errWB by 30% on average for 6° incidence.

The ε factor may be a function of the current gradients, but we have found that it is fairly stable over the range of conditions investigated. A typical example of errors estimated over the Gulf Stream is shown in figure 4.15

Figure 4.15: Example of current and wind situation over the Gulf Stream and associated errors for the estimation of UWB for beam 8 (12° incidence, green circles in top panels). In this example ε = 0.18 and the standard deviation of errWB is 10 cm/s, which is 9% of G times the mean value of US on that beam.

In coastal areas the closest usable cycle for azimuth θ’ may be further than in the open ocean, so that r2 can increase, up to a factor 3, near coasts that are aligned along the track (Figure 4.16).

Figure 4.16: Values of the beam distance r2 which comes in eq. (WB5), showing the impact of the coast on swaths that are nearly parallel to the coast. This tends to increase error levels, together with larger gradients of US near the coast.

Another source of error comes from the estimation of the G factor. G can be obtained from the ratio of the backscatter and Doppler modulation spectra, as detailed in 4.2.3 below. Error on this estimate of G, and the statistical error on the estimates of US,1D (θ’), have been taken into account by augmenting errWB by a factor 2 compared to eq. (WB5).

4.2.2.5. LEVEL 3: GRIDDED TOTAL SURFACE VELOCITY

The estimation of level 3 (gridded) products relies on the estimated errors of specific level 2 products, i.e. errDOP and errWB detailed in the previous sections 4.2.2.2 and 4.2.2.4 in the case of the total surface current, but also on sampling effects. A particular issue for vector quantities in the SKIM measurement geometry is that only radial velocities (i.e. line-of-sight velocity projected on the mean sea surface) are accessible for each cycle, and thus the vector components such as U and V for the total surface current must be mapped from the combination of measurement cycles onto a grid.

Figure 4.17: Illustration of current components U and V and radial component UR in the case of the Gulf Stream.

This gridding is first done across the swath of a single satellite pass (level 3a), then by combining different satellite passes (level 3b). The algorithm we have tested is based on the optimal interpolation (OI) of coastal HF radar data by Kim et al. (2008), with adaptations described here and in Ubelmann et al. (submitted). A side-product of the OI method is an error estimate.

Figure 4.18: Schematic of radial and gridded velocity fields

The radial velocity UR is not directly the 2-component field (U,V) represented on the grid. However, they are linked by a linear projection operator, which makes OI possible. The gridded velocity vector [ U …. V…. ] has dimension 2Ng with Ng the number of points on the grid, it is our “state vector”. Kim et al. (2008) give

[ U …. V…. ] = BHT(HBHT + R)-1 [ UR…]

The observation error covariance matrix R is filled according to the best knowledge of error statistics (see level 2 above). The error-free covariance matrices between gridded and radial (BHT), and radial and radial (HBHT) components assumes a Gaussian covariance error model, with no correlations between U and V components. As shown on figure 4.19, this is not the best possible covariance model.

Figure 4.19: Covariance model derived from numerical simulations (top) or using the Kim et al. approach (bottom).

This is where our method diverges from Kim et al. We chose to implicitly include covariances between U and V component. This is done using a Helmholtz decomposition in rotational and divergent parts. This is motivated by the fact that rotational and divergence fields are more isotropic than directional velocities and their covariances could be better known (e.g from altimetry and velocity transect spectra), and the rotational part is generally dominant at the scales of interest (Figure 4.20).

Figure 4.20: Example of divergence (left) and vorticity (right) off the Oregon coast, from a ROMS simulation. Scales are in kilometers.

Ubelmann et al. (submitted) has derived a covariance model that uses the streamfunction Ψ and potential Φ, such that U = ∇xΨ + ∇Φ. The advantage is the specification of covariances in the (Ψ,Φ) space (illustrated on Figure 4.21) which can be better known (e.g. from altimetry for the non-divergent part) with lower cross-correlations than U and V. The implementations of these covariances in the general OI formula are detailed in the article.

Figure 4.21: upper left: synthetic power spectrum chosen for the rotational current function, in (m/s)**2/cycle/km. Upper right: Inverse Fourier transform of the power spectrum, giving the autocovariance as a function of the distance (km) used to fill the covariance matrices. Lower left: first derivative. Lower right: second derivative and first derivative over L, both used to fill the covariance matrices as well.

The new covariance model generally performs better in fitting the covariances in the output of ocean circulation models as illustrated on Figure 4.22, compared with the covariance model on Figure 4.19.

Figure 4.22: Covariance. Empirical (top) and theoretical (bottom, see Ubelmann et al., submitted, for details).

In the end, the performance of the method is evaluated by the effective pixel resolution of both the L3a and L3b products. This resolution is defined as half the wavelength at which the coherence between “truth” and the product drops below 0.5. It is shown in figure 41 for the case of our Gulf Stream simulations. Here, the threshold wavelength being 65km, this resolution is dxeff = 32.5 km. We further define an accuracy of the L3a product (at that resolution) by computing the error against the the truth filtered at dxeff pixel size. This gives here errors of 0.09 and 0.14 m/s for the along and across track velocity respectively in the L3a, and 0.23 and 0.24 in the zonal and meridional direction respectively for L3b.

Figure 4.23: Average coherence of L3a simulated current vector field with the “truth” provided by MITgcm simulations, in the case of the Gulf Stream, for October 2011.

To illustrate that our method is not limited to non-divergent fields, an example of estimated Level 3a field is shown in figure 41 for the case of equatorial currents.

From the velocity field, it is possible to estimate gradients, and in particular the generally weak convergence, that is critical for fluxes of nutrients between the deeper ocean and the surface. Figure 4.24 shows that SKIM should be able to resolve the divergence field at the scale around 100 km. Other quantities (level 3 and 4 products) will likely be derived from this divergence: these are vertical velocities and, using ancillary data, vertical fluxes of nutrients, accumulation of microplastics … Regions of large negative divergence may be 'hotspots' of accumulation of marine plastic debris.

Figure 4.24: Illustration of SKIM capability to recover snapshot of current divergence in the equatorial upwelling.

Finally we estimated the performance of different layouts of beams and rotation speed for the SKIM horn plate, for the three configurations defined in section 4.2.1. From the measurement geometry, the largest errors are on the edge of the swath for the along-track velocity, and in the center for the cross-track velocity. Compared to the SKIM1 version used in the 2016 proposal, the addition of 2 beams, from 6 to 8, and a lower number of azimuths gives generally lower errors in SKIM2. If shorter cycles are used, as in SKIM3, it is better to use a higher rotation speed to make the sampling more homogeneous.

Figure 4.25: Optimal interpolation error for a 350 km long segment of a SKIM swath, for 3 different beam and rotation speed settings, with error parameters corresponding to the Gulf Stream region. Each black dot is the center of a measurement cycle.

Region:	Equator	Fram (open water)	Fram (ice)	Gulf Stream	Oregon coast
input L2 error (12° & 6° beams) NB: see table 4.1 for details	0.08 & 0.15 m/s	0.10 & 0.16 m/s	TBD	0.16 & 0.20 m/s	0.16 & 0.20 m/s
L3a (single swath): Leff , along & cross-track error NB: mission goal is 80km, 0.1 m/s for L3a.	89 km, 0.03 & 0.05 m/s	59 km, 0.11 & 0.14 m/s	TBD	65 km, 0.09 & 0.14 m/s	90 km, 0.04 & 0.05 m/s
L3b (multi-swath): Leff , zonal & meridional error	290 km, 0.14 & 0.18 m/s	62 km 0.12 & 0.13 m/s	TBD	120 km, 0.23 & 0.24 m/s	135km, 0.09 &0.08 m/s

Table 4.6: Summary of effective resolutions and errors of L3a (single swath) and L3b (multiple swaths over time) surface velocity products for the SKIM2 set-up. The effective resolution is the wavelength at which the along-track spectrum of the cross-track current component crosses the noise level.

As expected from the formal errors, we obtain better performances for along-track than across-track velocity for L3a everywhere. The difference is less when looking at zonal versus meridional components in L3b, except for the Equator, but this may be due to strong anisotropy of equatorial currents. We also note that the relatively low errors at the Equator may be a methodological bias due to the use of CMEMS 1/12° currents, which are smoother than the 1/48° MITgcm currents used for the other regions.

In general, L3b has coarser effective resolution than L3a, meaning that the rapid ocean time variability overcome the time-accumulation of independent data. Particularly true at Equator where the time scales of variability are very short. However, in Fram, the short revisit time gives the same error level in L3b and L3a, showing the potential for continuously monitoring of the high latitudes up to 82°N. Over ice the error is expected to be comparable with a compensating error of higher Doppler noise due to a lower backscatter and SNR, but a lower wave error due to much lower Stokes drift because short waves are strongly attenuated (e.g. Squire et al. 1995). Hence, for the figure on the cover page we have kept constant error statistics across the ice edge.

4.2.3. Ocean wave spectra

4.2.3.1. GENERAL PROPERTIES OF A DOPPLER WAVE SCATTEROMETER

For a radar footprint broader than a typical ocean wave wavelength, the reflectivity and Doppler variations, associated with the local wave facet tilts and velocities, will be laterally averaged across the beam. This across-beam averaging will act to eliminate or 'cancel out' any plane surface wave contrast not aligned with the beam direction. Only surface wave geometry and velocities with a phase front that 'matches' the electromagnetic phase front, can coherently emerge from this across-beam averaging.

This principle clearly opens the basis to retrieve the directional properties of modulated backscattered signals, intensity and phase, along the range direction. The phase-front matching principle results in very high directional resolution (about 1° in azimuth): the range reflectivity modulation analysis, in any given azimuth direction, provides a very narrow slice through the two-dimensional surface wave slope number spectrum. This is also true with Doppler modulation. First, this will provide an unambiguous propagation direction of the detected reflectivity (intensity signals) modulations. More importantly and innovatively, the range Doppler modulation analysis, in any given azimuth direction will give, for the first time from Space, very narrow slices through the two-dimensional surface wave orbital velocity spectra (Nouguier et al., submitted). This should dramatically improve the estimation of long swell properties because the orbital velocity spectrum goes like k E(k) with E(k) the wave elevation spectrum, whereas the slope spectrum is k2 E(k). See also section 4.2.4 for sea ice.

Measurements with real aperture radars are not impacted like SARs by local detected motions, leading to the strongly nonlinear mapping effects (Hasselmann et al., 1985) and along-track resolution losses in SAR imagery over the ocean (e.g., Kerbaol et al., 1998). With rotating multiple-angle beams and improved sensitivity, the Ka-band SKIM instrument will extend the long wave analysis capabilities of SARs and of the future Ku-band SWIM. The Ka-band sensitivity will especially help to more precisely characterize small waves (down to 20 m wavelength), with an unprecedented directional resolution.

This capability has been recently illustrated using well-resolved along-range Sentinel-1 measurements. Indeed, analyzing Sentinel-1 C-band data without SAR processing, high-pass filtered radar signals (wavelengths shorter than 20 m) exhibit very strong sensitivity to both wind speed and direction. For Sentinel 1 and future SKIM measurements, this property is very useful for a robust retrieval algorithm to estimate a local surface wind vector, and to constrain the wave-motion correction in the interpretation of the directional Doppler measurements as surface currents.

Still, there is a trade-off between the half-footprint size Ly (a larger footprint gives a directional resolution δ ~ 1/Ly ), and the signal to noise ratio, which decreases like 1/Ly. Compared to SWIM, the use of Ka-band allows for a smaller footprint (6 km instead of 18 km), but this may still be too large for the shortest components. For this reason, and also to allow the flagging or removal of backscatter inhomogeneities, SKIM will also use an unfocused SAR processing.

4.2.3.2. UNFOCUSED SAR

Figure 4.26: Ground resolution for 12° beam.Black=Antenna footprint – Green=Processed Doppler bins – Red=Iso-range (every 400 m) Doppler processing can provide some azimuthal resolution better than Ly except for beams along the track. It may thus be possible to resolve slightly shorter components when the beams are pointing across the track.

4.2.3.3. LINKING RANGE-RESOLVED MEASUREMENTS AND DATA PRODUCTS

Effective resolution

In a strict signal processing point of view, the minimum detectable wavelength of the spectrum is 𝜆min=2δx, δx being the final ground resolution of the signal. δx is firstly linked with the radar signal bandwidth B, with B=200 MHz giving 0.75 m in radar line-of-sight. Secondly, the impact of the misregistration between the pulses of the cycle has to be quantified to get the final range resolution. The pulses are coregistered within a cycle through the on-board Chirp Scaling resolution (see section 5.1.2.6). The residual errors due to approximations are below 0.5 m. This assumes that the impact of Earth rotation is taken into account (requiring the on-board knowledge of the position on the Orbit). This leads to a worst range resolution of 1.25 m in radar geometry and 6 m in ground resolution. 𝜆min is thus equal to 12 m,

The effective resolution then depends on the level of the signal at those wavelengths. The power spectral density P(k,θ) of the modulations is related to the ocean elevation spectrum E in the azimuth θ and in the opposite direction θ+π (Jackson et al. 1985) ,

P(k,θ) = (2π)0.5 ( k2 MTF2) [E(k,θ) +E(k,θ+π)] / Ly

where MTF is the modulation transfer function between backscatter and slopes (MTF ~ 10), and the spectrum E is normalized such that the sea surface elevation variance is

< ζ2> = ∬ E(k,θ ) dk dθ.

Considering observed spectra from Leckler et al. (2015), the relative modulations of σ0 are of the order of 8% in the downwind direction for wavelengths between 10 and 20 m and Ly =200m, and this reduces to 2.5% (which is 0.1 dB) for Ly = 3 km. aging parameters. Resolving waves at 20 m wavelength thus requires a modulation sensitivity of 0.1 dB or better at that wavelength.

Modulation functions

For each pulse number p, the line-of-sight Doppler velocity u(x,p) and backscatter σ0(x,p) is resolved in range x with 2014 gates of 3 m (at 12°) or 6 m (at 6°) but integrated in azimuth. These two signals have a modulation that bears the signature of ocean waves. From the analysis in Nouguier et al. (submitted, eq. 34 and 35) we have

cos4θi σ0(x,p) =	(MOD1)

2ke u(x,p)= (MOD2)

The parenthesis term in this last expression is a “contamination” by the σ0 modulation that adds up to the velocity modulation given by the last term. In theory, this first contaminating term is negligible for 12° incidence and grows for larger incidences. The overbar in the last terms is equivalent to the 1/Ly term of Jackson et al. (1985).

In order to mitigate the distortion of the spectrum, it is better to use the spectrum of the NRCS-weighted Doppler, which removes the denominator in eq. (MOD2), as illustrated in Figure 4.27.

Figure 4.27: Left: spectra of Doppler, right: spectra of NRCS*Doppler compared to the input spectrum in black.

The presence and magnitude of such modulations for Ka-band data at incidences 1 to 70° was documented with a wide range of conditions measured from the Katsiveli platform in the Black sea (Yurovsky et al. 2017a,b), and illustrated in figure 4.28.

Figure 4.28: Example of radar backscatter and Doppler velocity (ULOS) acquired on the Katsiveli platform on 19 September 2015, the incidence angle is 10° and the wind speed is U10= 9.7 m/s.

Modulations in airborne data at 4 to 12 degrees incidence has been confirmed by AirSWOT data acquired over the Gulf of Mexico, in February 2017 (see figure 4.29). When integrated over 400 m, the single look complex SAR doppler gives a RAR-like signal, with a high resolution in range.

Figure 4.29: Example of Doppler recorded on AirSWOT flight #16201, 7 February 2017, integrated over 6 km along the flight track (the scale of the azimuth integration in SKIM footprint). This shows both a modulation of the Doppler and the mean Doppler G US (See Nouguier et al., submitted, for details). The wind speed was 9 m/s.

Figure 4.29 shows the resulting Doppler as a function of range. Its large scale variations correspond to the wave bias UWB=GUS that is of the order of 2 m/s and decreases with incidence angle as predicted by Nouguier et al. (submitted).

4.2.3.4. ESTIMATION OF THE WAVE SPECTRUM E(k,θ)

Using only σ0(r) the modulation spectrum Cσσ(k,θ) gives the directionally ambiguous spectrum E(k,θ)+E(k,θ+π), with a magnitude of Cσσ(k,θ) that is proportional to the azimuth integration width Ly (Jackson et al. 1985a, Caudal et al. 2014, eq. 5). A reduction in speckle noise can be achieved by taking the real part of the co-spectrum with a small time lag (Caudal et al. 2014, eq 7),

C’σσ(k,θ) = < Real { FT[ σ0(x,p) ] FT*[ σ0(x,p+dp) ] >

where the < . > brackets stand for an average over possible values of p, FT is the Fourier transform, and * represents the complex conjugate.

The same thing applies to the Doppler velocity,

C’uu(k,θ) = < Real { FT[ u(x,p) ] FT*[ u(x,p+dp) ] >

and their cross-spectrum

C’uσ(k,θ) = < Real { FT[ u(x,p) ] FT*[ σ0(x,p+dp) ] >

We can thus use either C’σσ(k,θ) or C’uu(k,θ) to estimate E(k,θ)+E(k,θ+π). Preliminary results from the SEAMOTION RADAR simulator indicate that C’uu(k,θ) is less noisy for waves longer than 60 m wavelength, as expected from its theoretical link with the orbital velocity spectrum [ E(k,θ)+E(k,θ+π) ] x gk tanh (kD), whereas shorter components are less noisy with C’σσ(k,θ), as expected from the link with the slope spectrum [ E(k,θ)+E(k,θ+π) ]x k2.

We thus combine both spectra to obtain our ambiguous wave spectrum,

[ E(k,θ)+E(k,θ+π) ] = αu(k,θi) C’uu(k,θ) + ασ(k,θi) C’σσ(k,θ)

where αu(k,θi) and ασ(k,θi) are empirical functions of the wavenumber and incidence angle, provided by a look-up table.

In order to limit the effects of inhomogeneities in the scene backscatter, which is the source of spurious signals at large wavelengths, the Fourier transforms, spectra and co-spectra will be performed over 512 points only (1.5 km at 12° incidence) and averaged using a Welch-type method (Welch 1967) after removing segments with anomalous variance. The use of Doppler spectra should already limit the impact of back-scatter inhomogeneities caused by slicks, ships, internal waves…

The directional ambiguous spectrum obtained for each cycle is then “dis-ambiguated” by looking at the sign of the co-spectrum. Consistency checks are applied across the swath.

4.2.3.5. ESTIMATION OF G

The ratio cos2θi [σ0(x,p) - σ0] / [sinθi σ0 u(x,p) ] is equal to G ω / k where G is factor used for the current retrieval, and ω is related to k via the ocean wave dispersion relation. Hence G is obtained from the ratio of the two spectra.

4.2.4. Measurements over sea ice

4.2.4.1. BACKSCATTER AND ICE DRIFT

Likewise, across the ice edge, there are very different sea state and backscatter properties. We have not yet performed simulations of SKIM signatures over the ice at the same level as over open water, e.g. using the SCALIAN Seamotion-Radar simulator, and we do not yet have high resolution Ka-band data over ice. Still the analysis of existing airborne and satellite sensors over the ice from the GPM mission provides interesting data up to 9° incidence.

The joint analysis of GPM and ice concentration from OSI/SAF shows a lower backscatter over ice at 6° to 9° incidence by about 8 dB on average (Figure 4.30). These data contain only southern ocean ice, where the snow depth is expected to be significant (e.g. Markus and Cavalieri 1998). It has been shown for SARAL/ALtiKa that the instrument is more sensitive to scattering from the air-snow interface and/or the snow volume (e.g. Armitage and Ridout, 2015). Since echoes from conventional Ku-band radar altimeter penetrate to the ice surface, the comparisons with Ka measurements allow to improve estimates of snow depth.

Figure 4.30: Ka-band backscatter as a function of incidence angle for open water and sea ice pixels from GPM in Ka band. The colors shown the number N of data points (figures courtesy of N. Longepe, CLS).

We thus expect higher backscatter in Arctic sea ice, with a level typically 8 dB below the backscatter over the ocean. This should lead to a lower SNR and higher Doppler error (figure 4.31). Finally, there may also be a benefit of alternating the polarization of the horns on the rotating plate to distinguish ice features. This will be evaluated in phase A.

Figure 4.31: Estimated rms error over ice compared to water when reducing the backscatter by 8 dB over the ice compared to water.

4.2.4.2. SIGNATURES OF OCEAN WAVES

There has been much progress in the understanding of wave patterns in C-band SAR imagery over sea ice, with the main imaging mechanism due to the SAR processing in presence of wave orbital velocities (Ardhuin et al. 2015b, 2017a). In the case of a Doppler real aperture radar like SKIM, we may get a weak modulation of the backscatter due to the convergence of ice floes at the crests, but should still get modulation of the Doppler velocity, and, for the 6° incidence there should be a detectable range bunching for wave conditions such as shown in figure 4.32.

These modulation mechanism will be investigated in phase A using airborne data such as shown here.

Figure 4.32: top: Example of airborne lidar surface elevation corresponding to 2 km of data, located 10 km within the ice, off the west coast of Greenland. The gray and black arrows indicate the dominant wave direction (before and after correction for aircraft motion). The alternating high/low stripes orthogonal to that direction are the peaks/troughs of individual waves. Bottom: Color image mosaic over the same area. (from Sutherland and Gascard, 2016. Data: CRYOSAT validation campaign, CryoVex 2006, courtesy of Henriette Skourup and Rene Forsberg).

4.2.5. Delta-K processing

An additional technique, that could not be fully evaluated for this proposal (but that will be more thoroughly theoretically investigated before the end of 2017), is the use of the large frequency bandwidth to cross-correlate echoes at two different frequencies. Alpers and Hasselmann (1978) already discussed how line-of-sight microwave backscatter can be modulated by the longer gravity waves, and the phase velocity of these waves well determined from a resonant "spike" that appears in the cross product power spectrum formed from multiplying the microwave signals backscattered at two different frequencies. These authors also further discussed that the Doppler shift of the resonant "spike" corresponds to the propagation velocity of gravity waves whose wavelength satisfies the prescribed "resonance condition". With typical ‘beat-waves’ wavelengths of about 20 and 50 m, the PP-delta K implementation on SKIM measurements can thus mimic, from space, the measurements usually performed on-ground, with coastal HF radars. Namely, the Doppler spectrum at these selected wavelengths gives a measurement of the dispersion relation of ocean waves and its anomalies. Adding to the other Doppler estimates, from the range-resolved analysis, this delta-K technique can thus further constrain the estimation of U, and possibly its vertical shear (Shrira et al. 2001).

A priori, the following beat frequencies and corresponding ocean wavelength should be interesting,

f2-f1=90 MHz, corresponding to lw=8m(12°) – 15.9m(6°)

f3-f2=180 MHz, corresponding to lw=4m(12°) – 8m(6°)

f3-f2=90 MHz

A detailed analysis will be done in phase A to build the best filter bench with respect to the on-board complexity.

4.3. Observation requirements

4.3.1. Geometry and observing conditions

Monitoring of marginal ice zones requires a high inclination orbit, allowing to cover Arctic regions up to 81° N. This high latitude is compatible with a sun-synchronous orbit (SSO). SSOs also mean low Earth orbit (LEO) with an altitude of the order of 700 km. This altitude is compatible with the required radar power and expected backscatter at near-nadir. Power constraints on small platforms further restrain the orbit choice to a dawn / dusk position (6 to 7 AM or PM local time).

Further, the complementarity with today's Sentinel 1 (measuring a single component of the Doppler velocity and the long swell part of the wave spectrum) have led us to propose an orbit with a specific phasing relative to Sentinel 1 (S1). For CAL/VAL purposes we advise to maximize the overlap with Sentinel 1C (which should have the same orbit as today's Sentinel 1A), by shifting the ground track 4° in longitude to the East. This shift accounts for the different incidence angles of S1 and SKIM. After the SKIM CAL/VAL we expect to either stay on that orbit or shift further east to maximize the joint coverage of SKIM and S1. This thus calls for a 98° inclination SSO orbit, with an altitude of 695 km.

4.3.2. Revisit

Additionally, SSOs with high inclinations allow a frequency revisit at latitudes above 70°, depending on swath width, which is compatible with the monitoring of the smaller scale motions found there (due to a smaller Rossby radius of deformation), including ice jets. The requirement for Equatorial currents is not so critical in terms of revisit due to the larger scale of structures that are not monitored at all today in terms of current velocity. The main driver for the revisit time is the swath width, hence the choice of maximum incidence angles, because a larger incidence angle giving a wider swath. Our choice of a maximum incidence of 12° gives a swath width of 270 km. At the Equator, the revisit time should be less than 10 days when including ascending and descending tracks (Figure 4.33).

Figure 4.33: Illustration of temporal sampling with an orbit shifted 4° to the East of S1A, and a 280 km swath.

Indeed, according to our knowledge of the ocean mesoscale time variability, these short time revisits below 10 days should allow the observability of scales down to 70 km in wavelength for the Gulf-Stream example (Figure 4.34). This would offer a nice complementarity with missions such as SWOT constrained to a 21 day repeat cycle (imposed by near-range SAR coverage) that will not reconstruct current below L~115 km in L3b products (time continuous). These results are consistent with the effective resolution found experimentally in the simulated L3b products for SKIM (71 km resolution in the Gulf Stream), indicating that the temporal variability of the ocean is one of the the main drivers of the effective resolution of the L3b products.

Figure 4.34: Decorrelation time as a function of wavelength in the Gulf-Stream region from the MITgcm model. The decorrelation time is defined by the time lag for which the spectral coherence is 0.5 for a given wavelength.

4.3.3. Incidence angles

The maximum incidence angle is constrained by the expected return power, the technical capability to focus the radar beam on a footprint ~ 6 km in diameter, and the capability to measure the wave spectrum accurately. The SKIM proposal provides a consistent system with a maximum incidence angle of 12°. Larger incidence angles could be feasible with different antenna technologies, but these would have to use a large power to arrive at the same SNR level.

4.3.4. Coverage

The SKIM coverage should be near global (81°S to 81°N) given the mission objectives across all oceans. Increasing coverage towards the pole may allow catching a little more of the summer MIZ as the Arctic sea ice minimum may indeed be further than 81°N, as happened in September 2012. However, this would require either incidence angles much larger than 12° or a non-Sun synchronous orbit.

4.3.5. Resolution

SKIM is truly a multiscale instrument by design because it aims at global scales while resolving ocean waves with a wavelength of a few meters. There are two important spatial resolutions that must be chosen. One resolution is the shortest ocean wave wavelength Low,min that should be resolved, the other is the shortest wavelength Lv,eff in the variation of mean surface velocity and wave spectral properties that should be effectively resolved (signal > noise) in L3a products. These two scales Low,min and Lv,eff vary with the different scientific objectives, as dictated by the targeted phenomena. Their translation into actual data resolution and sampling strategy (beams, cycle duration, rotation speed …) is explained below.

A broad consensus in the mission science team is that Low,min = 20 m, and Lv,eff = 100 km (meaning dxeff = 50 km) will allow to achieve the science goals, and these should thus be requirements of the mission. We also recognise that further reducing Low,min to 10 m would reduce errors on the wave bias correction and allow a better analysis of the wind stress vector in relation to short waves. Likewise, Lv,eff = 100 km is sufficient to resolve well the mesoscale in most regions of the ocean (Figure 4.35), and critically fill the gap in tropical observations, but a more ambitious goal Lv,eff = 60 km may be feasible and help resolve a wider range of scales and their interactions.

Figure 4.35: Space and time scales ocean circulation (dark blue) and waves (light blue), and complementarity of SKIM space-time coverage (red boxes) with other current measurement systems (green). Note that HF radars only cover a few hundred kilometer from shorelines.

4.3.6. Accuracy

As explained above, the accuracy is linked with the resolution. To achieve the SKIM objectives, an accuracy of 0.1 m/s is required for each current component (U and V) at a wavelength of 100 km. This also requires an accuracy on the estimated Stokes drift components (US and VS) of 0.5 cm/s or 10%, whichever is greater.

4.3.7. Calibration and validation

4.3.7.1. BACKSCATTER

As detailed in section 5.1.2.2, SKIM has an internal calibration mode that uses a calibration horn below the antenna reflector. This will be used to assess the stability of the absolute gain of the 8 beams up to the antenna horns. Calibration will also be done by comparing data acquired in the normal acquisition mode with known radar cross-sections over homogeneous surfaces and at crossing points with other Ka-band instruments (today this would use the Global Precipitation Mission (GPM) Ka-band radar). This

cross calibration should allow provide fixed bias of the error budget. The remaining random error should be below 0.4 dB for each beam.

4.3.7.2. TOTAL SURFACE CURRENT

The primary source of data for surface current validation will be coastal HF radars that have a unique capability to measure the total surface current but have a coverage limited to a radius within a few hundred of kilometers from SKIM. HF radars have a range resolution depending on bandwidth (1.5 km for 100 kHz) and an angular resolution that depends on the processing method (e.g. Yaremchuk and Sentchev 2009), but typically of the order of 10 km at a distance of 100 km from land. HF radars can thus be very useful in confirming the actual spatial resolution of SKIM products for currents. HF radar data are now being integrated at the European level (Mader et al. 2016) in the context of EuroGOOS and CMEMS Service Evolution. Similar efforts have already been made in the USA, so that data will be available for validation.

Further global-scale validation data will be provided by the Drifting Buoy Program that operates buoys drogued at 15 m that give a near-surface current closely related to the total surface current. (http://www.aoml.noaa.gov/phod/dac/ ).

4.3.7.3. WAVE PARAMETERS

With unprecedented directional and spectral resolution, SKIM will provide unique wave data that cannot be fully validated by existing measurement systems. The network of wave buoys used for the JCOMM wave model verification program (Bidlot et al. 1997, 2016) only provides significant wave height (Hs) and peak or mean period. As part of CMEMS, work is underway to have a wave data production system that would provide the “first 5” parameters: at each frequency these are the energy, and first directional moments. There is thus a need for dedicated CAL/VAL work to verify the details of the directional wave spectrum. The other issue is the spatial variability of wave parameters. Down to a wavelength of 80 km (Ardhuin et al. 2017) this can be performed for Hs using other altimeter missions at crossing points to evaluate cross-track variability.

4.3.8. Summary of SKIM mission requirements

The requirements expressed in the previous sections are summarized in Table 4.7. When appropriate,

three levels of requirements are used. Otherwise a unique value is given when there is no possible trade-off.

These 3 levels are:

- Goal: is the value that, if exceeded, would not yield significant improvements in mission performance.

- Threshold: is the value below which the observation would not yield any significant benefit for the mission.

- Breakthrough: is an intermediate value between “threshold” and “goal“ that, if achieved, would result in a significant improvement for the mission. The breakthrough level is expected to be more appropriate than the “goal” from a cost-benefit point of view.

All requirements follow from the “top-level” requirement on the monitoring of currents, which is formulated for instantaneous total surface current accuracy of each component within a single SKIM pass. We have not defined requirements for L3b products given the wide variety of scales in different regions of the world ocean. We will define such requirements, region by region, during phase A. However, such additional requirements only have an importance for the choice of orbit, with very limited trade-offs except for considering level 4 products merging other sources of data.

Parameter	Goal	Breakthrough	Threshold	Comments
Total surface current or ice drift accuracy (each component)	0.05 m/s at Lv,eff = 50 km for L3a product	0.1 m/s at Lv,eff = 50 km for L3a product	0.1 m/s at Lv,eff = 100 km for L3a product
Revisit	< 1 day at equator	< 10 days at equator
Lifetime	10 years	7 years	5 years
Coverage	global	all ice-free ocean + most marginal ice zones
Further requirements derived from the above primary goal requirements
orbit	SSO, 695 km, -98°	N/A	SSO
Phasing with S1	+4°E, at least for cal-val	N/A	N/A
Latency	NRT	1 hour	1 day
Nadir ground track accuracy	1 km	2 km	5 km	need for nadir altimer SSH
Revisit	< 1 day at equator	< 10 days at equator
SKIM elevation pointing knowledge	0.2°	0.4°
SKIM azimuth pointing knowledge	1°	2°
Platform pointing accuracy knowledge	0.01°	0.01°
Platform pointing accuracy	0.1°	0.2°	0.3°
Platform stability (Relative Pointing Error)	1 arcsec over 20 s	7 arcsec over 20s	needed for accurate retrieval of attitude
Relative radiometric precision between beams (for modulations)	0.01 dB	0.02 dB	0.1 dB
Vector precision knowledge	0.1°	0.2°	Azimuth angle between 2 radar measurements on the same point
Absolute radiometric precision	0.5 dB	1 dB
Minimum resolved ocean wavelength Low,min	10 m	15 m	20 m
Associated requirements for secondary sea state goals
SWH accuracy (from wave spectrum)	15 %	20%
Wave direction (from wave spectrum)	5°	15°
Wind speed (from nadir)	2 m/s (or 10%, the larger)

Table 4.7: Summary of mission requirements

5. Proposed mission architecture

5. 1. General concept

The SKIM system concept will fulfil the mission requirements as described in section 4, as well as the boundary conditions of the Earth Explorer – 9 as reproduced here:

- The industrial development cost shall not exceed 150M€ for the space segment.

- The implementation program shall lead to a launch date in 2025 at the latest, fitting in a Vega-C dual-launch

- The mission concept shall exhibit a certain degree of scientific ( 4 ≤ SRL ≤ 6) and technological maturity.

The architecture of the system is the result of a phase 0 study for the SKIM instrument, performed by Thales Alenia Space (TAS) under CNES contracts. The instrument design has been adjusted after iteration with the science team. The interface with the satellite platform is fairly generic. Here we present the system with a Proteus150 PF, but it is likely that other small LEO platform would be appropriate.

The system is built around a high-resolution Ka-band Doppler altimeter, measuring at nadir and rotating off-nadir beams (0, 6 and 12° incidence angles), interfacing with a CFOSAT-like ground segment based on an extended altimeter type processing structure enabling the generation of specific SKIM products.

Space segment

The space segment requires a single small satellite in LEO near-polar Sun Synchronous Orbit with Local Time at Descending node between 6h and 7h in the morning. The satellite and payload performances are compatible with orbit altitude range between 500 km to 700 km. Because SKIM can complement Sentinel-1 (S1), the S1 orbit with a shift in longitude is considered in the following. The shift could be +4° to maximize overlap on ascending tracks, given the different incidence angles, or different to maximize the complementary coverage of the two missions. The satellite track is controlled at all latitudes with an accuracy of +/- 1 km (similar to Jason and Sentinel 3 series satellites), degradable to +/- 5 km.

The instrumentation relies on a Ka-band low incidence Doppler scatterometer using a mechanical low rate conical scanning (3 to 6 rpm) around the Nadir direction.An X-Band Payload Data Handling Transmitter is considered to download the instrument telemetry.

The payload is implemented on a small platform class illustrated on Figure 1 and detailed on section 5.2.3.

Figure 1: SKIM flight configuration.

5.2.1. SKIM payload

SKIM payload architecture is shown on Figure 2, it includes:

The SKIM Ka-band Radar (SKaR), elements outlined in blue on the figure 2. SKaR architecture is herited from SWIM instrument (breakdown and antenna) but also takes benefit of on-going altimeter developments for ESA (Poseidon 4 / Sentinel-6) and CNES (KaRIn RFU / SWOT). Star trackers (ST1 & ST2) are mounted on the antenna structure to provide accurate pointing knowledge (few arcsec). They will be also used by the satellite AOCS.

An autonomous X-band telemetry system (PDHT) used for the science data telemetry.

A GNSS system for Precise Orbit Determination and for monitoring of the SKaR reference clock (USO 10 MHz).

A Laser Retro-reflector array to complement the POD system.

The implementation of the POD system enables to have on-board a Nadir beam of high altimetry quality not absolutely required for SKIM primary mission but affordable without critical impact on satellite design and development plan.

The architecture and the operation of the instrument are derived and similar to the SWIM instrument developed for CFOSAT mission (Hauser et al., 2017). Major differences with SWIM are

- the selection of the Ka-band instead of Ku-band,

- the capability to produce higher spatial resolution products,

- the ability to provide Doppler measurements for surface kinematics retrieval.

The SKaR architecture is addressed in more details in section 5.2.2.3.

Figure 2: SKIM payload functional block diagram.(A payload architecture based on an altimeter/Doppler scatterometer and a X-band SSPA PDHT).

Instrument technical solution

The SKaR is able to operate both in Real Aperture Radar (RAR) and un-focussed Synthetic Aperture Radar (SAR) modes. The unconventional conical scanning geometry of the radar provides radial range sampled echoes at low incidence angles (6° and 12°) processed on-board to obtain radar cross-section profiles with respect to incidence, radial 1D amplitude and Doppler spatial wave spectrum and integrated Doppler measurements over each beams (used for current products). A nadir beam, that is an along-track sampling SAR altimeter, is implemented and designed to obtain accurate altimeter measurements. This Nadir beam could also be used (as for SWIM) for tracking of off-Nadir beams echoes (reception window control).

5.2.2.1. Operating parameters selection

The selection of instrument parameters is mainly driven by the need to provide accurate Doppler measurements with a dense sampling of the swath.

The basic principle of the Doppler measurement relies on the estimation by the pulse pair processor technique (autocorrelation of 2 succesive echoes, see section 5.2.2.5) of the Doppler spectrum mean frequency. The selection of the Ka-band (accuracy proportionnal to radar wavelength) and a high Pulse Repetition Frequency (Doppler spectrum sampling) are compulsory (see Figures 3 and 4).

Figure 3: Range resolution cell Doppler spectrum.For a non moving surface the Doppler spectrum of a range resolution cell (obtained in RAR mode) depends on the azimut extension of the cell in the satellite flight direction. Different theoretical spectrum are plotted for three LOS direction with respect to satellite movement direction. The unambiguous range corresponds to the PRF (equivalent to 134 m/s), so very quickly spectrum are many fold aliased. Anyway, the pulse pair processor estimates the mean frequency of the spectrum and the estimator is not biased by any under-sampling while the power spectral density width remains weaker than the unambiguous range. Note that the threshold in the different plots corresponds to the thermal noise floor and does not skew the estimation.

Figure 4: Instrument PRF selection.The Line Of Sight velocity resolution (instrument noise on the Doppler measurement) are plotted as a function of PRF for two different LOS observation geometry. The antenna diameter considered is 1.2 m. Optimal performances are obtained for PRF around 30 kHz and SNR in the range 5 to 10 dB (performance driven by geometric decorrelation). Increasing PRF will reduce significantly SNR and then performances.

The dense sampling of the swath is obtained thanks to the rotating system of the antenna feeds (offset parabolic antenna with fixed reflector, see later). The complete azimuth spread is covered (see Figure 4.3) by the conical scanning and an equivalent 270 km swath is obtained. The maximal configuration envisaged 8 beams with the following incidence angles: Nadir, 2 beams at 6° and 5 beams at 12° equally spaced in azimuth (see Figure 4.3). The number of beams is linked to the rotation speed and has been optimised with respect to current retrieval simulations.

The instrument chronogram is sequential, the pulses being transmitted and received on one beam before switching to the next one (Figure 7).

Figure 5: SkaR instrument chronogram(AR = Ambiguity rank – NIMP=Nb of echoes per cycle – PRF=Pulse Repetition Frequency of transmitted pulses and received echoes). The cycle and macro-cycle parameters (NIMP and kind of beams) are parameters of the radar data base. So the duration and configuration of the macro-cycle can be modified in-orbit to obtain shorter macro-cycles or to change order of beams for instance.

After a sequence on a given beam and before transmitting on the consecutive one, a dwell period is necessary to receive the last pulses transmitted. The complete transmission from nadir beam to the last 12° beams is called a macro-cycle. Beam type and cycle duration within a macro-cycle are programmable from ground using the Radar Data Base stored within the on-board software reprogrammable by telecommand. The antenna rotation rate is fixed and can not be modified in-orbit (only stop of the rotation will be feasible). With open bursts, a configuration with a rotation rate of 3.14 rpm (17.5°/s) is envisaged (5.6 rpm for SWIM). The displacement in azimuth angle from one macro-cycle to the other is 5.1°.

Figure 6: Antenna horn position on the rotating antenna plate: SWIM and SKIM configurations.For SKIM: Nadir horn in blue – 6° horns in red and black – 12° horns in other colors. The SWIM configuration presents 1 horn per incdence angle (Nadir, 2°, 4°, 6°, 8°, 10°).

Figure 7: Beam patter for 3.14 rpm rotation and beam set-up as shown in figure 58

8 gives the main radar parameters (timing and single look resolutions) preliminary selected for SKIM. Thanks to the Nadir beam return, echoes are time tracked at the macro-cycle rhythm. Then, the reception acquisition window of off-Nadir beams is extrapolated from the range Nadir instruction and the looking geometry. Pulse Repetition Frequency (PRF) is continously adapated (by step of 20 ns) along the orbit to account for the altitude variation. These operations are managed by the on-board software and has been used for AltiKa and SWIM.

Carrier frequency	35.75 GHz
Antenna diameter	1.2 m
Rotation rate	3 to 5.6 rpm (fixed)
Bandwidth	200 MHz
Pulse length	1.6 µs
Incidence	Nadir	6°	12°
Cycle duration	36.7 ms	36.7 ms	36.8 ms
NIMP (Nb of Rx echoes) / cycle	1024	1024	1024
Ambiguity rank	151	152	154
PRF (variable)	32 kHz	32 kHz	32 kHz
Antenna 3-dB footprint	5.9 to 6.2 km	6.0 to 6.3 km	6.1 to 6.5 km
Ground range resolution (single look)	1 km (first hit)	6.9 to 7.5 m	3.5 to 3.7 m
Ground azimuth resolution (single look)	400 m*	400 m**	400 m**
Swath	NA	140 km	280 km

Table 1: Summary of main instrument parameters.

* For the nadir beam, on-board processing is reversible, so data can be re-processed on-ground to obtain better azimuth resolution (down to 10 m).

** Doppler bin width. Corresponds to the minimum achievable resolution during the rotation (side-looking). Ground resolution cells (range-Doppler) are distorted by the modification of the squint angle during the antenna rotation as shown on Figure 4.26. Azimuth resolution varies from 400 m to around 1 km at 20° squint angle. In the along-track direction resolution cell is given by range resolution and the antenna beamwidth (RAR resolution).

Operational modes

Several operational modes will be implemented and could be activated by ground command:

Antenna rotation: this mode enables to activate the antenna rotation or to stop the antenna at a given position.

Calibration modes: 3 sub-modes can be activated to check in-orbit behaviour of the instrument. The internal calibration mode is similar to the one used for radar altimeters. It is representative of the range impulse response of the radar. Two antenna calibrations sub-modes are possible using a calibration horn placed just below the antenna reflector. These enable in combination with the internal calibration mode to assess and check the stability of the absolute gain of the six beams up to the antenna horns.

Acquisition mode (TBC): The nadir beam is used to search the echo and to stabilised it in the reception window. Then, range tracking instructions are extrapolated for off-Nadir beams. In the meantime echo power for each beam is also estimated and receiver gain is adjusted for each beam. This is a transient mode allowing the nominal tracking mode.

This mode will probably be removed since an open loop tracking based on satellite navigator bulletin and Digital Elevation Model stored in the instrument can be easily managed (as for Sentinel-3 and Sentinel-6 altimeters).

Tracking mode: This is the measurement mode providing all the different science measurements (see section 4.1.1.4). The tracking mode can be parametrized through the on-board software: with or without antenna, different averaging of pulses, etc.

All these operational modes are implemented and have been validated on SWIM instrument.

Architecture

The SKaR architecture is shown on Figure 8. Only critical equipments of the payload have redundancy, that is the HPA (cold redundancy) and the PDHT (hot redundancy for Mass memory and transmitter). However, this is believed acceptable approach for Earth Explorer Mission. A reliability rate of 0.75 is foreseen for the instrument, similarly as other scientific radar instrument.

The general architecture is similar than for SWIM instrument with adaptation of some elements to the Ka-band (LOs generation, frequency up/down converters, HPA, LNA, Duplexer, RF rotary joint, switch matrix, reflector, feed horns). The Core instrument Electronics Unit (CEU) will be a single unit based on the 6 slices of Poseïdon-4 DPU developed for Jason-CS/Sentinel-6 program plus and additional slice (see Figure 9). HPA and duplexer are stand-alone equipments.

Ka-band functions will benefit of the SARAL Altika (Ku/Ka module, duplexer, reflector) and SWOT KaRIn (EIK, duplexer) heritages.

Figure 8: SKaR functional block diagram.CEU is based on Poseïdon 4 (Sentinel-6 development, dark blue slices are re-used), HPA, Ka-Duplexer and switch matrix (inside RFA) are SWOT heritage. Critical parts of the antenna (RMA) are herited from SWIM.

The CEU is composed of 7 stacked slices interconnected via motherboard on top of the equipment and coaxial harness:

FORM_SEQ slice, that manages the altimeter sequencing, the science TM generation in CCSDS format, and high dat rate link with the PDHT.

DSP2 slice that implements the on-board software (running on a LEON2 processor) associated to different memories (boot PROM, SRAM, 16Mo MRAM memory for application software and DEM storage), and the MIL-BUS 1553 interface.

DCDC slice that generates towards all DPU slices the required regulated secondary voltages from the unregulated power bus, provides the current telemetry, and performs the thermistors telemetries acquisition (4 internal to DPU and 4 from RFU).

Rx_NUM slice, in charge of amplification of I and Q baseband received channels, I and Q analog to digital conversion, on-board digital echo processing (matched filtering, pulse pair, un-focussed SAR, …).

Tx_NUM slice, in charge of high-bandwidth digital chirp generation, high speed instrument sequencing signals generation, digital clocks generation (time base) from the 10 MHz USO.

The MOD_DEMOD slice that performs Ku-band (13.575 GHz) frequency generation (Local Oscillator implementation), direct modulation in Ku-band of Tx I/Q baseband signals and direct demodulation in baseband (I/Q) of Ku-band Rx signal (from Ka-band Rx chain).

The Tx/Rx Ku-Ka slice, in charge the translation to Ka-band of the Tx signals and to Ku-Band the received echoes. It includes the K-band (22 GHz) local oscillator, the Ka-band Low Noise Amplifier and the 10 MHz USO reference (OCXO).

The CEU interfaces with the platform are :

MIL-STD-1553 bus interface (Bus A&B): Telecommand (operating and uploading commands) and instrument Housekeeping Telemetry,

TC_RESTART command: reset of the instrument,

TOP 1 Hz (N&R): instrument synchronisation signal (datation),

10 MHz USO : DPU synchronisation (oscillators and clocks),

CEU current TM,

UPB: Unregulated Power Bus provided by the platform.

Figure 9: Poseidon 4 DPU CAD view.Tx/Rx Ku-Ka band slice will be added to form the SKaR CEU.

The duplexer as the same architecture than altimeter duplexer and consists of power ferrite switches in the transmit and receive paths to provide the required isolation in Tx and Rx modes. Transmit and receive paths are connected via a ferrite circulator which also provides a directional path to and from the antenna port. A switchable calibration path between the transmit and receive paths is provided using 50dB cross-guide couplers plus an additional ferrite switch, which also provides a third path to an external feed port (antenna calibration horn).

The antenna (see Figure 10) is a fixed offset reflector configuration with the eight feeds implemented on a rotating plateau. A switching matrix (based on seven ferrite switches) to transmit and receive RF signals in the appropriate direction (beam) is implemented in this rotating part (RFA). A single path RF joint performs the RF interface between the rotating part and the fixed part (duplexer antenna port). A motor ensures the rotation of the plateau and an optical coder provides continuously its azimuth position.

The design of the motor produces a small variation of the rotation speed during the rotation (<1.5%) but does not affect the instrument performances. Indeed, a micro-step motor (1/64° step) has been selected for SWIM to give the possibility to stop the rotation at a given position (commanded by ground TC). Continuous current motor could be used if necessary (TBC in phase B).

All TM/TC and power supply required by the switch matrix are transmitted by a DC rotary joint (slip rings). All the mechanism is led by ball bearings. Each horn transmits and receives linear polarization, radial (VV) or ortho-radial (HH) polarizations. There is no polarization loss in the concept.

The kinetic momentum produced by the concept is weak (< 0.4 Nms) and stable (< 0.0005 Nms). and does not affect platform AOCS.

The APCE, Antenna Power and Command Electronic, is divided in two equipments and installed inside the satellite payload module:

MMDE (Modular Mechanism Drive Equipement) which pilots the motor.

MSCE (Motor and Switch Command Electronic) which receives the command from the CEU to program the switch matrix and the motor via the MMDE.

A calibration horn, mounted on the structure below the reflector, enables to transmit and receive signals to all the horns on the rotating plateau for beam to beam in-flight gain calibration.

An active thermal control of the switch matrix is managed by the platform. To optimize the knowledge of the antenna pointing on-board, platform star trackers are accommodated on the antenna structure.

Figure 10: SWIM antenna principle reconducted for SKaR.Rotating parts are RFA and in the RMA (grey part fixed).

Degree of Innovative technology

Mainly two key technological areas are concerned :

Digital on-board processing inside the CEU

Antenna Rotary Mechanism Assembly

The on-board processing of received echoes (pulse compression, unfocussed SAR, averaging, …) is conventionnaly (SWIM, Poseïdon 4) implemented in European ASIC technology. While it provides robust and low power consuming solution adapted for wide equipment series, it remains a frozen application difficult to re-use in several radar configurations (bandwidth, swath, …).

For SKaR already developped ASICs can not be used. However, to overcome the long pre-development phase of ASIC solution, always risks generator (cost, planning, 2nd fundry run), a flexible FPGA based solution, as used for SWOT on-board processing by JPL, is considered.

At the present time, only the SIRF FPGA from Xilinx, already used in space programs has the capability and the technological readiness level required. This is the baseline solution considered.

Nevertheless, acceleration of European FPGA technology capabilities (NanoXplore) supported by ESA can be serenely envisaged to perform the SKaR on-board echo processing. A solution based on two Medium NanoXplore FPGA (prototype available) or one Large FPGA (prototype available in 2018) should be analysed.

Moreover, the Ultra large FPGA version (prototype available in 2019) would be an optimal solution since it may also integrate the functions of the FORM&SEQ slice and the instrument on-board software.

These evolutions of European FPGA contribute in the design of flexible digital future radar.

The antenna rotary mechanism can be significantly simplified using a mutli-path RF rotary joint (see Figure 11 ) instead of a single path rotary joint as for SWIM. The latter requires a slipring device to transmit power supply and commands to the switch matrix. One of the drivers in the design is the brushes used to transmit signals which are in contact with the gold plated tracks (rotating part). That design is qualified but it makes a complex design and development of the RMA+RFA assembly and limit performances of the system (reliability, rotation speed, …). On the other hand, the multi-path concept offers the possibility to combine the duplexer and switch matrix in a single equipment and to remove the costly and complex slipring device. The possibility to increase the rotation speed is interesting to improve the sampling of the swath particularly in the across-track direction between Nadir and mid-swath (see an example in Figure 4.3).

Therefore, it is worth to considering this trade-off during the early phases of the project and to initialize breadboarding activities.

Figure 11: Simplified synoptics of RFA & RMA. Left schematic corresponds to the configuration with a single path RF rotary joint. Switch matrix is the rotating part. Right schematic shows a multi-path rotary joint with a combined duplexer and switch matrix in the non rotating part.

Figure 12: Alternative SKIM observation geometry with 6 beams at 11 rpm. Blue=Nadir beams – Green =6° beams – Red / Yellow / Cyan / Magenta=12° beams.

Measurement techniques and on-board processing

Different measurement techniques are used. Some data processing is done on-board to reduce the data rate. A general and preliminary overview is shown on Figure 13.

Figure 13: SKaR on-board processing block diagram for the baseline (top) and additional Delta-K (bottom)

Range pulse compression (2048 points) is done on-board with correction of range migration for the off-Nadir Beam. This is already implemented on SWIM. The range processing is done at 250 MHz frequency giving a range gate of 60 cm (signal bandwidth oversampling by a factor 1.25). The Chirp scaling algorithm (compression and registration) is improved compared to SWIM by taking into account the Earth rotation effects leading to a better range resolution after pulse to pulse averaging.

The Nadir beam is processed like an un-focussed SAR altimeter (see Sentinel 6 Poseidon 4). Complex data after Range Migration Correction are transmitted in the telemetry. This enables to provide equivalent raw data. Processing is done by burst of 32 pulses and the 16 Doppler bins corresponding to the 3-dB azimuth antenna lobe are kept. The 32 bursts of the cycle are down-loaded in the telemetry.

Sea wave spectrum measurement (off-nadir beams) - Scattering modulation spectrum.

It is obtained from the spectrum of the radar backscattering modulation range profiles in all azimuth directions. Surface scattering modulations are produced by the different ocean waves (wind waves, swells, …). On-board pre-processing (following what is done on SWIM) is applied to obtain mean radar cross-section profiles. The on-board processing consists after range pulse compression in power detection (RAR mode), range selection (3-dB antenna main lobe), temporal summation of all the received echoes within the cycle (NIMP=1024). The finest range resolution (60 cm) is kept. Reflectivity modulation and waves height/slope spectrums will be obtained by on-ground processing. Possibility to perform this processing on-board will be analysed in phase A (processing complexity trade-off).

Un-focussed SAR processing for off-Nadir beams. It is planned to have also un-focussed SAR processing for off-Nadir beams to obtain 2D Waves height/slope spectrum. Compared to the Nadir beam processing, additional processing (bin stacking from burst to burst) will be implemented to reduce the data rate. Therefore, for each cycle 16 power Doppler beams covering the antenna footprint of the cycle with the finest range resolution will be provided.

Surface kinematics (Doppler velocity anomaly)- Pulse pair processing for all beams.

Pulse pair processing is a well-known technique (Zrnic, 1977) used in meteorological radar and implemented in the Cloud Profiling Radar of EarthCare mission. This method can be also used for ocean current measurement (Rodriguez et al., 2014). Amplitude and phase measurement of the mean radar signal are obtained by forming the cross-correlation product of two succesive echoes (see equation PP.1).

The measured phase is proportional to the velocity of the illuminated scatterers (see equations PP.1 & PP.2 in section 4.2.2.2) because the autocorrelation function is the inverse Fourier transform of signal spectral power density (see ). As discussed previously, main concern is to ensure a good correlation between the two echoes cross-correlated whose coherency depends on the Doppler spread within the illuminated area (RAR mode). The Doppler bandwidth is around 15 kHz (maximum at 90° azimuth angle) and a PRF of 32 kHz is selected to have an adequate sampling.

The measured amplitude is proportional to the mean signal power weighted by the correlation coefficient.

The on-board processing consists (after range pulse compression) in forming the cross-correlation product of two successive echoes and performing coherent integration of the complex samples over 32 ms for each range gate (on 512 pairs). In the telemetry the complex signal (I,Q) will be downloaded at the finest range resolution so averaging over the beam and Doppler spatial spectrum processing could be dne on-ground.

Surface kinematics (Dispersion relationship anomaly) – Delta-k processing for off-nadir beams. This technique is also well-known and used by coastal HF radar. It can be emulated by cross-correlating echoes at two different frequencies (Alpers and Hasselmann, 1978) and spectral analysis over several successive echoes give a sharp peak superposed on a broadband speckle noise. The peak of the spectrum is due to the movement of the gravity wave (orbital velocity) of wavelength λw = c/(2Δf sin θi), where f is the frequency shift between the processed echo and θi the incidence angle, and to the surface current. Thanks to the spectral bandwidth of the received echo (200 MHz), range profiles for different narrow bandwidth spaced by f can be obtained (see Figure 13, bottom). The spacing can be configured from ground via Radar Data Base parameters. For instance, separation by 180 MHz corresponds to a sea wave wavelength of 4 m (12° beam) and 8 m (6° beam). An intermediate frequency can be selected to obtain another wavelength.

Range pulse compression for the two frequencies and their cross-correlation is done on-board to reduce the data rate. However, all the 1024 cross-correlation products of the cycle (averaged in range) are included in the telemetry to allow different kinds of on-ground processing.

All these on-board processing algorithms are quite simple and well known (SWIM, Jason-CS/Sentinel-6, SWOT). The real time processing is quite short (PRF = 32 kHz) but the number of FFT points to be managed is quite low compared to what it is done on Jason-CS/Sentinel-6 for instance (10 kHz, 20k points pulse compression, RMC). Hardware implementation of this processing has been discussed in section 5.2.2.4.

Figure 14: Interface between main instrument data and waves/current products.

Instrument performances

Table 9 gives single shot signal to noise ratio in the line of sight (at middle swath). At the edge of the swath SNR is 6 dB lower. The Vandemark model (is used for sigma naught hypothesis (68% percentile). Mean transmitted power by the EIK (75 W) has been adjusted to get the necessary SNR of off-nadir beams (capability of EIK is 150 W). Transmission losses of 2 dB are considered, so transmitted mean power at antenna feeds is 47 W. SNRs are computed for Sentinel 1 orbit mean altitude considering, at antenna boresight and considering a 1.5 dB system margin. Antenna gain is computed considering 50% of equivalent illumination efficiency. The instrument receiver noise temperature at antenna port is 1045°K (1 dB improvement could be achieved with an on-going AsGa LNA run, will be measured by the end of 2017). Atmospheric losses of 3 dB at nadir are considered.

Beams	Nadir	6°	12°
Sigma naught (dB)	11	9.9	6.6
SNR RAR (dB)	33	14	7.6
SNR SAR (dB)	43	17	10

Table 2: Single shot Signal to Noise Ratio in the Line of Sight.

The nadir beam SNR is very good and will provides excellent altimetric performances (Figure 4.5) similar to Jason-CS/Sentinel 6 altimeter with an addition no Doppler signal aliasing thanks to the high PRF used.

Sea wave modulation spectrum performances.

They depend on the radar capability to detect weak reflectivity modulation and the lowest sea wave wavelength, down to 20 m (SKIM objective).

The reflectivity modulation can be as low as 0.2 dB. So a good radiometric resolution is necessary, objective lower than 0.1 dB. The following relation gives the instrument noise where N is the number of independent samples and SNR the single shot Signal to Noise Ratio:

(SWS.1)

Considering the SKIM definition, Kp = 0.1 dB requires integration of around 2000 independent samples. This will be done by pulse to pulse incoherent averaging and some range averaging.

The minimum sea wave wavelength that SKIM can measured depends on the range range resolution used to compute the spectrum and the accuracy of the range migration correction performed by the on-board processing (chirp scaling algorithm):

(SWS.2)

Chirp scaling algorithm has been extensively simulated and validated for the SWIM program. For the 12° beam, a worst case residual correction error lower than 2.5 m is achievable. The range resolution being 3.6 m, the minimum detectable sea wave wavelength is 12 m.

Pulse pair – Radial velocity measurement

The contribution of the surface velocity to the radial velocity measured by the radar is given by the phase of the pulse pair processing, as detailed in 4.2.2.2. The Doppler of the stationary surface (UNG : satellite motion + Earth rotation + orbit vertical velocity) must be substracted (main contribution). The general equation is the following:

where g is the off-Nadir angle and a the squint angle. Other parameters are for orbit characteristics and satellite position on the orbit.

The main contributor (first term) is due to satellite motion and produce a significant bias depending on the azimuth position of the beam during the antenna rotation. Antenna pointing shall be known with an accuracy of (g =0.3 arcsec – a=1.5 arcsec) to ensure a worst case error of 1 cm/s.

The second term corresponds to Earth rotation effect and requires an antenna pointing knowledge 15 times lower. It should be possible to inferred most of this contribution with antenna pointing knowledge from AOCS.

The last contribution is the vertical velocity due to orbit dive angle and can be easily inferred with antenna pointing knowledge from AOCS.

Preliminary simulations (see section 4.2.2.2) have shown that the restitution of this non geophysical velocity is feasible by retracking the data over one or several antenna scan cyles. The theoretical accuracy (Cramer Rao bounds) depends on the individual measurement noise, the number of beams per rotation cycle (linked to the rotation speed) and the number of rotation considered.

The data will be re-tracked after removal of the velocity bias induced by waves (UWB) which is estimated by independent measurements. So, remaining perturbators are the current, instrument noise and error on UWB estimation. Simulations have shown that the instrument noise contribution is weak (<1 cm/s) and the effect of the current acceptable (1 cm/s). This requires a satellite pointing stability of 2 mdeg over 20 s (if one antenna scan is used), feasible with standard AOCS equipments (star trackers, reaction wheel, magnetorquer bar), see analysis in section 5.2.3.2.

Deeper analyses shall be performed to consolidate the re-tracking methodology, the requirements on attitude control and to take into account other contributors (antenna beam pointing behaviour during the rotation for instance).

Instrument data rate

A preliminary analysis of the instrument data rate is expressed in table 3 below. All samples downloaded are 16 bits coded. Due to the antenna rotation 20% un-focussed SAR data of off-Nadir beams are not downloaded (Doppler lines parallel to range lines). For the nadir beam, 64 range gates are downloaded after the Range Migration Correction (raw SAR data).

Beams	Nadir	6° (2 beams)	12° (5 beams)
SAR RMC data (Nadir)	512 x 64 x 16 bits	-	-
Un-focussed SAR (off-Nadir)	-	16 x 1500 x 16 bits	16 x 2048 x 16 bits
Pulse Pair echoes (I,Q)	2 x 128 x 16 bits	2 x 1500 x 16 bits	2 x 2048 x 16 bits
Averaged echoes (power)	128 x 16 bits	1500 x 16 bits	2048 x 16 bits
Delta-k data	-	6 x 1024 x 16 bits	6 x 1024 x 16 bits
Amount of data / cycle	530 kbits	368 kbits	596 kbits
Amount of data / macro-cycle	4.2 Mbits (80 % SAR data 6°-12°)
Data rate	14.4 Mbits/s < 20 Mbits/s (overhead)
Amount of data / orbit	120 Gbits (100 mn)

Table 3: Instrument data rate and volume (per orbit)

Assuming the possibility to download on one ground station per orbit (Svalbard for instance) we can expect at least 9 mn of visibility. Therefore, the required data rate of the PDHT is 220 Mbits/s, lower than the capacity of the X-band system designed for the Sentinel satellites.

X-band PDHT

A redounded PDHT architecture is considered (see Figure 15), built around units that are either already developed or under development in the frame of other programs.

The Solid State Recorder (SSR) ensures the following functions :

Instrument data reception with 50 Mbits/s link;

Data storage into a 1-Tbit flash memory plan, managed according to Packet Store management services;

Data formatting and coding (RS);

Transmitter interface up to 300 Mbits/s.

An OQPSK transmitter with integrated 8 W SSPA and Viterbi coding is well-sized for the mission.

Several possibilities are available for the X-band antenna among them Sentinel-1, 2 & 3 satellites ‘”Iso Flux” antenna. However, a more compact helix antenna is also well adapted for non-agile mission and provide optimized gain pattern for LEO orbit:

http://www.ruag.com/fileadmin/ruag/Divisions/Space/Products/Satellite_Communication_Equipment/Antennas/PDF/X-band_Helix_Antenna.pdf considered here.

Figure 15: PDHT sub-system block diagram.Heritage from previous and on-going programs.

payload budget

Payload interfaces are detailed in Table 4. Instrument budgets are based on the strong heritage of this kind of equipments.

Duplexer and switch matrix power consumption has been increased according to higher PRF used for this instrument. The HPA power consumption is based on an efficiency of 30%.

Antenna structure has been scaled from the SWIM antenna structure with additionnal mass margin to take into account a wider definition of the mechanical environment.

CEU budget corresponds to the previous call configuration and is then conservative (1 slice shall be removed, power consumption to be refine with accurate measurement on Poseidon 4 EQM in 2018).

Equipment	Power (W)	Dissip. (W)	Mass (kg)	Volume (mm3) L x W x H
CEU	89	89	13	300 x 326 x 256
HPA	250	175	2 x 16	See Figure 16
Duplexeur	15.6	15.6	2.5	230 x 200 x 100
MMDE	20	15	3.1	194x189x142
MSCE	27	27	1.2	195x75x163
APCE Harness	-	-	0.8	-
Antenna	12 (heaters)	12	60 (nom.) 70 kg (with margin for mechanical environment)	1652x1200x2187 see Figure 16
Instrument harness	-	-	3.0	-
Instrument total	414 W	339 W	< 130 kg 56 kg (electronics) + 70 kg (antenna)
PDHT - SSR - Transmitter - Antenna	54 (Tx) 4 50 -	46 (Tx) 4 42 -	11 (with miscellaneous) 3 1.5 0.4	211 x 132 x 143 160 x 115 x 46 90 x 240

Table 4: Payload Mass-Power-Volume budgets

Figure 16: HPA (left) – Antenna (right) preliminary views.

Figure 17: Compact PDHT antenna (left) and transmitter (right)

Technological readiness level

The level of technological maturity of the SKaR (TRL) has been evaluated considering the Thales Alenia Space relevant heritage on space programs, that is:

SWIM instrument covering the overall SkaR concept and the antenna: SWIM is now qualified and the instrument PFM (antenna+electronics) tested, ready for DRB (TRL8).

Figure 18: SWIM antenna PFM (left) after tests (left) and during mounting on the flight panel (right). All SWIM electronic equipments are installed in the interior side of the flight pannel supporting the antenna. The white frame is a structure for ground operations and tests at instrument level.

Poseidon 4 (Sentinel-6) development (on-going phase C/D, CDR end 2016) for the CEU: slices EM are manufactured and tested on the BBSA (BreadBoard Simulator Antenna) and a full equipment EQM is under manufacturing. EQM tests are planned beginning 2018 (TRL7).

Figure 19: Poseidon 4 BBSA under test. The BBSA includes EM slices (FORM&Seq, Control Unit+1553, DCDC, Motherboard) and a fully representative breadboard for the Tx/Rx functions.

SWOT program for HPA, duplexer and switch matrix. The EIK as well as the critical functions of the duplexer have been qualified (TRL7). The Duplexer EQM is under manufacturing for tests planned end 2017 (PFM delivery in 2018).

One of the key instrument component is the rotating antenna system qualified in the frame of the SWIM program. In particular, strong efforts has been done to qualify an European combined RF/DC rotary joints. Translation to Ka-band of some elements is required but strong space heritage is already available in Europe (past and on-going programs). RF rotary joint, already used for on-ground radar and space Ka-band telemetry is TRL 5. The switch matrix is a proven equipment design and critical ferrite switch elements are qualified for high power handling (SWOT program). Minor changes to adapt to high PRF the switch matrix are required. Therefore, a TRL of 5 is considered. Same considerations are valid for the duplexer.

As discussed previously, there is a possibility to consider multi-path RF rotary joint. This will transfer the switch matrix to the fixed part (merge with duplexer) and remove the DC joint (slip rings). At present time, multi-path RF rotary joint is TRL 2. However, enough time is available for bread-boarding activities to reach a TRL5 before end of phase B1.

Instrument-central electronics architecture relies on the altimeter product line developed for ESA (baseband to/from RF direct modulation/demodulation and digital pulse compression) and Ka-band altimetry programs. No ASIC will need to be developped. All the on-board echo processing will be implemented in available qualified FPGA technology. This processing needs to be detailed and specified to initiate simulation and breadboard validation activities during phase A. All the elements of the CEU have then TRL higher than 5 (see details in Table 5).

CEU slices	Heritage	TRL	Comments
FORM & Seq	Poseidon 4	6 7 (2018)	Use of a high data rate parallel link (SWIM) to replace SpaceWire ASIC The sequencer software embedded in the FPGA will be adapted to the mission.
Control Unit + 1553 (DSP2 slice)	Poseidon 4	6 7 (2018)	Re-use The on-board software will be adapted to the mission.
DCDC	Poseidon 4 or KaRIn	7	Re-use of generic DCDC slices
Tx_Num (TimeBase & chirp)	Poseidon 4	5 7 (2018)	Re-use ASIC qualified
Rx_Num	Poseidon 4 SWIM	5	Re-use of analogic section Processing to be designed in 2 FPGAs (see explanation above)
Mod/Demod	Poseidon 4	6 7 (2018)	Re-use
Tx-Rx Ku/Ka	AltiKa / KaRIn	7	Re-use of Ku/Ka of AltiKa and KaRin components (mixer, K-band LO, LNA, …)

Table 5: SKaR CEU TRL definition

The High Power Amplification sub-system includes the EIK amplifier developed by CPI Canada for the Canadian Space Agency in the frame of SWOT program. JPL is developing the HV-PSU (EPC) and is responsible for the HPA. Well identified European suppliers for the manufacturing of this equipement and the HPA integration exist (CPR HPA of the EarthCare program, CoreH20 EM development, SWIM HPA, …).

Equipment	TRL	Comments
CEU	> 6 > 5	Poseïdon 4 (S6) – AltiKa-KaRIn heritage TRL5 for the on-board processing
Antenna	> 7 > 5	SWIM heritage + Ka-band programs RF rotary joint and switch matrix in Ka-band
MSCE	7	SWIM heritage
MMDE	9	TAS-B standard product
EIK	7	KaRIn SWOT EIK
HV-PSU	> 5	EarthCare, CoreH20 European experience
Duplexer	> 5	AltiKa and KaRIn SWOT heritage
PDHT	> 7	New generation will be qualified in 2017 Some elements TRL 8 and 9

Table 6: SKaR TRL definition as of June 2017.

5.2.3. Satellite architecture

The satellite analysis has been done considering TAS heritage to demonstrate the viability of the mission on an existing low-cost small LEO platform. It is built around the Proteus150 PF, which is the TAS platform developed in the frame of Myriad Evolution development activities (MYREV).

5.2.3.1. Spacecraft configuration

The overall configuration of the S/C is dictated by the implementation of the antenna while reusing a recurring, low-cost, Proteus150 platform. The main non-recurring elements consist in a reinforced structure and the addition of a third solar array wing.

At launch: The SKIM satellite offers compliant interfaces with VEGA launcher (see following table): Launcher Position compliance Comments VEGA Lower No Under VESPA upper Yes Upper VESPA Table 7: SKIM launcher compatibility Special attention has been paid to the satellite overall height to be compliant with VEGA dual launch configurations in upper position. The current orientation of the antenna (mounted on +Xsat) offers a good balance of the launch loads on the platform structure and is compatible with the launcher volume. This orientation also allows for adding a third wing, in order to comply with the power need of the payload.	Figure 20: SKIM in VEGA launcher fairing.

In orbit:

In order to fulfill the mission, the external accommodation was checked for non-obstruction of the field-of-views of the equipments (STR / TTC antenna / CSS). To minimize the thermo-elastic drift between the antenna and the STR, the later is accommodated directly on the antenna-supporting structure.

Figure 21: S/C in deployed and stowed configuration.

The S/C is composed of two modules, hosting separately the PF units and the PL units.

Figure 22 shows the internal accommodation. The PF units (labeled in blue) are recurring units. The PL units (PL in orange) are all accommodated in a dedicated module, located on the +Xsat panel (support of the antenna) and all the dissipative units have a dedicated thermal surface. This preliminary accommodation offers a good compromise between balancing and modularity.

Figure 22: SKIM internal accommodation (PL and PF).

AOCS Subsystem outline

The Proteus 150 platform taken as baseline in this study is assumed to provide the SKIM instrument with a stable and accurate nadir pointing. The assumed orbit is a Sun-Synchronous Orbit (SSO) at 6h between 500km and 800km, implying low needs in manoeuvres for sun-pointing and the absence of eclipses for almost 9 months over one year. The nominal performances of the platform can be summarized as follows:

Absolute Performance Error (APE)	0.02° (3σ) on each axis
Relative Pointing Error (RPE)	7e-4 °/s (3σ) over the frequencies from 0.1Hz to 1Hz
Attitude Determination	0.0025° (3σ) on each axis
Orbit Determination	30m in 3s (3σ) in position 30cm/s in 3s (3σ) in speed
Mean Agility	0.3°/s in attitude 1mm/s to 2.5m/s in position

Table 8: Nominal performance of the Proteus150 platform

At the actuator level, the momentum induced by the rotating part of the instrument reaches 0.4Nms. This disturbance torque will be directly handled by the four Reaction Wheels (RWs) on-board the platform, with each of them having a momentum of 4Nms. Therefore, this additional disturbance will not degrade the current absolute pointing performances. In addition, this extra actuation capability is only reinforced by the moderate inertias of the platform thanks to the use of relatively small solar panels of 2m x 1m. The reduction in the compensation of the gravity gradient or of the solar radiation pressure is then greater than the 0.4Nms induced by the instrument. In addition, the expected torque induced by the rotating sensor is also a known disturbance that can be modelled and used into a feedforward controller to reduce the impact on the platform pointing error.

At the sensor level, the initial platform is equipped with only one Star Tracker (STR), such that the use of a double head allows to increase the attitude determination. A more thorough study would be necessary to evaluate this gain, but one may consider a nominal error of 16 arcsec (3σ) in the X-Y plane of the STR, and of 100 arcsec (3σ) around the line-of-sight axis Z. This error at the sensor level is then reduced by advanced filtering techniques to reach the accuracy presented above. A last main advantage of the current payload is that the star trackers are directly mounted on the instrument, reducing thus the mechanical offsets due to multiple interfaces, and most of all, the thermal deformation during the orbit. This feature will allow to provide a measurement closer to the actual pointing direction by reducing the intermediate disturbances.

In conclusion, the use of the Proteus150 platform seems well suited to the instrument needs in terms of pointing requirements and accuracy. The baseline accuracies are in-line with the instrument needs while the disturbance compensation remains within the actual actuators capabilities.

Thermal architecture

With the standard objective to guarantee the units thermal environment while minimizing the consumed power, the SKIM thermal control is based on flight-proven technology. The following main design drivers/requirements have been identified for the thermal control:

The thermal control is designed to maintain all equipment, located in the spacecraft, within their specified temperature ranges during all expected mission phases and modes, and for the 5 to 7 years specified lifetime duration.

The thermal control takes into account the orbital environment induced by:

The Sun Synchronous Orbit at local-time descending node at 7h.

The satellite attitude with Earth on Z direction.

The satellite accommodation, with 3 solar-arrays wings, the antenna on the upper side, and the propulsion thrusters on the bottom side, is a major driver for the definition of the thermal control.

The thermal control is designed to minimize power consumption and mass.

The thermal control is achieved using only classical flight-proven technologies (processes and hardware), fully compliant with the TRL requirement of at least 5-6. It is based on passive cooling.

To comply with these requirements and constraints, the following thermal-design rules are proposed:

The antenna is, as far as possible, thermally decoupled from the platform.

Due to the Solar illumination and the earth pointing, the dissipative units need to be conductively cooled and should be preferable located on the lateral panels of the payload module with dedicated thermal radiators (SSM).

This standard thermal concept proposed for SKIM payload is illustrated in Figure 23.

Figure 23: SKIM payload thermal concept.

Mass and power Budgets

Mass and power budget considered for SKIM satellite assessment are detailed in tables hereafter. Standard margin philosophy have been considered.

Table 9: Satellite mass budget.

To be compatible with the power budget of the S/C presented in the following table, the power distribution by the SA is of 1050W ( 3 wings at 350W each EoL).

Table 10: Satellite power budget.

Mechanical analyses

This part was only transmitted to ESA.

5.2.3.6. Platform Technological Readiness Level

This part was only transmitted to ESA.

5.3. Ground segment

5.3.1. Overview of the architecture

Following the approach of developing a multi-mission ground segment, the current generation of Earth Explorer ground segments has been designed, built and integrated using generic components configured or adapted to each satellite. In this line, the envisioned SKIM ground segment consists of three main components: the Flight Operation Segment (FOS), the Payload Data Ground Segment (PDGS) and the Mission Performance Center (MPC). Some re-use of part of the CFOSAT ATBD may be possible.

Figure 24: Overview of the SKIM ground segment

The ground segment architecture should greatly benefit from experience inherited from Sentinel, with a PDGS for the processing of the data and a MPC for all products analysis, CAL/VAL and improvements of the algorithms. Existing validated elements from the Sentinel program should be re-used as far as possible to minimize risks and costs, and allow for a shorter development schedule.

5.3.1.1. FLIGHT OPERATION SEGMENT (FOS)

The FOS (Flight Operation Segment) is constituted of a control center, a S-band station and X-band polar stations. The S-band station is used for the TC upload and the download of housekeeping TM. The X-band is for the download of the science data. The FOS includes the preparation of the TC plan with the mission planning coming from the PDGS and the orbit monitoring. It analyses the housekeeping telemetry for real time monitoring of the instrument.

The FOS includes the TT&C ground stations and the Flight Operations Control Center (FOCC), and is responsible for:

Housekeeping telemetry acquisition,

TC plan upload,

Satellite tracking,

Operation and health follow-up of the spacecraft and the sensors from the S-band telemetry,

Provision of ephemeris, attitude and instrument mode data.

As for other ESA missions, the FOS center is located at ESOC, Darmstadt.

No specific activities are required to the FOS. It can be fully re-used from other missions. Operations support is restricted to normal working hours, i.e. five days a week. The instrument has to be designed to be able to stay automatically in a safe mode waiting TC from the ground.

5.3.1.2. PAYLOAD DATA GROUND SEGMENT (PDGS)

The PDGS is responsible for

The mission planning that will be ingested in the TC plan: SKIM acquires data continuously. Nonetheless, calibrations are required on regular basis (typically three times every day, as altimeters). In the routine phase, the TC plan can be defined as a component of the background mission plan as it is not expected to change. However, the MPC can suggest modifications for specific CAL/VAL activities (especially during the commissioning phase).

The processing of the science data and then the distribution and archiving through a standardized ESA web interface.

In case of expression of interest from meteorological agencies, a partnership with EUMETSAT could be discussed to access the EUMETCAST facility.

All the components can be re-used from other missions except the processing chain which will obviously be specific. In order to reduce risks and costs, the ground segment orchestrator should be generic and then based on the one of other ESA missions. The archiving and distribution are also based on generic ESA components. In particular several streams of ancillary data are necessary for flagging and processing some of the L1 data into L2. These include

ECMWF analyses and short-range forecasts for water vapor (range correction on nadir beam), wind vectors and wave heights.

spectral wave model output (e.g. full spectra from ECMWF WAM or Meteo-France WAM and Ifremer WAVEWATCH III).

OSI-SAF ice mask

A main driver of the algorithm chain development must be flexibility. As SKIM is highly innovative, algorithms should evolve easily. The algorithm chain is thus planned to be modular with clear interfaces between the steps. In this case, evolutions remain feasible with minimized impact on the development.

The algorithms are quite demanding on computer CPU (mostly for L1A). Thus an architecture based on a cluster is recommending. The algorithm development has to be built on up-to-date parallel computation technologies. In addition, the PDGS will take into account the demand of regular reprocessing (on a basis of one reprocessing per year).

5.3.1.3. MISSION PERFORMANCE CENTER (MPC)

The MPC is an expertise center for:

Commissioning activities with respect to the instrument and the products,

CAL/VAL activities (including CAL/VAL plan design) on the products,

Data circulation through dedicated network

Proposition of improvements of the processing algorithm and instrument configuration,

Planning of the instrument (definition of specific TC plan, new definition of routine plan).

The organisation of MPC activities will rely on Sentinel-1 and Sentinel-3 (wave, altimetry, current) experience. MPC activities are spread into several centers. These are the Expert Support Laboratories (ESL). The role of each ESL is defined with respect to the mission/product requirements and specific with respect to their expertise. Their activities are coordinated by the MPC leader.

Further, Globwave (http://globwave.ifremer.fr) and GlobCurrent (http://www.globcurrent.org) initiatives at ESA provide a basis for SKIM CAL/VAL activities. In particular, these project enabled to identify, gather, format, qualify and disseminate in-situ data and other satellite missions for cross validation purposes. Higher level products (Level-4 multi-missions products from Wind & Wave TAC of the Copernicus Marine Environment Monitoring Service for instance) will also useful for validation.

The different ESL will have close links so as to exchange their results and their proposals for improvement n a dynamic way. A MPC platform has to be developed to foster collaborative development. The validation database has to evolve to take account of missions and the latest available products. The Ifremer/LOPS database is a good example of a starting point that can certainly be enriched specifically for SKIM. This database gathers all satellite, numerical simulations and in-situ data relative to the topics. It also enables massive and/or interactive co-locations to have reference metrics and contextual observations for each SKIM acquisitions.

5.4. Mission analysis and operational concept

It is planned to operate SKIM continuously. This includes acquisition over continents that will be used at least for monitoring the backscatter, and that will probably find novel applications (e.g. estimating river flow, traffic, etc). As a result the mission planning is very simple, and station keeping manoeuvres should be kept to a minimum, within the constraint of keeping the ground track.

The nominal orbit is the Sentinel 1C orbit (assumed identical to 1A), shifted 4° to the east. Given their different incidence angles, this allows for a maximal overlap for ascending tracks with IW mode data for CAL/VAL purposes (Figure 25). It is expected that the orbit plane could be shifted further after a 6 months CAL/VAL phase, in order to maximize the joint coverage of the two missions. This will have to be compared to the benefits of keeping the same track and reducing nadir altimeter range errors.

Figure 25: Typical coverage of the SKIM swath. The left panel includes actual coverages of Sentinel 1 in EW (green) and IW modes (red). The right panel is a zoom on the German bight (using the SKIM1 configuration).

Every six months, an in-deep calibration of SWIM is recommended: calibration of AGC steps (specific CALIBRATION 1 mode), calibration of any drift of gain in the swath (CALIBRATION 2 mode), etc. The instrument parameters are then updated and uploaded to the satellite if required.

6. Programmatic elements

6.1. Instrument outline Design, Development and Verification plan

This part was only transmitted to ESA.

6.2. Satellite development plan

The overall schedule for SKIM assumes Phase A commencing in June 2018, with a standard 12 months being allocated to this phase. This is followed by a 9-month Phase B1 which shall conclude with the System Requirement Review (SRR).

Phase B2 will then kick off in March 2020 and will conclude with the Satellite Preliminary Design Review (PDR) closure after its review board. During this phase activities to procure platform equipment and hardware will begin.

The Phase C will start after the PDR closure, and will end with the Satellite Critical Design Review (CDR) closure. This Satellite CDR will be preceded by the instrument CDR by approximately 6 months, allowing refinement of the satellite design according to the instrument CDR. During this phase the platform structure and propulsion subsystems will be submitted to Test Review Boards. Although most elements of the platform are recurrent from TAS’s Proteus150 platform line, the schedule accounts for the modified structure required for the SKIM satellite and the addition of a third solar array wing requiring the LV1 Xcheck to be performed later than on a standard Proteus150 PF.

Phase D will begin in September 2022 and is dedicated to the satellite assembly, integration, and testing with payload. The schedule considers a typical PFM test-sequence for the satellite AIT. The platform is expected to be ready for mating 6 months following the platform TRR, however a further 5 months of margin is included for platform delivery. The delivery of the instrument is set to be in November 2023, again the schedule allows some margin for this in case of instrument delays. Mating will take place no later than January 2024. Satellite testing will begin, with the flight version of SKIM being delivered once the results from the TVAC testing have been obtained and accounted for. Phase D will conclude with the Qualification Review and Acceptance Review. The Phase D is also dedicated to AIV of the Ground segment and its deployment on the Customer site, followed by dedicated Validation tests.

Phase E1 will commence with the shipment of SKIM and includes the launch campaign up to launch. The SKIM satellite will be ready for launch in May 2025.

Once in orbit , the phase E2 is the conducted for the In-Orbit Tests covering all the System (platform, instrument and Ground segment), up to the demonstration by the prime that all the performance requirements are met by the SKIM system. This phase E2 is concluded by In-Orbit Acceptance Review (IOAR) which is the milestone to transfer the property of the SKIM System to the operator for its operations during the design lifetime.

Phase B2	T0
Satellite PDR	13
Satellite CDR	31
Satellite TRR	50
Satellite QR	56
In-Orbit Acceptance Review	Launch + 3 months

Table 1: Timing of satellite development milestones

Figure 1: SKIM planning.

6.3. Space segment cost breakdown

This part was only transmitted to ESA.

Annex A : Changes compared with the 2016 SKIM proposal

A.1. Science objectives

Whereas the 2016 proposal was very much focused on ocean waves, the SKIM 2017 proposal is focused on surface velocity measurements, with a broader scientific community and two particular regions of interest on the tropical ocean and the marginal ice zones. Waves are still a required product, in particular for the wave bias correction, and provide many opportunities for ground-breaking science with the combination of currents, ice drift and waves.

A.2. Instrument design

This focus on velocities has led to a rethinking of the sampling strategy to obtain the finest effective resolution. This has led to an addition of 2 beams (one at 6° incidence, another at 12° incidence) and change in rotation speed of the horn plate. We now propose two acquisition modes (closed and open bursts) with different cycle lengths. Both are compatible with the data rate and power constraints of the platform envisaged. They can be used for trade-offs on alternative platforms. As explained in section 5, other alternatives have also been explored (changes in rotating joint allowing faster rotation …) and their impact on mission performance have still to be evaluated.

Further analysis of the antenna vibration modes has also led to a reinforcement of the antenna structure to be compliant with the vibrations environment of the VEGA-C launcher. This has led to a 15% increase of the payload weight (S/C Max Mass including 10% system margin : 574 kg instead of 501 kg in the 2016 proposal).

A.3. Performance and error analysis: from SRL3 to SRL4 and 5

The 2016 proposal was prepared for the June 2016 deadline, from very preliminary phase 0 results. We agree with the evaluators that we only had a general feasibility statement of the SKIM concept, corresponding to a Scientific Readiness Level of 3.

In contrast, finalization of the phase 0 study at the end of 2016, and the involvement of a wider scientific community have led to a detailed error analysis of three identified error sources (Doppler centroid estimation, platform attitude misknowledge and wave-induced bias in the geophysical Doppler) and algorithms for correcting such errors to a level that meets the scientific requirements, as summarized in Ardhuin et al. (submitted).

The sensitivity of geophysical Doppler velocity to sea states and surface current has been demonstrated in Ka band and at SKIM-like incidence angles (4 to 20°) with airborne (AirSWOT, Nouguier et al., submitted, our figure 4.4) and platform-based data (Yurovsky et al. 2017a,b), consistent with the theoretical analysis of Nouguier et al. (submitted). The extended range of incidence angles treated further support the choice of a maximum incidence of 12° in spite of a moderate swath width of 270 km.

Key aspects (noise level, sampling errors …) have now been evaluated in the SKIM configuration (this was absent in the 2016 proposal) using three simulation tools:

- the SEAMOTION RADAR simulator which produces I/Q signals over several measurement cycles,

- the SKIMsimulator, developed like the SWOTsimulator (https://github.com/SWOTsimulator), that allows to use modeled ocean and ice properties for each measurement cycle and produce L3a, L3b maps of the SKIM measurements, including error estimates.

- OSSEs for ocean currents (using NERSC/TOPAZ system) and ocean waves (using MFWAM system).

These elements confirm that the Scientific Readiness Level of the main SKIM product, i.e. the total surface current vector, is at a level 4, and the rapid progress of the SKIM team in less than a year shows that SRL 5 is achievable for all SKIM products by the end of 2018.

Annex B: Mission performance

B.1. Methodology: science to level 2 product needs and level 1B requirements

The science needs have been well expressed in GCOS-200 for climate needs and Le Traon et al. (2015) for operational oceanography applications, and other documents specific to key regions such as Cravate et al. (2016) for the Tropical Pacific. These requirements have been particularly matured into level 2 products and variables as part of the GlobCurrent project (Globcurrent 2015). The translation into level 1B requirements is specific to the measurement technique. A key issue for all phase-based radar measurements (ATI, Doppler centroid …) is the estimation and correction of the wave-induced bias UWB using a wind-only proxy (Mouche et al. 2012, Martin and Gommenginger 2017) or estimates of UWB using a wider set of sea state parameters, which is our approach. This has the added benefit of allowing wider range of applications, and extending level 2 products from total surface current U only to U and surface Stokes drift US giving also the quasi-Eulerian velocity UE =U-US which varies on larger time scales, allowing for more robust level 3b gridded products.

The translation of these product needs into Level 1b requirements is thus specific to the SKIM mission, and has been accomplished by the SKIM team, starting from the CNES-funded “VASCO” phase 0 study. This has relied heavily on the development of a prototype end-to-end mission simulator, which provide all the necessary elements for a full end-to-end mission prototype simulator.

The following section B.2 present the principle of the simulator elements, up to level 3 (gridded products). The properties of the level 3 products are indeed the parameters that drive the mission requirements. This is followed in section B.3 by examples of quantitative output of the simulator that demonstrate the feasibility of mission objectives.

B.2. Simulation / performance modelling tools

Our end-to-end simulator prototype is based on a generic ocean scene + radar sensor simulator “Seamotion Radar” described in section B.2.2 in the specific context of SKIM, with details on the Doppler processing and analysis. “Seamotion Radar” is used to verify theoretical assumptions, in particular related to noise levels and level 1 products.

A SKIM-specific simulator called “SKIMsimulator” was developed in 2017 on the same principle as the “SWOTsimulator” (https://github.com/SWOTsimulator), and is designed to produce simulated level 3a (single swath) and 3b (multipe swath) gridded data for use by the SKIM team and the wider science and applications community. The purpose of SKIMsimulator is to allow future users to evaluate the expected quality (sampling, error, revisit …) of future SKIM data based on numerical current (+ ice) + wave model output, with various levels of measurement error estimates. The SKIM simulator has first been focused on surface velocity variables and will be distributed to the scientific community before the end of 2017.

The SKIM simulator uses output of a wide range of numerical models developed by the SKIM team

- ocean circulation + sea ice models like HYCOM+CICE, MITgcm+CICE, NEMO+LIM and ROMS

- spectral models like WAVEWATCH III and WAM (Roland & Ardhuin, 2014, Doble & Bidlot 2013)

- phase-resolving models such as X-BEACH (Roelvink et al. 2009) and SWASH (Zijlema et al. 2012)

- nonlinear « deep water » corrections for spectral wave models (Nouguier et al. 2009)

and the wider scientific community.

B.2.1. “SKIMSimulator”

B2.1.1. Level 2 ERROR MODEL

SKIMsimulator is built in python with few dependencies so that it is easy to install and run. Inputs for the simulator are NetCDF model files, containing quasi-Eulerian current vector (UE,VE), surface Stokes drift vector (US,VS) and coordinate variables. All parameters that may need to be tested are provided in a parameter file ‘params.py’.

The SKIM grids are saved separately in a NetCDF file so that the simulator needs to compute the grid only once, when the geometry is chosen (parameters orbit file, rotation speed, number, position and order of beams, cycle duration …). Grid files contains coordinates, times, radial angle, inclination of the nadir, along and across track distance to the nadir. Variables from the grids are read by the simulator to interpolate the model velocity on SKIM swath. Model characteristics are specified in the parameter file (type of model, name of variables, timestep and number of step to process, list of model files to process), to allow a wide range of numerical models as an input. The last section in the parameter file concerns the reconstruction of SKIM-like data with noise, and contains instrumental noise files and factor G, (US,VS) files, G and parameters regarding the reconstruction of the wave-induced bias UWB.

The outputs of the simulator are SKIM grids and SKIM-like data and are saved in NetCDF (one file per pass per satellite cycle). The projected radial true velocity UR, the instrumental noise, the measured US, the remaining bias on the estimation of uss, the observed velocity (sum of the radial true velocity and noise variables) are saved in the SKIM-like data netcdf files. Other variables used by the simulator are also saved such as the model file used for the interpolation, or the standard deviation of the US for the computation of the error budget.

Examples of NetCDF files given by this first step are available at

fftp://ftp.ifremer.fr/ifremer/ww3/HINDCAST/OTHER/SKIM/

B.2.1.2. Level 3 PROCESSING

The processing for Level 3a and 3b plugs in from the observed radial velocities of the SKIM simulator.

For Level 3a, the routine builds a grid over the SKIM 270km wide swath, regular in the along track (satellite ground track) and across-track direction. The pixel size is 5km by default, which is about 1/10th of the smallest wavelength targeted for the Level 3 products. Following the implementation described in Ubelmann et al. (submitted) and summarized in 4.2.2.5 , the inversion is performed on 300km long sub-blocs of the grid (to limit the number of observations under 1,000 for a reasonable matrix inversion time).

For Level 3b, the routine builds a regular grid in the zonal and meridional direction, with a pixel size of 0.05° by default in both directions. For high latitudes, the size in degree is reduced in the zonal direction to keep quasi-square cells in kilometers. The core of the inversion algorithm is the same as for Level 3a, except that observations come from multiple passes at different time. Following Ubelmann et al., sub-blocks are considered both in time (12 day window) and in space (300km in zonal and meridional directions).

Examples of Level 3a and 3b data files are available at:

ftp://ftp.ifremer.fr/ifremer/ww3/HINDCAST/OTHER/SKIM/

B.2.2 “SeaMotion Radar”

SeaMotion is a Scalian Alyotech software product dedicated to maritime operations simulations. It is a multispectral tool, able to produce visible and infrared (IR) sea scene images and sea surface radar IQ signal as well. “SeaMotion Radar” is the SeaMotion part dedicated to IQ signal generation.

Figure B.1: Visible / InfraRed / IQ Signal example outputs from SeaMotion

One of the key aspect of SeaMotion is its full GPU implementation, for both rendering (OpenGL) and general purpose computing (NVIDIA CUDA), leading to reduced computation times. This allows, on the one hand, interactive edition and preview for accurate scenario creation and, on the other hand, fast data production.

The main advantages of such a simulator are:

- Reduction of the number of measurement trials,

- Refined a posteriori analysis of these trials by simulation,

- Aid for the conception of sensors: test of a wide range of radar and environment configurations with full control over environment parameters.

B.2.2.1 Simulation scheme

Overview - SeaMotion Radar implements a physical model that aims to calculate and simulate sea clutter in a way that reproduces the signal properties encountered on real acquisition in known environmental conditions. The simulated signals are used to test signal processing employed in real radar systems and to show how performance varies in different environmental conditions. The following figure presents the simplified simulation scheme of SeaMotion Radar. It is divided in two main parts: the first one corresponds to environment and EM simulation while the second one deals with sensor simulation.

Figure B.2: SeaMotion Radar simulation scheme

Scope - SeaMotion Radar is dedicated to open seas simulation and covers X, Ku and Ka bands. It simulates coherent-pulse radar only and is based on the Stop & Go assumption: the scene (radar and sea) is considered static during pulse duration and is fully updated at the end of each pulse.

B.2.2.2 Environment and EM simulation

Two-scale model (TSM) - The approach for dealing with the radar backscattering process of the sea surface relies on a multi-scale modelling, which may be summed up as follows:

A “two-scale” electromagnetic (EM) modelling in which the small scale (SS), which is not resolved, is dealt with statistically. It is modelled by the Kirchhoff approximation.

The large scale (LS) is dealt with by an animated 3D representation of the surface with low spatial resolution (driven by the Kirchhoff approximation: ~1 meter for 35.75 GHz central frequency), in which the local slope is used to modulate or “tilt” the non-resolved small scale backscattering.

Figure B.3: Two-scale division of the Elfouhaily spectrum

Sea spectrum - The sea surface is generated from a 2D sea spectrum which consists in a wind sea spectrum and an additional (and optional) swell spectrum.

The 2D wind sea spectrum mainly depends on the wind vector (norm: U10; direction: φ) and the fetch and is the result of the combination of:

· An omnidirectional spectrum: Elfouhaily et al.

· A spreading function : Donelan, Longuet-Higgins or Elfouhaily

A swell spectrum can be added to the wind sea spectrum. Different swell spectrum types can be used (Sine, Gaussian, Durden-Vesecky) and the swell direction does not have to have the same direction as the wind sea spectrum.

Sea surface heights (resolved sea spectrum) - The spectrum approach in order to generate sea surface produces a 2D spatial pattern which can be repeated (without artifact creation) as much as desired. It allows the generation of “infinite” sea surfaces which is needed in order to cover the beam of a realistic radar. However this repeated pattern can produce statistical bias in generated IQ signal.

In order to minimize the negative side effects of this repetition, the resolved spectrum has been divided in two overlapping spectrums (Low Frequency and High Frequency):

Each spectrum contains 512x512 frequency samples,

The spatial step of the HF spectrum (ΔxHF) corresponds to the two-scale model cutoff (~1m for 35.75 GHz when using Kirchhoff approximation),

The spatial step of the LF spectrum (ΔxLF) is 16.ΔxHF,

The overlapping range of the two spectrum parts is handled with Hann windowing.

Figure B.4: Resolved sea spectrum cut

The 3D surface corresponds to the sum of the result of the inverse Fourier transform of LF and HF spectrums. The 3D surface spatial step corresponds to the two-scale model cutoff (~1m for 35.75 GHz when using Kirchhoff approximation):

The LF grid is interpolated (spline) at ΔxHF.

The HF grid is repeated (16x16) in the LF grid in order to generate a complete pattern.

The complete pattern size is : 16x512*1m = 8.192 m.

Figure B.5: Surface reconstruction example (height field)

Figure B.6: Surface reconstruction example (slopes)

Choppy transformation - In order to generate a weakly non-linear sea surface, the trochoids model (or Choppy wave model) is used. It consists in adding horizontal displacements to the IFFT generated height field. It results in sharper crests and larger troughs than linear model.

Figure B.7: Height field (left) vs Choppy wave model (right)

B.2.2.3 EM backscattering (unresolved sea spectrum)

The NRCS of the surface is obtained by interpolating in a table computed off-line.This table contains the NRCS of a surface element, at the prescribed resolution, as a function of local incidence and azimuth angles of the incoming EM wave. For near-nadir applications, the average NRCS of the rough surface element is estimated according to the physical optics (PO) approximation, which writes

Only the unresolved (sub-grid) waves, which contribute to the facet roughness, are accounted for in the elevation covariance.

For each sea surface element, the radar cross section (RCS) is computed according to following scheme:

- Facet surface and normal computing,

- Viewing angles in local coordinate system (azimuth, incidence) computing,

- Precomputed NRCS (normalized radar cross section) table (local incidence, local azimuth) interrogation,

- The final radar cross section corresponds to the product NRCS x surface.

B.2.2.4 Summary of simulated field

The following figure summarizes the different spectrum parts used in the simulator:

- SS : (unresolved) Short Scale spectrum,

- LS : (resolved) Large Scale spectrum:

- HF spectrum: ΔxHF =1m,

- LF spectrum: ΔxLF =16x1m.

Figure B.8: Spectrum use in SeaMotion Radar

For our SKIM simulations the accuracy of the simulation was tested against the analytical result of Nouguier et al. (submitted), in terms of backscatter and Doppler. A 12° incidence, For a wind of 7 m/s and a fully developed sea state defined by the spectrum of Elfouhaily et al. (1997) the simulator gives a Doppler centroid of 90 Hz compared to 140 Hz with the analytical solution. This difference is explained by the motion in the SS part which is not included in the simulator.

B.2.2.5 Sensor simulation

Scanning strategies - Different scanning strategies can be used in SeaMotion Radar:

- Fixed pointing angles ;

- StripMap (fixed pointing direction of the radar antenna broadside to the platform track);

- SpotLight (fixed aim point) ;

- Rotating antenna (with fixed depression angle).

Receiving channels - SeaMotion can handle one to four receiving channels with distinct (receiving) antenna patterns. A unique emitting antenna pattern is handled by the simulator.

Each 2D (site, azimuth) antenna pattern is defined by a 1D site profile and a 1D azimuth profile.

Sum in range cells - SeaMotion Radar generates IQ signal from the sea surface data. Each sea surface element carries its world coordinates position (X, Y, Z) and its RCS. The first step is to add each sea surface element in the corresponding range cell.

The range cell index computation is very straightforward and only depends on the distance between the sensor and the sea surface element. The sea surface contribution will be added to the range amplitude,

A = (RCS)0.5 / r2 ,

and phase

φ=φ0 – 4π r / λ,

where r is the distance between the sensor and the sea surface element, φ0 is the initial phase and λ is the EM wavelength. The distance attenuation is handled at this processing step in order to minimize numerical noise.

Radar equation- The radar equation transforms complex amplitudes A eiφ to complex voltages A' eiφ',

A' = P GT GR R λ2 A2 / (4 π)3 L ,

With:

Waveform - SeaMotion Radar generates range compressed raw data. The range compression process consists in convolving the complex voltage array with the compressed waveform. The complex waveform is the result of the convolution of the emitting chirp and its complex conjugate (autocorrelation). A weighting window (Hann, Hamming…) can be used in order to decrease the side lobes level.

SKIM-like IQ signal example:

Sea surface:

· Elfouhaily omnidirectional spectrum

· Donelan spreading function

· U10: 7m/s

· Fetch: 5000 km (fully developed sea)

· Significant Wave Height: 1.30 m

· Peak wavelength: 44.46 m

· No swell

· Sea surface spatial step: 0.84 m

Radar carrier:

· Altitude: 700 km

· Nadir spot speed: 6765 m/s

Radar sensor:

· Central frequency: 35.75 GHz

· Pulse Repetition Frequency: 32 kHz

· Bandwidth: 200 MHz

· Sampling step : 0.62 m

· 2274 range cells

· Compressed waveform with Hamming weighting window

· Rotating antenna with 12° incidence

· Uptrack and upwind configuration

· 1024 pulses (0.032 s)

IQ Signal - The following figures present the modulus of the generated IQ signal. Each line corresponds to one pulse IQ signal while a column corresponds to a range cell.

Figure B.9: IQ signal modulus example [dB]

Figure B.10: IQ signal modulus example [dB] (zoom)

Doppler - Doppler spectra can be computed on the complex IQ signal. The following figure shows Doppler spectrum computed on the first 280 range cells. Each line corresponds to the Doppler spectrum of a range cell. A range cell Doppler spectrum is the mean of 8 Doppler spectrums computed on 128-sample-long time blocks.

The ambiguous velocity in this scenario is 134.17 m/s. The radial velocity range in the following figure is [-67.09 m/s; +67.09 m/s].

Figure B.11: Doppler spectrum example (cells 1 to 280)

Doppler analysis on a range cell - Time-Frequency analysis can be performed on each range cell. The following figure presents Doppler spectrum (on 64-sample-long time blocks) over time for the cell 100.

Figure B.12: Doppler spectrum over time for a single range cell

In this example, the mean radial velocity is 38.5 m/s and Doppler shift over time cannot be highlighted with this method (and with this simulation configuration and duration).

Pulse pair processing - The following figures present the mean radial velocities for each range cell. The mean radial velocity is computed with Pulse Pair processing on the 512 independent pulse pairs (from the 1024-pulse-long IQ signal).

Two different curves are displayed on the graphs:

· Geometrical mean radial velocity (sensor along track displacement and beam rotation only)

· Mean radial velocity computed on the generated IQ signal.

Figure B.13: Mean radial velocities from Pulse Pair processing

Figure B.14: Mean radial velocities from Pulse Pair processing (cells 1 to 280)

First, we can see that geometrical and raw data mean radial velocities curves have the same slope. In addition, we can observe that the mean radial velocities goes from 35 m/s (cell 1) to 43 m/s (cell 280), that is similar to the mean radial velocities found with Doppler spectrum analysis.

For one 32 ms cycle, this leads to one estimate of the Doppler velocity for each sample in range range (2274 values in the considered configuration). Those values are then averaged to derive the final estimate of the Doppler velocity over one cycle.

Figure 23 shows the Doppler and RCS range profiles obtained over a cycle in a case of unrealistically coherent swell (for illustration purpose) combined with a wind sea.

Figure B.15: Doppler and RCS modulation as a function of range and time over a 32 ms cycle, in a case of strongly coherent swell.

An example of Doppler velocity estimated from the pulse pair algorithm is plotted in Figure 24 as a function of the incidence angle varying in the footprint. The modulation from the swell is superimposed over the geometric sin(θ) slope. The final velocity estimate is obtained by averaging over the footprint.

The quantity considered here is the “long term” average horizontal Doppler velocity, thus requiring all fluctuations, including the wave-induced ones, to be removed by averaging over the footprint. The error reduction factor brought by averaging can be expressed as Neff-1/2, Neff being an effective number of independent measurements. Due to correlations in range (obvious in the plot below), it is generally smaller than the number of range samples available in a footprint.

Figure B.16: modeled Doppler velocity (in m/s, shifted by Doppler ambiguity) as a function of incidence in a footprint

Surface current estimation - The following figures illustrate the process of estimating the surface current through the PP algorithm. To emphasize the contributions to the measured Doppler velocity, we take advantage of the ability offered by the software to generate signals with or without a surface current, with or without a moving sea surface.

The figures hereafter shows the detrended radial Doppler velocities estimated through PP as a function of the incidence angle in the swath. Four curves are plotted, corresponding to the following settings: 1) static surface, no current, 2) static surface, 0.2 m/s current, 3) moving surface, no current, 4) moving surface, 0.2 m/s current. The sea is fully developed under a 10 m/s wind. Observation geometry is upwind, along-track, with an incidence angle of 12°. Estimated Doppler velocities are computed by averaging over 1023 pairs in a cycle of 1024 pulses.

The first plot presents the measured radial velocities along the full swath, for static and moving surfaces. The offset between the corresponding curves result from the waves contribution to the Doppler bias (here about 0.4 m/s). The second plot shows a detail of the previous one, exhibiting the contribution of the surface current. It also clearly appears that the correlation length in the measurements is smaller when the surface is moving, as already stressed in chapter 4.

Note that, due to its low magnitude (only 4 cm/s in radial velocity), the current mainly acts like a deterministic shift in Doppler velocity relative to the values obtained with zero current, but with the same realization of the sea surface.

Figure B.17: estimated velocity as a function of the incidence angle (over a full cycle, i.e. 6 km). In black: static surface. In red: moving surface.

Figure B.18: estimated velocity as a function of the incidence angle. Zoom from the previous figure showing the contribution of the surface current.

The last figure below shows the histograms of the measured velocities in the four configurations, illustrating the relative magnitudes of the Doppler fluctuations, the waves and current contributions. The current radial component can simply be retrieved as the shift of the average velocity relative to the current-free configurations. This gives an excellent estimate, with a precision far beyond what is practically achievable, because it is based on a perfect a priori knowledge of the velocities that would be obtained with precisely the same sea surface, but with no current.

Figure B.19: histograms of the velocities estimated in the whole swath (2200 values).

B.3 Outline of algorithm to be used for geophysical retrievals

The SKIM products from level 0 and level 1 are organized into two types of products, those specific to surface velocity (total surface current U, quasi-Eulerian current UE which are derived from the Doppler centroid anomaly, and other products related to the wave spectrum E(k,θ) which are derived from the modulations in range r of the received power P(r) and Doppler velocity ULOS(r). The two types are linked, in particular by the surface Stokes drift US .

Figure B.20: Schematic of all products from level 0 to level 3.

The particular geometry of the SKIM measurements introduces an important distinction between Level 2 products (attached to each footprint or “cycle”) and thus corresponding to a particular azimuth θ (radial components) and Level 3 products, that combine all directions in the form of vectors or directional spectra E(k,θ) and their projections, and that are gridded for a single swath (3a) or multiple swaths (3b).

B.3.1 Surface velocity (primary processing with pulse-pair)

The primary algorithm uses the phase shift between pairs of pulses (Zrnic 1977). We recall

<S(t) S*(t+PRI)> = ρ PS eiΦ ,

|<S(t) S*(t+PRI)>| = ρ PS,

Φ = arg[<S(t) S*(t+PRI)>].	(PP.1)

This gives the range-resolved line-of-sight velocity

ULOS =λ Φ PRF / (4 π).

This velocity is dominated by a non-geophysical component UNG is estimated from the Earth rotation, satellite orbit, and platform attitude parameters (Hansen et al. 2011).

The main contributor (first term) is due to satellite motion and produce a significant bias depending on the azimuth position of the beam during the antenna rotation. Antenna pointing shall be known with an accuracy of (=0.3 arcsec – =1.5 arcsec) to ensure a worst case error of 1 cm/s.

The second term corresponds to Earth rotation effect and requires an antenna pointing knowledge 15 times lower. It should be possible to inferred most of this contribution with antenna pointing knowledge from AOCS.

The last contribution is the vertical velocity due to orbit dive angle and can be easily inferred with antenna pointing knowledge from AOCS.

Preliminary simulations (see section 4.2.2.2) have shown that the restitution of this non geophysical velocity is feasible by retracking the data over one or several antenna scan cycles. These parameters are adjusted by a fit to the measured ULOS.

The first geophysical parameter is the “geophysical Doppler velocity” obtained by the difference,

UGD= (ULOS - UNG ) / sin(θi),

and projected on the ground by the sine of the incidence angle θi. This velocity contains contributions from the ocean waves (Chapron et al. 2015, Nouguier et al., submitted),

UWB ≃ G U∞S,R , (WB1)

in which the non-dimensional G factor can be estimated from the ratio of the Doppler and backscatter modulations. The total surface current velocity is then simply

UR = rUGD - UWB, (WB2)

where we can use r ≃ 1 (Chapron et al. 2005), as a first approximation (see Nouguier et al., submitted).

The radial component of the Stokes drift for the same sea state in deep water U∞S,R is coming from the wave spectrum E(k,θ)

Because we do not have the full spectrum at each cycle, we estimate U∞S,R using neighboring cycles,

where US,1D (θ') is the contribution of the waves measured in direction θ'

Processing to level 3 data is performed by optimal interpolation

[ U …. V…. ] = BHT(HBHT + R)-1 [ UR…].

B.3.2 Wave spectra and G

Following Caudal et al. (2014, eq 7), we obtain the spectra of backscatter

C’σσ(k,θ) = < Real { FT[ σ0(x,p) ] FT*[ σ0(x,p+dp) ] >

where the < . > brackets stand for an average over possible values of p, FT is the Fourier transform, and * represents the complex conjugate and Doppler velocity,

C’uu (k,θ) = < Real { FT[ u(x,p) ] FT*[ u(x,p+dp) ] >

and their cross-spectrum

C’uσ (k,θ) = < Real { FT[ u(x,p) ] FT*[ σ0(x,p+dp) ] >.

We thus combine both spectra to obtain our ambiguous wave spectrum,

[ E(k,θ)+E(k,θ+π) ] = αu(k,θi) C’uu(k,θ) + ασ(k,θi) C’σσ(k,θ)

where αu(k,θi) and ασ(k,θi) are empirical functions of the wavenumber and incidence angle, provided by a look-up table.

The directional ambiguous spectrum obtained for each cycle is then “dis-ambiguated” by looking at the sign of the co-spectrum. Consistency checks are applied across the swath.

The ratio cos2θi [σ0(x,p) - σ0] / [sinθi σ0 u(x,p) ] is equal to G ω / k where G is factor used for the current retrieval, and ω is related to k via the ocean wave dispersion relation. Hence G is obtained from the ratio of the two spectra.

B.4 Quantitative examples of output simulator

Sections 4 and B.2 of the proposal already contains many quantitative examples that were there to illustrate our technical assumptions. In particular Tables 2 and 6 summarize the L2 and L3 error as derived from our simulations. We have thus only included a few more illustrations in figure B.22. These illustrate the typical errors in three regions of interest for SKIM:

western boundary currents (here the Gulf Stream)

equatorial currents (here around 23 W in the Atlantic)

marginal ice zones (here Fram Strait).

Figure B.21: Illustration showing Ocean surface current snapshots of the reference truth (left), the simulated L3a product for SKIM flying over the scene at the same time (middle) and the simulated L3b gridded product using all SKIM observations in a +/- 6 day window. Upper row: in the Gulf-Stream region. Middle row: in the Equatorial region. Lower row: in the Fram region. Units are m/s.

Related files are available at ftp://ftp.ifremer.fr/ifremer/ww3/HINDCAST/OTHER/SKIM/

B.5. Quantitative results from simulator proving required performance

The compliance of our preliminary algorithms with the required performance is established in section 4. Specifically, the requirement on

total surface current accuracy of 0.1 m/s at an effective resolution (wavelength) of Lv,eff = 100 km (i.e. dx = 50 km) was demonstrated (see table 6, reproduced below) for most ocean regions of interest using realistic ocean conditions (i.e. we used spring / fall conditions, without assuming a wave height of 2 m for example)

the relative radiometric precision of 0.1 dB is achieved by averaging over pulses and Welch-type spectral averaging

The minimum resolvable wavelength of 20 m is related to the radiometric precision and azimuthal averaging, as discussed in section 4.

For the Gulf Stream the error in Table 6 is of 0.09 and 0.14 m/s for the two components, but at a resolution of 65 km. We have verified that 100 km wavelength to give errors under 0.1 m/s due to the sharp roll-off of the kinetic energy spectrum.

Region:	Equator	Fram (open water)	Fram (ice)	Gulf Stream	Oregon coast
input L2 error (12° & 6° beams) NB: see table 2 for details	0.08 & 0.15 m/s	0.10 & 0.16 m/s	TBD	0.16 & 0.20 m/s	0.20 m/s
L3a (single swath): Leff , along & cross-track error NB: mission goal is 80km, 0.1 m/s for L3a.	89 km, 0.03 & 0.05 m/s	59 km, 0.11 & 0.14 m/s	TBD	65 km, 0.09 & 0.14 m/s	90 km, 0.04 & 0.05 m/s
L3b (multi-swath): Leff , zonal & meridional error	290 km, 0.14 & 0.18 m/s	62 km 0.12 & 0.13 m/s	TBD	120 km, 0.23 & 0.24 m/s	TBD

Table B.1: Summary of effective resolutions and errors of L3a (single swath) and L3b (multiple swaths over time) surface velocity products for the SKIM2 set-up. The effective resolution is the wavelength at which the along-track spectrum of the cross-track current component crosses the noise level.

Annex C: Elements for a Draft Mission Requirements Document

WARNING: There is a considerable level of duplication between section 3 and this Appendix, due to the nature of the information given in both parts. In particular section C.3.1 and C.3.2 are essentially copies (for the sake of completeness of this Annex) of sections 3.1 and 3.2

C.1. Introduction

The ocean surface velocity combines surface currents, driven by winds, pressure gradients, and surface or internal tides, with wave-induced Stokes drift, and rapid O(1 m/s) velocity fluctuations with the wave orbital velocities. This total multi-scale velocity field redistributes heat, freshwater, ice and everything that is in the upper ocean, natural or man-made, including microplastics. Resolving finer scales of the ocean circulation (e.g. Klein and Lapeyre 2009) is a key challenge for quantifying vertical fluxes of nutrients and carbon, and the budget of mechanical energy of the ocean.

Because the ocean surface is the interface between ocean, atmosphere and land, currents play an important role in defining the fluxes of heat, momentum, carbon, water, etc. between Earth System components. For this reason, ocean surface currents are a key variable in tropical ocean monitoring systems (Cravate et al. 2016). Currents are also the main cause of ocean wave variability at scales under 200 km (Ardhuin et al. 2017). Wave observations from space are still limited to very few parameters, and a limited spatial and temporal resolution compared to the variability of sea states.

The main balance of forces at the mesoscale and larger scales (50 km and more) generally involves the Coriolis force and the pressure gradient associated with the sea level slope. Hence ocean surface currents can be diagnosed from the sea level (e.g. Ducet et al. 2000, Penven et al. 2014). The scientific community and space agencies have devoted much effort to go beyond nadir altimetry. An important result is that a wide swath is necessary to monitor the smaller ocean structures that evolve more rapidly. Interferometric SAR technology will be used to produce a 120 km wide swath for the Surface Water Ocean Topography (SWOT) mission.

Instead of ocean topography, one can measure velocities directly. The feasibility of such measurements was demonstrated by Chapron et al. (2005) from Envisat ASAR, and many other following works using the Doppler centroid signal which led to the estimate of maps of one component of the surface current vector. This is equivalent, although more noisy, to Across-Track Interferometry (Romeiser et al. 2014). Compared to altimeter-derived currents, Doppler-derived velocities contain higher resolution ageostrophic motions and high frequency signals that are, at present, only observed by sparse near-surface drifters (Lumpkin and Pazos 2007, Elipot et al. 2016) or High-Frequency radars within 300 km of coasts (e.g. Gurgel et al. 1999). This is particularly relevant in the tropics where geostrophy explains a small fraction of surface currents and does not give access to the very important divergence and upwelling (the structure of which is poorly known) as well as trans-equatorial transport. This is also applies at the ice edge. The Arctic is going through a major regime change, with a Marginal Ice Zone (MIZ) extending wider. A strong coupling is expected at the ice edge between ocean currents, ice, waves and atmosphere that is very difficult to observe today.

Recent investigations of several Doppler-based systems have insisted that ocean waves cause a “Doppler wave bias” (UWB, see Chapron et al. 2005, Martin et al. 2016, Martin and Gommenginger 2017) that is proportional to the surface Stokes drift velocity US along the look direction (Chapron et al. 2005, Nouguier et al. submitted). Given the growing interest into wave-current interactions (e.g. Cavaleri et al. 2012), wave-ice interactions (Thomson et al. 2016) and the known feedback of currents on waves (e.g. Ardhuin et al. 2017), there is a great interest in making joint current, wave and ice measurements.

C.2. Mission background and justification

C.2.1. Knowledge and observational gaps: why SKIM makes a difference

Identified knowledge gaps where progress is needed and where SKIM could make a difference include (non exhaustively),

- boundary currents at the edge of the Marginal Ice Zone and current-waves-ice interactions

- equatorial currents and wind-driven circulation near the Equator

- western boundary currents dynamics

- wave-current interactions: upper ocean mixing, drift, sea states

- air-sea fluxes: wind stress vector τ, energy flux τ ∙ U from wind to ocean circulation

- nearshore wave properties and extreme coastal sea levels

- directional wave properties and microseism / microbarom sources

- remote sensing: waves affect a wide range of measurements (sea level, salinity, wind …)

These are detailed below and associated to science and mission objectives. They include objectives for which SKIM alone can provide a full monitoring with a reasonable revisit time near the equator and faster at higher latitudes, over the lifetime of the mission. For other objectives related to faster evolving processes, SKIM can provide essential data required to correct model biases and systematic errors. SKIM will also be a pathfinder for future satellite altimetry missions that may want to add off-nadir measurements to fulfill these goals.

We have separated the scientific goals into short-term objectives for which clearly identified questions are addressed by active research communities, and longer term emerging objectives for which the ground-breaking nature of SKIM should lead to important progresses that still have to be anticipated by a wider scientific community and/or for which the algorithms are yet to be developed.

C.3. Mission objectives

C.3.1. Short-term scientific goals

Because of the fast evolution of the Arctic, with dramatic changes that can be monitored during a space mission lifetime, and the easy to achieve fast sampling around 75°N, we first focus on the Arctic marginal ice zone, after defining SKIM observables in the context of upper ocean dynamics.

C.3.1.1. UPPER OCEAN DYNAMICS: CURRENT, MIXING, DRIFT & WIND WORK

Defining the total current and its components

The total surface current vector (TSCV), denoted U below, is defined from the average drift velocity of water parcels. This Lagrangian mean drift is U = UE+US , the sum of a quasi-Eulerian current (Jenkins 1989) UE and a Stokes drift US (Stokes 1849). US is the drift motion due to waves that arises from a correlation between the displacement and velocity field. Using linear wave theory, the Stokes drift is readily computed from the directional wave spectrum (Kenyon 1969, see also Peureux et al. 2017). US at the sea surface is of the order of 1.0 to 1.8 % of the wind speed, typically larger than the local wind-induced quasi-Eulerian current known as the Ekman current (e.g. Ardhuin et al. 2009), unless a strong stratification is present (see figure C.1).

US is the part of the total current that varies most rapidly over the vertical, so that the associated transport is typically less than a third of the Ekman transport, and it canceled by the Stokes-Coriolis effect (Hasselmann 1970, Xu and Bowen 1994, Rascle and Ardhuin 2009). In contrast, UE is expected to be fairly uniform close to the surface with horizontal and vertical modulation by Langmuir circulations. This UE is thus comparable to the velocity of drifters drogued at 15 m (Collard et al. 2010).

Figure C.1: Near-surface modeled profiles of quasi-Eulerian UE and Stokes US velocities. Left: snapshot of UE = (UE,VE) with a 20 m deep mixed layer, Center: without stratification. In these two panels, depth is normalized by the significant wave height Hs due it is role in upper ocean mixing (Agrawal et al. 1992). Right: average profiles over 3 months, during the LOTUS3 experiment (Price et al. 1987) and modelled with a variable stratification. Symbols indicate measured values which may correspond to UE + US/2. Dashed line correspond to a model without Stokes-Coriolis effect. Adapted from Rascle and Ardhuin (2009).

The Lagrangian current U is closely related to coastal High-Frequency radar measurements (e.g. Gurgel et al. 1999, Ardhuin et al. 2009). At large space and time scales (larger than the Rossby radius of deformation and twice the inertial period), UE is in geostrophic balance: i.e. the sea level deforms to produce a pressure gradient that balances the Coriolis force. UE may thus be estimated from satellite altimeter data. However, time series at smaller time scales are generally dominated by near-inertial oscillations for mid to high latitudes, e.g. these oscillations have a period of 33 hours at a latitude of 21°N (figure C.2).

Figure C.2: Snapshot (left) and time series (right, dashed lines) of near-surface current, provided by the CMEMS 1/12° global ocean model at two tropical locations in the Atlantic ocean and the 50°N station PAPA in the Pacific, showing the importance of mesoscale currents and near-inertial motions. Overlaid solid lines are measured time series at (21°N,23°W) and (0°N, 23°W) are from the PIRATA moorings (Bourles et al. 2008).

The value of UE is also modulated by upper ocean mixing (Sutherland et al. 2014). Surface mixing is believed to be largely controlled by Langmuir circulations or LCs (e.g. Noh 2004, Cavaleri et al. 2012). The source of energy of LCs is the stretching of ocean turbulence by the Stokes drift, which is of the order of the wind stress times the Stokes drift (Kantha and Clayson 2004).

Energy fluxes

A first component of the air-sea energy flux that can be estimated from SKIM is the work of the wind on the surface current which was discussed above and is a key term in the ocean energy budget (e.g. Ferrari and Wunsch 2009). A possible retrieval of wind stress is discussed in 3.1.4.1 below, otherwise a bulk formula approximation τ≃Cd U10 U10 will be used (e.g. Edson et al. 2013).

The turbulent kinetic energy produced by breaking waves and the Stokes drift straining of turbulence are two other important fluxes. The sea state is an important component of atmospheric analysis and prediction models (e.g. Uppala et al. 2005) and operational coupled forecast models (e.g. Breivik et al. 2015). Key aspects are the determination of momentum and heat fluxes at the air-sea interface, and the proper parameterization of wave-related upper-ocean mixing which controls the diurnal cycle of ocean surface properties and air-sea fluxes. Measurements of the surface Stokes drift vector together with the wind stress vector will provide a direct estimation of the flux of energy to Langmuir turbulence (e.g. Ardhuin and Jenkins, 2006; Belcher et al. 2012; McWilliams 2016).

C.3.1.2. PROCESSES AT THE MARGINAL ICE ZONE

The Arctic Ocean is changing rapidly with thinner, less compact, more fragmented and mobile sea ice (Rampal et al., 2009). The Marginal Ice Zone is predicted to become the dominant Arctic ice province in the summer by the 2050s, as illustrated in figure C.3 (Aksenov et al., 2017). How fast this will actually happen is not clear. Recent experiments in the Beaufort sea are already showing an unprecedented dominance of pancake ice in October (Thomson et al. 2017). The scientific community must therefore be ready to observe and understand these changes.

Figure C.3: Left: Fraction of Arctic sea ice covered by the MIZ (sea ice concentration between 15 and 80%), as projected for the 21st century monthly mean (solid) in winter (December–February; blue lines) and summer (June–August; red lines). The shading denotes one standard deviation (Aksenov et al., 2017). Inset: changes in MIZ width in in summer (June–September, red line) and winter (February–April, blue line) 1979–2011 from satellite data (thick: mean, thin: mean+/- one std, dashed: trend, adapted from Strong and Rigor, 2013). Right: Surface ocean currents modulus (solid red line) and sea ice (solid green line), along with ice fraction across the MIZ (blue dashed line), adapted from Rynders (2017).

As the MIZ expands, ocean waves propagate further into the sea ice cover, breaking more ice and inducing greater lateral melt and more vigorous stirring of the upper ocean from air drag and floe motion (Zhang et al., 2015). This can enhance ice melting, deepening, and freshening of the oceanic mixed layer and its warming from the solar insolation, resulting in enhanced ice melt and a strong positive feedback to the sea ice decline (Tsamados et al, 2015).

Upwelling at the sea ice edge can generate eddies and ice edge oceanic jets (e.g., Bulczak et al., 2015). A related phenomenon is the fast-moving sea ice, so-called ice jets (see figure 2 and 5). Possible causes of the ice jets are the atmospheric and oceanic jets and variation in the surface roughness sea ice near the ice edge (Heorton et al., 2014) or granular dynamics of sea ice in MIZ (Feltham 2005; Rynders 2017). An example simulated jets with strong acceleration of the ocean currents and sea ice drift in the MIZ are shown in Figure 2. The jets can interact with waves through complex coupling mechanisms, including mixing by Langmuir circulations, with a major effect on the near-surface mixing and heat balance.

Over the last 20 years, significant wave heights (Hs) in the Arctic have increased at the rate of 7 to 10% per decade, with Hs doubling in the current century (Francis et al, 2011; Khon et al, 2014; Stopa et al, 2016; Thompson et al, 2016; Aksenov et al, 2017). This could result in an increased wave-induced sea ice breakup and a higher wave energy flux towards the Arctic coastline, accelerating coastal erosion, permafrost collapse and potentially increasing methane release (Overeem et al, 2011; Thomson et al, 2016). Jones et al (2009) detected increased erosion of the Alaskan Coast during 1970s-1990s and 2000s, with average erosion rates in excess of 25 m per year.

Given today’s poor knowledge of ice edge processes, there is a need to have a basin-scale monitoring of currents and waves in both open water and in the marginal ice zone.

C.3.1.3. EQUATORIAL CURRENTS AND WIND-DRIVEN CIRCULATION

Near the Equator, equatorial geostrophy only allows to estimate a small part of the surface current variability, and in particular does not resolve time-dependent divergent currents near the equator. Short period and nonlinear processes often dominate and so far can only be observed by in-situ techniques (Schlundt et al. 2014). For example, Tropical Instability Waves (TIWs) with a near-30 day period often dominate the meridional velocity at the equator (V component in figure 2). The chaotic nature of the TIW generation does not allow to reproduce strength and phases of these waves in forced ocean simulations. However, also in assimilation models, the constraints provided by assimilated data (sea surface height and temperature, as well as profiles of temperature and salinity) are often too weak to produce agreement between simulated currents and currents observed by in-situ methods (e.g. figure C.4). The zonal component, particularly at lower frequency, is often better reproduced in ocean models as it is dominantly forced by the wind and generally follows equatorial geostrophy. However, surface current products purely derived from observations (sea surface height, temperature, and wind stress) still often fail in reproducing zonal and meridional velocity components in the equatorial ocean and the use of these products is of limited value (Schlundt et al. 2014).

Equatorial Kelvin and Rossby waves play a key role in the basin-scale adjustment of the thermocline during El Niños or for the establishment of the seasonal Wyrtki jet in the Indian Ocean. The divergent current associated with these waves (which displace the thermocline and eventually influence the surface temperature and atmosphere above) are not resolved by the “equatorial geostrophy.” Similarly, the cross-equatorial surface transport (which for example plays an important role in the seasonal heat balance of the Indian Ocean) is currently only estimated indirectly from the wind.

Figure C.4: Distribution of near-surface drifters on February 6, 2017. Colors correspond to countries of origin. Note the lack of drifters near the Equator due to their wind-driven divergence away towards the tropics.

Drifters have helped improve on altimeter-derived currents, but they are sparse in divergent flow regions like the equatorial or coastal upwelling regions. This is why, the Tropical Pacific Observing System report recommends a “Continuation of technological developments to measure ocean surface currents remotely” as it is crucial in the tropics for forecasting systems (Cravatte et al. 2016). Besides their importance for the analysis of the forcing of El Nino events, surface currents are also needed for the tropical heat budget which now relies on sparse surface drifters (Foltz et al. 2003, Hummels et al. 2013). Combining surface currents from SKIM and sub-surface velocities, from moorings or drifters, would provide information on the vertical poorly known vertical mixing. Collard et al. (2008) demonstrated a monitoring of equatorial currents using the Doppler centroid from Envisat wave mode, giving the line of sight component of the velocity, as shown in Figure C.5.

Figure C.5: average surface velocity for July 2004, 2005 and 2006, from Envisat (Collard et al. 2008).

A Doppler-based satellite mission should provide equatorial current vectors every 10 days at most to allow the monitoring and analysis of these features. Even with this revisit time, the collection of high resolution (scales of 100 km and larger) snapshots of the total surface current will give access to the equatorial divergence fields, giving a first measurement of the patchy and very narrow Equatorial upwelling. This will help understand the way the sea surface temperature (SST) anomalies are generated, which matter for atmospheric response.

C.3.1.4. COASTAL AND WESTERN BOUNDARY CURRENTS

Strong coastal currents are poorly monitored today, because altimeters are less accurate in coastal regions (e.g. fewer measurements closer than 10-25 km from the coast, stronger tidal signals, strong land-sea contrasts in atmospheric corrections) and because ageostrophic effects are generally strong close to the coast. It is for example difficult to resolve exchanges between the open ocean and the shelf from existing current datasets. In several regions, such as the eastern Pacific or west coast of India, those exchanges can yield surface hypoxia by bringing subsurface sea anoxic water, with very detrimental effect of ecosystems and fisheries (e.g. Naqvi et al. 2009).

Ageostrophic currents are also a key component in frontal regions associated with western boundary currents (e.g. Gulf Stream, Brazil, Kuroshio, Agulhas). Down-front wind-stress and buoyancy fluxes (cooling, evaporation) can intensify boundary current instabilities and ageostrophic cross-frontal circulation. This has important impacts on the cross-frontal exchange of heat, freshwater and nutrients (eg Oguz et al., 2015). For example, the cross-frontal transport of cold, fresh Antarctic Surface waters into the subantarctic zone is thought to be the main factor controlling the water mass properties of mode and intermediate waters formed here (Holte et al., 2013), yet the net ageostrophic transport is difficult to estimate accurately. In the Agulhas current, a choke point of the conveyor belt that exchanges heat and freshwater between the Indian and Atlantic oceans (Beal et al. 2011), it is also difficult to recover the full magnitude of the current (Rouault et al. 2010, see Figure C.6) with altimetry and hydrography alone, in spite of drifter data being used to establish the mean dynamic topography (Rio et al. 2014).

Figure C.6: Mean range‐directed surface current velocity (in m s−1) in an ascending path configuration for (a) ASAR and (b) AVISO (altimetry). Positive values indicate a flow toward the northeast, about 75° from the north. The ASAR mean was computed from 463 ASAR images collected over the Agulhas Current region from 2 August 2007 to 10 September 2009. Only data points with more than 10 observations and radar incidence angles greater than 30° were included in the calculation of the ASAR mean. AVISO current vectors were rotated in the ASAR‐directed range velocity. The stippled black line in Figures 4a and 4b indicates the position of the 1000 m isobath. (From Rouault et al. 2010).

C.3.1.5. HYDRODYNAMIC COMPONENTS OF COASTAL HAZARDS

The response of coastal environments to natural and anthropogenic forcing factors (including climate change) depends on the characteristics of the forcing agents (i.e. sea-surface height, currents, waves). Extreme hydrodynamic events generally occur due to a combination of high astronomical tide, large storm surge and energetic nearshore waves, with a strong sensitivity on the dominant wave period and directional distribution, as schematized in figure C.7. This is exacerbated by sea level rise due to global change and/or local subsidence.

Figure C.7: Schematic of hydrodynamic components that contribute to coastal flooding and erosion

The monitoring of these hydrodynamic components that are responsible for coastal erosion and flooding, as well as the monitoring of sea surface kinematics for navigation and activities at sea, is limited to a few floating buoys measuring waves, few current meters (most of time non-permanent) and tidal gauges measuring still water level. There are about 100 wave buoys along the 100,000 km of European shorelines (one in 1000 km), most of which only report two or three parameters, and no information on the distribution of wave energy as a function of directions. In terms of data availability the situation is much worse in the developing world. In addition, regional seas and complex coastlines (e.g. nearshore European Seas and channels) are strongly influenced by micro and mesoscale processes and in-situ measurements cannot be extrapolated to adjacent nearshore areas. As a result, the understanding of storm events on shorelines and the assessment of derived risks and impacts relies heavily on numerical models for waves and storm surges (e.g. Masselink et al. 2016). Coupling of ocean and atmosphere circulation with wave models is the topic of active research and badly needs more validation data (e.g. Kerr et al. 2013, Ardhuin et al. 2017b). Numerical models have their largest errors within 200 km from the coasts (e.g. Figure C.8), due to, among other factors, inaccurate directional wave spectra and errors in coastal winds (Roland and Ardhuin 2014).

Figure C.8: Left: Normalized rms difference between modelled Hs and satellite altimeter measurements for the year 2009 (Rascle and Ardhuin 2013). These errors give a misleading impression of the state of knowledge of the sea state: other parameters are much more uncertain (Stopa et al. 2016b), including directional spread (bottom: at WMO buoy 62069, France, model is red symbols, data is black line) and frequency content, as illustrated by the height of waves with periods larger than 10 s (right: Hs10 at buoy EURO offshore of Rotterdam, the normalized rms error is 50%).

Focusing on coastal flooding, waves can lead to wave run-up and overtopping (due to individual waves and infragravity waves) but also to wave set-up (mean sea level rise due to wave breaking). At larger scaleswaves also modify wind stress and storm surges (Mastenbroek et al. 1993, Bertin et al. 2012).

Figure C.9: Left: Example of measured sea level on a steep cliff showing large variation of the sea level at the time scale of 5 minutes (From Sheremet et al. 2014). These are infragravity waves whose amplitude is generally proportional to the square root of the significant wave height and a the period of the energetic waves. Right: Once these two factors are normalized, the run-up predicted by state of the art models still varies by a factor 2 with changes in the directional spread (half-width of directional wave spectrum, in colors) and also a factor 2 or more with the frequency width (from Guza and Feddersen 2012).

Accurate measurement of the shape of the directional wave spectrum from SKIM implies the beginning of a paradigm shift for the different approaches to achieve coastal erosion problems and coastal engineering applications. For example, mean direction and spread are a key parameters to determine the extreme sea level at the shoreline. Figure 10 shows 3 m high fluctuations in sea level due to infragravity (IG) waves: the magnitude of these IG waves varies strongly with the wave spectrum shape (e.g. Herbers et al. 1994). Resolving wave spectra is unprecedented down to 20 m wavelength, and effectively resolves waves in all marginal seas as shown in figure 11.

Figure C.10: average fraction of wave energy in January 2014 resolved when using a cut-off wavelength of 150, 70 and 20 m. Note that low values in the Arctic and other ice-covered regions are an artefact of the model that does not properly represent wave-ice interactions. January 2014 was very stormy in the North Atlantic.

By itself, remote sensing from a single satellite that comes every 4 days for a 5-year long mission, is not enough to resolve all storm events. Yet, this gives 500 scenes with varying hydrodynamic conditions that can be used to learn about physical processes and correct model parameterizations (e.g. Ardhuin et al. 2010, Roland and Ardhuin 2014), or perform a statistical correction and downscaling of model results (e.g. Camus et al. 2011, Hegermiller et al. 2017).

C.3.1.6. EXTREME WAVES AND WAVE-CURRENT INTERACTIONS

The statistical distribution of wave heights and the occurrence of “freak” waves, much larger than their neighbors, is a fascinating subject. As presently understood, for a given value of the significant wave height Hs, the extreme individual wave height Hmax is a function of the shape of the full frequency-directional spectrum as illustrated in figure C.11. Fedele et al. (2016) showed that observed freak waves could be explained by the constructive interference of elementary waves (the wave spectrum that SKIM can measure) enhanced by second-order bound nonlinearities which can be predicted theoretically but are sensitive to the spectrum shape (e.g. Janssen 2009, Fedele 2015). Earlier research emphasized the role of a nonlinear phenomenon known as “modulational instability” to explain rogue waves. That theory had been demonstrated in laboratories for waves travelling all in the same direction, but it is very sensitive to the width of the directional spectrum (Waseda et al. 2009).

Figure C.11: Sea state and extreme waves during the “Draupner event”. The modeled directional wave spectrum (left) was used as boundary condition to a phase-resolving (HOS) non-linear wave model. The center panel shows the HOS results (squares) compared to theoretical estimates of the inverse probability distbution (Nh = 1 / P (crest height > x SWH). The right panel illustrates examples of the HOS model time series at third order (blue) and second order (red) compared to the measured time series in black (adapted from Fedele et al. 2016).

Wave focusing by currents may produce other effects in a weakly (Belibassakis 2007) or more strongly nonlinear regime (Waseda et al. 2009, Janssen and Herbers 2009). There is thus a strong need for more accurate measurements of directional wave spectra in the ocean, together with currents, especially in shallow and intermediate water depths. Indeed, extreme waves observed in the North Sea or off the west coast of Ireland are intermediate water or even shallow water depth waves, with the non-dimensional product of wavenumber and depth (kD) ranging from 1 to 2.

Hence a comprehensive and accurate global record of detailed angular and spectral wave energy distribution combined with surface currents would considerably advance our knowledge of extreme individual wave heights. Such a record is not available now. Sentinel-1 class of satellite-based synthetic aperture radar (SAR) measurements has at global monitoring capability (thanks to the wave mode, Hasselmann et al. 2012) with sparse coverage but high resolution. Still, the SAR imaging mechanism is often strongly nonlinear, with a distorted image of the ocean wave field, of particular concern for mapping extreme events. Sentinel-2 class of satellite-based sun-reflected optical observations will provide information at single-wave resolution, revealing wave-current interactions and local wave enhancement (Kudryavtsev et al. 2017), but these snapshots are limited to coastal zones and favorable sun and instrument geometry.

C.3.1.7. OCEAN REMOTE SENSING

Just like improving geodetic reference frames has a cross-cutting impact on all Earth Observations, a better knowledge of ocean waves will benefit to all of ocean remote sensing. Munk (2009) underlined the “inconvenient sea truths” given by the slope statistics derived from the POLDER mission. As he put it, the dataset “says nothing about time and space scales” and is thus very difficult to interpret and use for microwave applications for which the brightness temperature and backscatter are a function of the spectral content of ocean waves. Measuring the shortest possible components of the wave directional wave spectrum, together with slope statistics given by backscatter different azimuths and direction is necessary to further establish the quantitative modelling of ocean waves for remote sensing applications to altimetry (sea state bias), radiometry (for L-band roughness corrections), ocean color (surface reflectance).

C.3.2 Longer-term scientific goals

C.3.2.1. WIND STRESS VECTOR

SKIM will provide new measurements, at nadir and off-nadir, of Ka-band backscatter and Doppler, that are uniquely related to air-sea fluxes of momentum and mechanical energy. These fluxes are primarily controlled by the wind stress vector. Several studies have highlighted that scatterometers can provide a better proxy to the wind stress than the wind speed (e.g. Weissman and Graber 1999, Portabella and Stoffelen, 2009). However, scatterometers wind direction mostly comes from the alignment with the wind of the short gravity-capillary waves that contribute to the ocean backscatter. Given the large body of evidence that the wind stress can actually be at an angle that is intermediate between the directions of the dominant and intermediate waves and that of the wind (e.g. figure 3.13), SKIM combined measurements offer unique opportunities for taking this aspect into account when estimating the wind stress vector.

Figure C.12: Example of measured wave spectra and differences between the measured wind direction (blue) and stress direction (magenta), with large differences when the wave direction (green) is oblique (from Potter et al. 2015).

Resolving 20 m long waves, and likely shorter components, while measuring the wind direction from the azimuthal modulation of the Doppler and backscatter at 6° and 12° incidence, with wind speed estimates from the nadir beam, SKIM will enable to more precisely document this variation in direction. Moreover, theory suggests a strong parallel between surface wind stress and nadir and near-nadir backscatter measurements, because both are formed through partial integration of the wave slope distribution (e.g. Plant 1982, Kudryavtsev et al., 2014). The unique combination of SKIM measurements shall thus be a great occasion to revisit wind stress vector estimates.

To date, only aircraft Ku-band altimeter backscatter data, collected over open-ocean extensive near-surface measurements acquired by the research vessels, indicate a more highly correlation with u*, the friction velocity, than with the 10-m wind speed (Vandemark et al., 1997). In this study, the authors reported that a single parameter mapping between altimeter signals and u* shows excellent agreement for u* ranging from 0.0 to 0.4 m/s, without the need for other variables usually used in bulk formulae:current, stratification, or other ancillary data. This suggests that Ku-band altimeter backscatter data would be better used for direct wind stress derivation rather than for wind speed. After Nouguier et al. (2016) extensive comparisons between Ku- and Ka-band GPM measurements, It is expected that this conclusion easily translates to Ka-band nadir and near-nadir measurements.

C.3.2.2. MICROSEISMS AND SOLID EARTH

Since the pioneering work of Shapiro et al. (2005), the use of microseism signals generated by ocean waves has been the source of many innovations in the monitoring of faults and volcanoes. Microseisms are particularly strong at periods 2 to 10 s. Their generation by ocean waves is now well understood (Longuet-Higgins 1950, Hasselmann 1963, Ardhuin et al. 2015) and the dominant source is associated to the presence of waves travelling in opposite directions. The separation of such opposing wave trains will not be possible with CFOSAT/SWIM, but is possible in principle with SKIM using the combination of Doppler and backscatter modulations. This possibility has not been demonstrated yet and we expect difficulties associated to noise. Figure C.13 below shows the large contrast between the wave heights, that can be measured today with altimeters, and the source of microseisms which is only available by numerical modelling.

Figure C.13: Generation of the May 2002 seismic noise burst. (a) An extra-tropical storm (track indicated by red dots) and hurricane “Alma” produce swells with nearly opposite direction where (b) the wave induced pressure perturbation is maximum. (Reproduced from Obrebski et al. 2012).

Such a modeling of microseisms from wave models has been validated across the globe by Stutzmann et al. (2012) but it still presents large errors that probably come from the modeled wave spectra and the seismic propagation models at these periods.

An independent verification of the magnitude and spatial distribution of strong microseisms sources would allow more refined analyses. For example this would improve the time resolution of changes in seismic wave velocities estimated from microseism correlations. These velocity changes are used to monitor volcanic activities and stress relaxation in faults. A better microseism model may also help reveal fainter and yet unknown signals, possibly up to gravitational waves (Mulargia et al. 2016).

C.3.3. Beyond science: applications and societal impact, particular case of CMEMS

The Copernicus Marine Environment Monitoring Services (CMEMS) are a central point for data ingesting, processing, validation and distribution. The services are adding value to EO data in terms of environmental monitoring by making the best synergetic use of EO, in situ and modelling. CMEMS has included waves to the other ocean services in April 2017 and have made the consistency of ocean currents and waves a priority for the development of future services (CMEMS has funded 3 service evolution R&D projects in 2015: Wave2NEMO, OWAIRS, ArcticMix related to coupled waves-ocean processes). Accurate forecasts of surface currents have also been identified as a priority for CMEMS users. Today the different communities have the possibility to better exchange and make easier the data exchange for cross qualification and cross validation. In addition, the CMEMS Arctic MFC plans to address the waves-ice interactions in the Marginal Ice Zone as a follow-up of the European SWARP project and SKIM is so far the only proposed observing system able to provide sea ice drift information in the MIZ.

Data assimilation in the CMEMS operational ocean models is rapidly moving from Optimal Interpolation type of methods to more advanced methods that account for flow-dependent “errors of the day” (primarily Ensemble Kalman Filters and 4D-variational methods as well). Those methods are often able to handle complex non-linear observation operators, such as Lagrangian motions (Sakov et al. 2012) and coupled ice-ocean systems (Lisæter et al. 2003). The combined wave and surface currents from SKIM present a similar type of conceptual complexity and fall within the scope of the applications of ensemble Kalman Filters. A PhD study should be sufficient to test the performance of assimilating directly the L2 UGD product in a waves, ocean and sea ice coupled model.

C.4. Mission requirements

We reproduce here table 7 (section 4) with all the mission requirements When appropriate, the three levels of requirements are used. Otherwise a unique value is given when there is no possible trade-off.

These 3 levels are:

- Goal: is the value that, if exceeded, would not yield significant improvements in mission performance.

- Threshold: is the value below which the observation would not yield any significant benefit for the mission.

- Breakthrough: is an intermediate value between “threshold” and “goal“ that, if achieved, would result in a significant improvement for the mission. The breakthrough level is expected to be more appropriate than the “goal” from a cost-benefit point of view.

All requirements follow from the “top-level” requirement on the monitoring of currents, which is formulated for instantaneous total surface current accuracy of each component within a single SKIM pass. We have not defined requirements for L3b products given the wide variety of scales in different regions of the world ocean. We will define such requirements, region by region, during phase A. However, such additional requirements only have an importance for the choice of orbit, with very limited trade-offs except for considering level 4 products merging other sources of data.

Parameter	Goal	Breakthrough	Threshold	Comments
Total surface current or ice drift accuracy (each component)	0.05 m/s at Lv,eff = 50 km for L3a product	0.1 m/s at Lv,eff = 50 km for L3a product	0.1 m/s at Lv,eff = 100 km for L3a product
Revisit	< 1 day at equator	< 10 days at equator
Lifetime	10 years	7 years	5 years
Coverage	global	all ice-free ocean + most marginal ice zones
Further requirements derived from the above primary goal requirements
orbit	SSO, 695 km, -98°	N/A	SSO
Phasing with S1	+4°E, at least for cal-val	N/A	N/A
Latency	NRT	1 hour	1 day
Nadir ground track accuracy	1 km	2 km	5 km	need for nadir altimer SSH
Revisit	< 1 day at equator	< 10 days at equator
SKIM elevation pointing knowledge	0.2°	0.4°
SKIM azimuth pointing knowledge	1°	2°
Platform pointing accuracy knowledge	0.01°	0.01°
Platform pointing accuracy	0.1°	0.2°	0.3°
Platform stability (Relative Pointing Error)	1 arcsec over 20 s	7 arcsec over 20s	needed for accurate retrieval of attitude
Relative radiometric precision between beams (for modulations)	0.01 dB	0.02 dB	0.1 dB
Vector precision knowledge	0.1°	0.2°	Azimuth angle between 2 radar measurements on the same point
Absolute radiometric precision	0.5 dB	1 dB
Minimum resolved ocean wavelength Low,min	10 m	15 m	20 m
Associated requirements for secondary sea state goals (see also table C.5)
SWH accuracy (from wave spectrum)	8 %	20%
Wave direction (from wave spectrum)	5°	15°
Wind speed (from nadir)	2 m/s (or 10%, the larger)

Table C.1: Mission requirements summary

C.5. Observation techniques and requirements

The following tables summarize the instrument-level (raw data and level 1 processing) that follow from the mission requirements,

Parameter	Value
Frequency	35.75 GHz
Polarization	Radial
Bandwidth	200 MHz
PRF	32 kHz
Observation geometry	Conical scan
Incidence angles	Nadir – 6° - 12°
Number of beams	8 beams
Number of pulse / beam	145 to 1024 (programmable)
Rotation rate	3 to 5.6 rpm (fixed)
Beam aperture (3-dB)	0.5°
Peak Power	1.5 kW
Pulse length	1.6 to 3.2 µs
Data rate	20 Mbits/s (nom.) – 50 Mbits/s (max)

Table C.2: Instrument parameters.

Processing	Type	Coding
Range pulse compression	Matched filtering Range migration correction	Complex 32b
Power detection + temporal averaging	Range samples	Integer 16b
Pulse Pair processing + temporal averaging	Range samples	Complex 16b x 16b
Unfocussed SAR	Range Migration Correction Doppler centroïd Correction Bin stacking	Complex 8b x 8b
Delta-k	3 delta-k frequencies Cross-correlation 2 range samples	Complex 8b x 8b

Table C.3: Instrument on-board processing (for all beams)

Parameter	Value
SNR (single shot)	>30 dB Nadir >10 dB (6°) >7 dB (12°)
Altimetric performances - range noise - range bias/drift	1cm @1Hz (2m) 1 mm/an
Radiometric performances - Absolute accuracy - Relative Beam Accuracy - Resolution (Kp)	+ 0.5 dB + 0.1 dB 0.1 dB
Pointing restitution accuracy	0.2 arcsec (incidence) 1 arcsec (azimuth)
Pulse pair phase estimator resolution (LOS over beam)	2 cm/s (12°) 2.6 cm/s (6°)
Effective resolution (range resolution+range migration correction)	<6 m

Table C.4: Instrument performances

C.6. Data products and usage

The geophysical variables required to develop the science and applications described in section 3, and which are derivable from SKIM, are summarized in Table C.3, which is an expanded version of Table 3.1. These data products will be used in many ways, either directly or for the verification / validation / assimilation in numerical models.

Variable	description	Units	Accuracy (goal)	SRL	References
UGD	Geophysical Doppler velocity	m/s	0.1 m/s over 6 km footprint, 1024 pulses	4-5	This proposal
UR=UR-UWB	Total surface current, radial component, 12° beams	m/s	0.15 m/s over 6 km footprint, 1024 pulses	4-5	Chapron et al. (2005) Martin et al. (2016) Ardhuin et al. (subm.)
U=(U,V)	Total surface current vector	m/s	0.1 m/s at 80 km wavelength or 40 km pixel (L3a)	4-5	Ubelmann et al. (subm.)
UE=(UE,VE)	Quasi-Eulerian current vector	m/s	0.1 m/s at 80 km wavelength	4	Chapron et al. (2005)
Ui=(Ui,Vi)	Ice drift velocity vector	m/s	0.1 m/s at 40 km wavelength (L3a)	3	Ardhuin et al. (subm.)
Us=(US,VS)	Surface Stokes drift vector	m/s	0.4 cm/s or 10%, whichever is greater for radial components	4-5	Ardhuin et al. (subm.)
E(k,θ)	Ocean wave spectrum Following quantities up to σ0 are derived from E(k,θ) their accuracy is for Leff = 50 km.	m3	20% for partitions with a mean square slope > 10-4. Minimum wavelengh Low,min = 20 m.	4	Jackson et al. (1985) Caudal et al. (2014)
Hs	significant wave height	m	30 cm or 8%, whichever is greater	8 for nadir 4 for other	Grelier et al. (2016)
Hsi	swell height for partition number i	m	30 cm or 8%, whichever is greater	5	Caudal et al. (2014)
Tpi	Peak period of partition i	s	1 s, if Hsi > 30 cm	5	Caudal et al. (2014)
θm,i	mean direction of partition i	degrees	5° if Hsi > 30 cm	5	Caudal et al. (2014)
σθ,i	directional spread of partition i	degrees	4° if Hsi > 30 cm	5	Caudal et al. (2014)
Fp2,i(K=0)	Second order equivalent surface pressure variance for partition i at large wavenumbers (source of microseisms)	Pa2 m2	50% or 5 x 106 Pa2 m2 whichever is greater	3	Hasselmann et al. (2013) Ardhuin et al. (2011)
σ0	normalized radar cross section	unitless	0.1 dB resolution of modulations at 20 m wavelength	5	Jackson et al. (1985) Hauser et al. (2017)
mssshape	mean square slope	unitless	0.001	3	Nouguier et al. (2016)
U10N	Neutral wind speed at 10 m height	m/s	2 m/s or 10%	3	to be defined, based on Nouguier et al. (2016)
τ	wind stress vector	Pa	to be defined	2	to be defined
SSH	sea surface height	m	3.5 cm RMS (1 s average)	8	see section 3.

Table C.5: Geophysical parameters derived from SKIM measurements. See section 4 for justification of SRL and error budget (in particular tables 4.1 and 4.6).

C.6.1. Direct data usage and level4 products

Surface velocity are already produced from data :

HF radars in coastal areas,

altimetry, scatterometers and drifters, soon Doppler radial components

sea ice drift from SAR / radiometers correlations or other techniques (e.g. Korosov & Rapal 2017).

The same is true for ocean waves, with

altimeter product (e.g. Globwave)

SAR-derived “fireworks” (propagated partitions, see Collard et al. 2009).

Some of these datasets (in particular the Globcurrent, Globwabe and fireworks) are planned to be integrated into the CMEMS catalog before 2019 current product . SKIM will provide additional data to build these level4 products in the the context of the Climate Change Initiative programs for Sea ice (CCI sea ice drift) and waves and currents (CCI sea state).

In some regions, e.g. latitudes 75 to 82°, the SKIM stand-alone product will resolve a large part of the time variability at wavelengths of 100 km and more and can be used as a “stand-alone” product.

Even for the Equator where the revisit time is longer, the SKIM snapshots will probably be used for many things from inferring vertical fluxes from the surface divergence, finding hotspots of microplastics convergence, search operations at sea, and even the search for aircraft wreks (such as flight AF447 in 2009).

A more complete use of the data will likely combine models and data.

C.6.2. Model verification and calibration

Waves Operational weather services have performed a routine validation of their wave analyses and forecasts against buoy data for the last 20 years (Bidlot et al. 2002, Bidlot 2017). This model verification project is part of the JCOMM program, and has been open to research / pre-operational systems such as the one operated by Ifremer/SHOM. This project was pivotal in demonstrating the benefits of new parameterizations that take into account swell dissipation in a way consistent with SAR-derived observations (Ardhuin et al. 2009, 2010). This difference in parameterization explains the general better performance of the “MTF” and “SHM” systems compared to “ECM” system.

Figure C.14: Example of monthly report from the JCOMM wave model verification program, showing the scatter index of significant wave height (Hs) at 126 buoys for May to July 2016. Most of these buoys are in the Northern Hemisphere and thus strongly affected by swells. The best performing systems are “MTF”: Meteo-France MFWAM which includes altimeter and SAR assimilation, “SHM” operated by Ifremer as a research demonstrator. This figure was taken from one of the quarterly JCOMM reports.

The “SHM” system runs without assimilation but uses higher order propagation with the WAVEWATCHIII code and tidal current forcing for some regions, and “ECM” operated by ECMWF which uses a different wave generation / dissipation parameterization. All 3 systems use ECMWF wind fields, but with different temporal resolutions. The reason for differences with other systems is mostly the accuracy of the forcing wind fields.

With SKIM data, a new era of wave measurements will combine spectral extension (resolving all dominant wave components) and spatial coverage at high resolution, but sparse time sampling. Following WMO recommendation, ECMWF is to become a Lead Centre for Wave Forecast verification (Bidlot 2017). At first to reproduce what has been for JCOMM but later to potentially extend the validation to other observation data sets. SKIM data, in due time could constitute a useful data set. Another use of SKIM data will be the provision of a ‘sky-truthing’ of the wide variety of wave-measuring platforms which lack a common calibration method (Swail et al. 2009).

Currents and ice drift. CMEMS surface currents are presently validated against in situ drifters, but the feedback from the validation to producers is limited by the low spatial density of surface drifters. The validation of surface currents of the Arctic MFC reveals for example a general low bias varying seasonally but this is not sufficient to identify the necessary improvements of the model. Surface currents from SKIM will provide an unprecedented wealth of validation data for operational current forecasts and development of new forecasting models. The validation of Arctic MFC ice drift and waves near the ice edge is also missing from present ice drift measurements although both are crucial informations for ships. SKIM measurements in the MIZ will thus cover a terra incognita of ice motions. SKIM will provide an unprecedented wealth of validation data for operational current forecasts and development of new forecasting models. The validation of Arctic MFC ice drift and waves near the ice edge is also missing from present ice drift measurements although both are crucial informations for ships. SKIM measurements in the MIZ will thus cover a terra incognita of ice motions.

C.6.3. Data assimilation

So far, OSSE have been done separately for surface current and ocean wave spectra. In the future, a coupled ocean circulation + wave model that resolves the interaction scales of waves and currents (typically resolution finer than 1/6° for currents and 1/4° for waves) may be able to assimilate directly the geophysical Doppler velocity UGD , directly taking into account the correlations of UE and US.

C.6.3.1. OSSE for surface current in HYCOM

The TOPAZ assimilation system (now referred to as the Copernicus Arctic Marine Forecasting Center) uses a regional configuration of the HYCOM ocean model over the North Atlantic and Arctic Oceans and assimilates different types of satellite and in situ observations with an Ensemble Kalman Filter running 100 dynamical members. The ensemble members are perturbed by the addition of non-divergent random winds with an amplitude of 2.5 m/s (see Xie et al. 2017 for more information about the reanalysis). We have used the simulated sea surface currents estimation uncertainties as of SKIM to produce a measure of the impact of assimilating SKIM surface currents in conjunction to all other observations on a typical weekly cycle of the TOPAZ reanalysis in May 2015, after 24 years of reanalysis.

The DFS diagnostics (Degrees of Freedom for Signal) is a standard way to measure the information content of assimilated data and has a maximum value of 100 in the case of the EnKF used in TOPAZ. Acceptable DFS values range from 0 to 10, above which there is a risk of “over-assimilation”. The EnKF being applied locally, the observation impact is calculated grid cell by grid cell, its calculation relies on the knowledge of the location of the observations, their time and uncertainty estimates. Since all observations are assimilated jointly, the impact of one observation type is reducing that of the other types. The DFS values are dependent on the background and observation uncertainties specified in the TOPAZ system and may turn out different in a different ocean data assimilation system.

Figure C.15 exhibits the DFS values obtained by assimilating simulated SKIM surface currents to other (real) measurements. The highest DFS appears in frontal regions like the Equatorial Counter-Current, the Gulf Stream and the Azores Current. The area near the Equator shows particularly high values as the impact of traditional altimeter data is limited by the vanishing of the Coriolis force. The South Atlantic is artificially removed as the simulated SKIM data only covers the North Atlantic.

Figure C.16 present the same results in relative terms as percentage of the total DFS on each location. It indicates that SKIM provides the second most useful dataset overall, and the most useful information in the Gulf Stream and Equatorial regions for data assimilation. It may seem counter-intuitive that the impact of surface currents from SKIM exceeds the impact of depth-averaged currents as measured from traditional altimeters in the Gulf Stream. This could indeed be a transient effect due to the first-time assimilation of SKIM: the ensemble variance of surface currents may reduce on the following assimilation cycles and the DFS reduce accordingly. Alternatively it could be due to the neglection of representativity errors.

The results presented here only utilize the surface currents but not yet the surface waves nor the sea ice drift from SKIM. Even if the OSSE is only performed as a “one-shot” case study and does not resolve complex feedbacks of repeated data assimilation cycles, the OSSE indicates that there is a scope for assimilation of sea surface currents in an operational forecasting system and that SKIM data is not redundant with respect to other ocean observations.

Figure C.15: Degrees of Freedom for Signal of all assimilated observations during one week in May 2015. From Top line from left to right: OSTIA Sea Surface Temperature, CMEMS delayed-mode altimeter tracks, SKIM sea surface. Bottom line: OSI SAF Sea ice concentrations, in situ (incl. Argo) Temperature and Salinity profiles.

Figure C.16: Same as above but showing relative DFS as fraction of the total. Note than SKIM surface currents are the main source of information in the Gulf Stream and in the Equatorial Counter-Current.

C.6.3.2. OSSE for waves in MFWAM

The lessons from using SAR wave spectral information provided by Envisat and Sentinel-1 revealed the significant improvement of directional properties of swell wave systems (Aouf et al. (2012 & 2016). Unlike conventional assimilation of altimeter wave height which affects the total sea, the assimilation of wave spectra directly corrects the mean wave parameters of each wave train that can be propagated by the wave model over a long period. It has been also demonstrated that the assimilation of wave spectra is more efficient to correct peak period in particular for mixed sea and swell.

The proposed SKIM mission will ensure wave partitions of more than 20 m of wavelength describing wave trains of mixed seas and partial wind-sea dominant sea state. This opens a room of improvement regarding to wave modelling and also assimilation in wave forecasting systems.

The OSSE experiment consists in using the Meteo-France settings of the WAve Model (MFWAM) to generate synthetic wave spectra as observed by SKIM. The wave model is driven by 6-hourly analysed ECMWF winds and the corresponding sea state is considered as « truth ». 6-hourly wave spectra at SKIM orbit tracks are extracted and considered as synthetic SKIM wave spectra. One can add random errors in order to take into account SKIM instrument errors. These spectra have been sampled every 50 km along the SKIM track.

The assimilation system of Meteo-France has been implemented and used in operations for Envisat level 2 wave spectra (Aouf et al. 2012). The assimilation of SKIM synthetic wave spectra is performed in the wave model MFWAM for the global scale driven by 3-day forecast ECMWF atmospheric forcing (winds and ice fraction). This use of forecasts introduces a de-correlation of the first guess with the synthetic wave data. The model grid resolution is 0.5°. The assimilation uses a cut-off on the wave spectrum of a wavelength of 50 m (shorter components adjust fast to the wind forcing and thus have a limited impact). The period of run starts from 22 August 2016 at 0:00 (UTC) until 4 September 2016 at 0:00 (UTC). In this study combined assimilation of SKIM and Sentinel-1 wave spectra has been performed. To validate the results comparison with altimeters wave data and also « truth » wave parameters built by the wave model can be used.

Figure (left) shows an example of analysis increment of mean wave period induced by the assimilation of SKIM wave data on 29 August 2016 at 0:00 UTC. This reveals a strong impact induced by the assimilation with a maximum of difference reach more than 2 seconds. The impact is affecting most of the ocean basins in particular the pacific ocean because of hurricane and typhoon seasons with several high waves systems are recorded.

The validation with synthetic « truth » significant wave height shows the significant reduction of the normalized scatter index in all ocean basins, as illustrated in figure (right). The scatter index of significant wave height is improved by roughly 19 %, 17 % and 20 % in the tropical, intermediate and high latitudes regions, respectively. We can easily remark that the best correction on significant wave height is obtained for the high latitudes and the tropics. The first ocean region is well known as the starting point of low pressure systems generating high wind sea and swell wave systems, while the second ocean region represents the place of convergence of all swells propagating from long distance. The same tendency of results has been indicated when comparing the assimilation results with significant wave heights from altimeters Jason-2 and Saral.

The study reveals the relevance of using SKIM directional wave spectra in the forecast period. The impact stays efficient with a maximum increment more than 1 second after 3-day forecast as illustrated in figure left. We can see clearly when comparing figures (left versus right) that the use of SKIM wave data enhances the impact on mean wave period in different ocean basins (indian, Atlantic and pacific oceans) regarding to the assimilation of S-1A and S-1B spectra only.

Moreover the validation of the impact of the assimilation in the forecast period (assimilation is switched off) the scatter index of significant wave height from run with the assimilation of SKIM and S1A and S1B wave spectra is better than the one from run with assimilation of altimeter wave height, as illustrated in figure C.19.

The advantage of the assimilation of SKIM wave data is inducing correction on several wave trains separately. This process contribute to improve at the end the peak wave period estimate of the total sea. The validation of the assimilation of SKIM and S1A and S1B with NDBC buoys indicates an improvement of the scatter index of the peak period by roughly 15 % regarding to the reference run without assimilation.

Figure C.17: Left: example of snapshot impact (analysis increment) for the mean wave period. Right: Scatter index of analysed significant wave height (Hs) from runs without assimilation (NOASSI), or when assimilating S1A and S1B SAR wave modes (ASSI-S1AB) or when assimilating SKIM, S1A and S1B (ASSI-SKIM+S1AB). The smaller is the scatter index, better the wave analysis is. The larger impact of SKIM is due to its broader spectral content.

Figure C.18 : left : snapshot of impact for the mean wave period from run with assimilation of S-1A and S-1B only after 3 days in the forecast period (7 september 2016 at 0:00 UTC). right : same as left but with assimilation of SKIM and S-1A and S-1B.

Figure C.19: Variation of the scatter index of significant wave height for intermediate latitudes (between |20°| and |50°|) in the forecast period (the assimilation is switched off). triangles, circle and cross lines stand for runs with the assimilation of both SKIM, S-1A and S-1B wave data (ASSI-SKIM-S1), the assimilation of altimeter wave height only (ASSI-ALTI) and control without assimilation (NOASSI), respectively. The comparison has been performed with altimeters wave height from Jason-2 and Saral.

C.7. Synergies and international context

C.7.1. Synergies with Sentinels 1, 2 & 3

The closest measurement existing at the same time as SKIM for currents and waves (after the end of the CFOSAT mission) is provided by the Sentinel 1 (S1) synthetic aperture radar imagery constellation. At the time of writing, Doppler processing on S1 still suffers from poorly corrected platform attitude. Yet, solutions are anticipated. This will provide means to derive Doppler and wave parameters that can yield valuable information on one component (in the line-of-sight) of the total surface current vector. SKIM does not require S1, but it will benefit from the presence of S1-C and/or S1-D for validation purpose, and to enhance sampling and production of combined products. We have thus phased the orbit of SKIM to that of S1 to either have overlaps that provide match-up datasets enhancing the value of both missions. A different shift of the orbital plane could be used after CAL-VAL to maximize the combined coverage of SKIM and the S1 constellation.

The Sentinel-2 class of satellite-based sun-reflected optical observations will provide information at single-wave resolution, revealing wave-current interactions and local wave enhancement (Kudryavtsev et al. 2017). these snapshots are limited to coastal zones and favorable sun and instrument geometry.

Finally, the combination of currents and their convergences with ocean color data, in particular from Sentinel 3, will provide many opportunities for investigating vertical fluxes.

C.7.2. Synergies with nadir altimeters

The present constellation of altimeters (Sentinel 3, Cryosat 2, Jason 3, SARAL/AltiKa …) will continue to provide wave heights and sea level measurements that will be used to map ocean currents. SKIM provides one more nadir altimeter (with good performance even if SKIM does not include a radiometer) but also highly complementary measurements (total surface current vectors + wave spectra).

C.7.3. Synergies with SWOT

SWOT is designed as a 3-year mission and has a launch date estimated in 2021. If we believe this date and the margins in the launch schedule for SKIM are used (giving a lunch date in 2015) then the two missions will not overlap. If these two assumptions do to materialize a joint flight, even for a reduced duration, observations at cross-over would greatly benefit both missions, in particular for the separation of geostrophic and ageostrophic flows (see Figure C.20) and for cross-calibrations.

Figure C.20: Simulated ocean currents over the Gulf Stream (Courtesy D. Menemenlis, JPL) and associated SKIM and SWOT simulated observations for a single satellite pass. The SWOT simulated current assumes that the current is in geostrophic equilibrium with the sea surface height (SSH), which introduces small scale noise associated to ageostrophic motions.

Even if they do not fly at the same time, Sentinel 1 data (Doppler centroid from IW mode) could provide a go-between to translate patterns of sea level (SSH) into surface currents.

C.7.4. Other aspects and possible additional payloads

As mentioned in the executive summary, marine monitoring also uses tracking system such as Argos and AIS. Although these are not Earth Observation systems they provide exceptional services that are complementary to SKIM observations. In particular, tracking of frigatebirds has revealed a close connection with ocean mesoscales (Kai et al. 2009) and the tracking of ships is now used to to infer surface currents at the ship location (e.g. http://www.e-odyn.com/). Hence there would be a great benefit in making sure that these tracking data are acquired simultaneously with SKIM data, possibly by including the necessary payloads on the SKIM platform. This aspect was not considered in this proposal to limit the complexity of the analysis, but it can be discussed in phase A.

Annex D: acronyms

AOCS	Altitude and Orbit control System

BAT	Battery

CEU	Core Electronics Unit

CMEMS	Copernicus Marine Environement Monitoring System

CSS	Coarse Sun Sensor

ECV	Essential Climate Variable

EPS	Electrical Power Platform

GB2	GloBalstar 2

GNSS	Guidance Navigation Sub-System

HPA	High Power Assembly

I/F	Interface

IRN	IRIDIUM

MAG	MAGnetometer

MLI	Multi Layer Insulation

MMDE	Modular Motor Drive Electronics

MSCE	Motor and Switch Command Electronics

MTB	Magneto Torquer Bar

MYREV	MYRiade Evolution

NetCDF	Network Common Data Form (https://www.unidata.ucar.edu/software/netcdf/)

OCM	Orbit Control Mode

PCDU	Power Conditioning & distributing Unit

PL	Payload

PF	Platform

PROTEUS	Plateforme Reconfigurable pour l’Observation, les Télécommunications Et les	Usages Scientifiques.

RTU	Remote Terminal Unit

RW	Reaction Wheel

RWI	Reaction Wheel Isolator

SA	Solar Array

SAPU	Solar Array Protection Unit

S/C	SpaceCraft

SKaR	SKIM Ka-band Radar

SMU	Satellite Management Unit

STR	Star Tracker

SSM	Second Surface Mirror

TAS-F	Thales Alenia Space – France

TMI	TeleMetry Immediat

TRL	Technologies Readiness Level

TCR	Telecomand Command & Ranging

TTC	Thermal Telemetry and Control

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Zrnic, D. S. (1977). Spectral Moment Estimates from Correlated Pulse Pairs, IEEE Trans. Aero. Electronic Sys., 13(4), 344 - 354. doi: 10.1109/TAES.1977.308467

Annex F: SKIM team and community for research and applications

The SKIM Team & community are organized in 4 concentric circles:

- the PI: F. Ardhuin

- the core proposing team (co-Investigators)

Erik van Sebille (UU),	Justin Stopa (LOPS),

Sophie Cravatte (LEGOS),	Oyvind Breivik (MET Norway),	Peter Brandt (GEOMAR),

Frédéric Nouguier,	Alexis Mouche	&	Bertrand Chapron (LOPS),

Christine Gommenginger,	Yevgeny Aksenov	&	George Nurser (NOC),

Anton Korosov,	Johnny A. Johannessen &	Laurent Bertino (NERSC),

Marion Tissier	&	Ad Reniers (T.U. Delft),	Lotfi Aouf (Meteo-France),

Clement Ubelmann (CLS),	Céline Tison	&	Gérald Dibarboure (CNES),

Fabrice Collard	&	Lucile Gaultier (OceanDataLab),	Alberto Naveira-Garabato (U. Southampton)

Melisa Menendez (EHI-UC),	Eleonore Stutzmann (IPGP),	Frederic Dias (Univ. College Dublin),

- the full list of SKIM team members who have contributed to the proposal (see cover page) and section F.2 for Cvs.

- a SKIM community interested in using SKIM data for various research or applications:

Below are listed persons who have expressed a strong interest in the SKIM mission. co-Is are highlighted in bold. The list is organized by topic and country of residence. Short CVs of science team members and description of institutions follow.

This is the list as of June 15, 2017

Support letters are gathered at : ftp://ftp.ifremer.fr/ifremer/ww3/TEAM/EE9/SUPPORT_LETTERS

F.1 Identified user groups by application fields

F.1.1 marginal ice zones: currents, ice & waves

ESA member states & Canada

Dr. Dany Dumont, Université du Quebec à Rimouski, Quebec, Canada

Dr. William Perrie, Bedford Institute of Oceanography, Halifax, Canada

Dr. Fanny Girard-Ardhuin, LOPS, Univ Brest-CNRS-Ifremer-IRD, France

Dr. Peter Sutherland, LOPS, Univ Brest-CNRS-Ifremer-IRD, France

Dr. Jean Tournadre, LOPS, Univ Brest-CNRS-Ifremer-IRD, France

Dr. Giuseppe Aulicino, Università Politecnica delle Marche, Italy

Dr. Laurent Bertino, NERSC, Bergen, Norway

Dr. Anton Korosov, NERSC, Bergen, Norway

Dr. Pierre Rampal, NERSC, Bergen, Norway

Dr. Tim Williams, NERSC, Bergen, Norway

Dr. Agnieszka Herman, Univ. Gdansk, Poland

Dr. Yevgeny Aksenov, NOC, Southampton, UK

Dr. George Nurser, NOC, Southampton, UK

Dr. Céline Heuzé, University of Gothenburg, Sweden

Other countries

Prof. Mike Meylan, University of Newcastle, Australia

Dr. Richard Gorman, NIWA, New Zealand

Dr. Fabien Montiel, University of Otago, New Zealand

Dr. Bjorn Lund, RSMAS, Miami, FL, USA

Dr. Erick Rogers, Naval Research Laboratory, Stennis, MS, USA

F.1.2 Ocean circulation

ESA member states & Canada

Prof. Cédric Chavanne, Université du Québec à Rimouski, Quebec, Canada

Dr. Sophie Cravatte, LEGOS, Toulouse, France

Dr. Guillaume Charria, LOPS, Univ Brest-CNRS-Ifremer-IRD, France

Dr. Jonathan Gula, LOPS, Univ Brest-CNRS-Ifremer-IRD, France

Dr. Louis Marié, LOPS, Univ Brest-CNRS-Ifremer-IRD, France

Dr. Guillaume Roullet, LOPS, Univ Brest-CNRS-Ifremer-IRD, France

Dr. Rosemary Morrow, LEGOS, Toulouse, France

Dr. Jérôme Vialard, LOCEAN, IPSL, Paris, France

Dr. Pierre-Marie Poulain, OGS, Trieste, Italy

Dr. Laurent Bertino, NERSC, Bergen, Norway

Prof. Oyvind Breivik, MET Norway, Bergen, Norway

Dr. Johnny A. Johannessen, NERSC, Bergen, Norway

Prof. Peter Brandt, GEOMER, Germany

Dr. Emil Stanev, Helmolz Zentrum Geestacht, Germany

Dr. Simon Ruiz, IMEDEA, Mallorca, Spain

Dr. Pedro Velez, IEO, Tenerife, Spain

Dr. Erik van Sebille, University of Utrecht, The Netherlands

Dr. George Nurser, NOC, Southampton, UK.

Dr. Yevgeny Aksenov, NOC, Southampton, UK

Other countries

Prof. Leandro Farina, Universidade Federal do Rio Grande do Sul, Brasil

Prof. Andres Sepulveda, University of Concepcion, Chile

Dr. Vladimir Toro, Univ. de Antioquia, Colombia

Prof. Nori Aiki, Institute for Space-Earth Environmental Research, Nagoya University, Japan

Prof. Baylor Fox-Kemper, Brown University, Dept. of Earth, Environmental, and Planetary Sciences, USA

F.1.3 Ocean waves, including wave-current interactions

ESA member states & Canada

Dr. Fabrice Ardhuin, Lab. d'Océanographie Physique et Spatiale, Univ Brest-CNRS-Ifremer-IRD, France

Dr. Peter Sutherland, LOPS, Univ Brest-CNRS-Ifremer-IRD, France

Dr. Emil Stanev, Helmolz Zentrum Geestacht, Germany

Dr. Luigi Cavaleri, ISMAR, Venezia, Italy

Dr. Alvise Benetazzo, ISMAR, Venezia, Italy

Prof. Oyvind Breivik, MET Norway, Bergen, Norway

Dr. Johnny A. Johannessen, NERSC, Bergen, Norway

Dr. George Nurser, NOC, Southampton, UK.

Dr. Yevgeny Aksenov, NOC, Southampton, UK

Other countries

Dr. Diana Greenslade, Bureau of Meteorology, Melbourne, Australia

Prof. Ian Young, University of Melbourne, Australia

F.1.4 coastal hazards & Coastal engineering

ESA member states & Canada

Dr. Ira Didenkulova, Marine Systems Institute, Tallinn University of Technology, Tallinn, Estonia

Dr. Nicole Delpeche-Ellmann, Tallinn University of Technology, Tallinn, Estonia

Dr. Fabrice Ardhuin, Lab. d'Océanographie Physique et Spatiale, Univ Brest-CNRS-Ifremer-IRD, France

Dr. Jérome Aucan, LEGOS, Centre IRD de Nouméa, Nouméa, New Caledonia

Dr. Anne-Claire Bennis, University of Caen, France

Dr. Xavier Bertin, LIENS (CNRS-Univ. La Rochelle), La Rochelle, France

Dr. Bruno Castelle, EPOC (CNRS-Univ. Bordeaux), Bordeaux, France

Dr. Guillaume Dodet, LETG-GEOMER (Univ. Brest – CNRS), Brest, France

Dr. Déborah Idier, BRGM, Orléans, France

Prof. Vincent Rey, Mediterranean Institute of Oceanography, Toulon, France

Prof. Julien Touboul, Mediterranean Institute of Oceanography, Toulon, France

Prof. Serge Suanez, LETG-GEOMER (Univ. Brest – CNRS), Brest, France

Prof. Konstandinos A. Belibassakis, Naval Architec. & Marine Engng, National Tech. Univ. Athens, Greece

Prof. Giovanni Besio, Dipartamento di Ingegneria Civile, Chimica e Ambientale, University of Genova, Italy

Prof. Ing. Eugenio Pugliese Carratelli, Consorzio inter-Universitario per la Previsione e Prevenzione dei Grandi Rischi, Univ. Federico II, Naples & Univ. Salerno, Italy

Prof. Ad Reniers, T.U. Delft, The Netherlands

Prof. Dano Roelvink, IHE Delft Institute for Water Education, The Netherlands

Prof. Marion Tissier, T.U. Delft, The Netherlands

Prof. Rafal Ostrovski, Institute of Hydro-Engineering of the Polish Academy of Sciences, Gdansk, Poland

Prof. Carlos Guedes Soares, Instituto Superior Técnico, Universidade de Lisboa, Portugal

Prof. Liliana Rusu, 'Dunarea de Jos' University of Galati, Romania

Dr. Ana Abascal, IH-Cantabria, Universidad de Cantabria, Santander, Spain

Prof. Mendez Incera, ETSI Caminos, Canales y Puertos, Univ. Cantabria, Santander, Spain

Dr. Melisa Menendez, IH-Cantabria, Universidad de Cantabria, Santander, Spain

Dr. Yevgeny Aksenov, NOC, Southampton, UK

Other countries

Dr. Mark Hemer, CSIRO Oceans and Atmosphere, Australia

Prof. Ian Young, University of Melbourne, Australia

Dr. Yong Wan, China University of Petroleum, Qingdao, China

Prof. Prasad K. Bhaskaran, Dept. of Ocean Engineering & Naval Architecture, IIT Kharagpur, India

Prof. Yaron Toledo, Physical oceanography and marine engng laboratory, Tel-Aviv University, Israel

Prof. Nobuhito Mori, Kyoto University, Japan

Dr. Sooyoul Kim, Tottori University, Japan

Dr. Roeber Volker Roeber, Tohoku University, Sendai, Japan

Dr. Arshad Rawat, Mauritius Institute of Oceanography, Mauritius

Prof. Christian Appendini, Laboratorio de Ingeniería y Procesos Costeros, UNAM, Sisal, Yucatán, México

Dr. Issa Sakho, Université de Thiès, Senegal

Dr. Adem Akpinar, Division of Coastal Engineering, Uludag University in Bursa, Turkey

Dr. Peter Bromirski, Scripps Institution of Oceanography, La Jolla, CA, USA

Dr. Tom Drake, Office of Naval Research, USA

F.1.5 air-sea interactions & marine meteorology

ESA member states & Canada

Dr. William Perrie, Bedford Institue of Oceanography, Halifax, Canada

Dr. Jacqueline Boutin, LOCEAN, IPSL, Paris, France

Dr. Lotfi Aouf, Météo-France, Toulouse, France

Dr. Hubert Branger, IRPHE (CNRS-Univ. Marseille), Marseille, France

Prof. Alvaro Semedo, UNESCO-IHE, Delft, The Netherlands

Prof. Oyvind Breivik, MET Norway, Bergen, Norway

Dr. Fabien Leckler, Service Hydrographique et Océanographique de la Marine, Brest, France

Dr. Peter Sutherland, Lab. d'Océanographie Physique et Spatiale, Univ Brest-CNRS-Ifremer-IRD, France

Prof. Anna Rutgersson, Uppsala University, Sweden

Dr. Jean-Raymond Bidlot, ECMWF, UK

Dr. Andrew Saulter, Met Office, Exeter, UK

Dr. Jamie Shutler, Univ. Exeter, UK

Other countries

Prof. Nobuhito Mori, Kyoto University, Japan.

Dr. Richard Gorman, NIWA, New Zealand

Dr. Richard Bouchard, US National Data Buoy Center, NOAA, Stennis, MS, USA

Dr. Robert E. Jensen, U.S. Army Corps of Engineers, PI of wave measurement program, Vicksburg, USA

Prof. Brian Haus, RSMAS, Miami, FL, USA

Dr. Bjorn Lund, RSMAS, Miami, FL, USA

Dr. Erick Rogers, Naval Research Laboratory, Stennis, MS, USA

F.1.6 ambient seismic and acoustic noise : solid earth and upper atmosphere

ESA member states & Canada

Prof. Michel Campillo, ISTerre (UGA-CNRS-IRD), Grenoble, France

Dr. Alexis Le Pichon, CEA, Bruyeres le Chatel, France

Prof. Barbara Romanowicz, IPGP & Collège de France

Dr. Eleonore Stutzmann , IPGP, Paris, France

Dr. Chris Bean, Dublin Institute of Adv. Studies, Ireland

Dr. Sven Peter Näsholm, NORSAR, Norway

Dr. Martin Schimmel, Institute of Earth Sciences Jaume Almera – CSIC, Barcelona, Spain

Other countries

Prof. Anya M. Reading, School of Physical Sciences (Earth Sciences), Univ. Tasmania, Australia

Prof. Richard C. Aster, Department Head, Department of Geosciences, Colorado State University, CO, USA

Dr. Peter Bromirski, Scripps Institution of Oceanography, La Jolla, CA, USA

Dr. Peter Gerstoft, Scripps Institution of Oceanography, La Jolla, CA, USA

Dr. Tiago Oliveira, Woods Hole Oceanographic Institution, USA

Prof. Michael Ritzwoller, Dept. of Physics, University of Colorado, Boulder, CO, USA

F.1.7. coastal ecosystems

ESA member states & Canada

Prof. Jörg-Olaf Wolff, ICBM, University of Oldenburg, Germany

Other countries

Dr. Ernesto Caetano, Institute of Geography, National University of Mexico, Mexico

F.1.8. wave physics, statistics & applied mathematics

ESA member states & Canada

Prof. Tarmo Soomere, Estonian Acad. of Sciences, Tallinn, Estonia

Prof. Frederic Dias, University College Dublin, Ireland

Other countries

Prof. Yaron Toledo, Physical oceanography and marine engng laboratory, Tel-Aviv University, Israel

Dr. Sergei Badulin, Head of Laboratory, P.P. Shirshov Institute of Oceanology, Moscow, Russia

F.1.9 climate change

ESA member states & Canada

Dr. Nadia Kudryavtseva, Tallinn University of Technology, Estonia

Dr. Guillaume Dodet, LETG-GEOMER (Univ. Brest – CNRS), Brest, France

Dr. Justin Stopa, Lab. d'Océanographie Physique et Spatiale, Ifremer-Univ Brest-IRD-CNRS, France

Dr. Sofia Caires, Deltares, The Netherlands

Prof. Alvaro Semedo, UNESCO-IHE, Delft, The Netherlands

Dr. Yevgeny Aksenov, NOC, Southampton, UK

Other countries

Dr. Mark Hemer, CSIRO Oceans and Atmosphere, Australia

Prof. Nobuhito Mori, Kyoto University, Japan

Dr. Victoria G. Grigorieva, P.P. Shirshov Institute of Oceanology, Moscow, Russia

Prof. Baylor Fox-Kemper, Brown University, Dept. of Earth, Environmental, and Planetary Sciences, USA

F.1.10 Marine energy & ocean engineering

ESA member states & Canada

Prof. Constantine Michailidis, Cyprus University of Technology, Cyprus

Prof. Jens Peter Kofoed, Head of Division, Aalborg University, Denmark

Dr. Aurelien Babarit, Ecole Centrale de Nantes, France

Dr. Anne-Claire Bennis, Univ. Caen, France

Dr. Jean-François Filipot, France Energie Marine, Brest, France

Dr. Christophe Maisondieu, Ifemer, Brest, France

Prof. Konstandinos A. Belibassakis, Naval Architec. & Marine Engng, National Tech. Univ. Athens, Greece

Sander Hulst, BMT Argoss, The Netherlands

Dr. Ricardo Martins Campos, CENTEC-IST, University of Lisbon, Portugal

F.1.11 Remote sensing

ESA member states & Canada

Dr. William Perrie, Bedford Institute of Oceanography, Halifax, Canada

Dr. Nadia Kudryavtseva, Tallinn University of Technology, Estonia

Dr. Jacqueline Boutin, LOCEAN, IPSL, Paris, France

Dr. Bertrand Chapron, LOPS, Ifremer-Univ Brest-IRD-CNRS, France

Dr. Fabrice Collard, OceanDataLab, Brest, France

Dr. Alexis Mouche, LOPS, Ifremer-Univ Brest-IRD-CNRS, France

Dr. Danièle Hauser, LATMOS (CNRS-U. Versailles St Quentin), France

Dr. Romain Husson, CLS division radar, Brest, France

Dr. Estelle Obligis, CLS, Toulouse, France

Dr. Geir Engen, NORUT, Tromso, Norway

Dr. Harald Johnsen, NORUT, Tromso, Norway

Dr. Johnny Johannessen, NERSC, Bergen, Norway

Dr. Christine Gommenginger, NOC, Southampton, UK

Other countries

Dr. Dimitri V. Ivonin, P.P. Shirshov Institute of Oceanology, Moscow, Russia

Dr. Vladimir Karaev, Institute of Applied Physics, Russian Academy of Sciences, Russia

Prof. Vladimir Kudryavtsev, RSHU, Saint Petersburg, Russia

Dr. Douglas C. Vandemark, University of New Hampshire, NH, USA

F.2 Curriculum vitae of all team members

In alphabetical order except for the PI which is presented first.

Dr.	Fabrice Ardhuin, Ph.D., Hab., LOPS, Brest, France
short bio. F. Ardhuin is a CNRS senior research scientist (DR2), and director of the Laboratoire d’Océanographie Physique et Spatiale (LOPS), a joint research unit of University of Brest, CNRS, Ifremer and IRD. F.A. received his Ph.D. from the Naval Postgraduate School (Monterey, CA) in 2001, and a habilitation degree from University of Brest in 2005, and has been working on ocean waves and many related topics ever since. His scientific activities range from microseism to remote sensing with a strong focus on wave-current and wave-ice interactions, and their nearshore impact. He has been involved in the take-off of Doppler oceanography from space since the seminal paper by Chapron, Collard & Ardhuin (2005), and is now a member of the Science Teams of both SWOT and CFOSAT satellite missions. He was the recipient of the 2008 Fofonoff Award of the American Meteorological Society, the 2009 “Christian Le Provost Océanographe” award (now a grand prix of the French Academy of Sciences), and a 2009 ERC grant for “Integrated Ocean Waves for Geophysical and other Applications”.
Project role	PI of the SKIM science team
Experience	since October 2014 CNRS senior researcher (DR2), LOPS, France 2010-2014 Ifremer senior researcher, LOS, France 2001-2009 researcher/program manager, French Navy (SHOM)
Publication record
Citation report as of May 2017 (from ISI Web of science), for a total of 101 items: Sum of the Times Cited: 2378, Average Citations per Article: 24. h-index: 28 Selected relevant papers: Ardhuin, F., N. Rascle, B. Chapron, J. Gula, J., Molemaker, J., Gille, S. T., Menemenlis, D., and Rocha, C. (2017). Small scale currents have large effects on wind wave heights, J. Geophys. Res., 122, 2017. in press. doi:10.1002/2016JC012413. Ardhuin, F., L. Marié, N. Rascle, P. Forget, and A. Roland, A. (2009). Observation and estimation of Lagrangian, Stokes and Eulerian currents induced by wind and waves at the sea surface, J. Phys. Oceanogr., 39, 11, 2820–2838, 2009. doi:10.1175/2009JPO4169.1. Ardhuin, F., Sutherland, P., Doble, M., and Wadhams, P. (2016). Ocean waves across the Arctic: attenuation due to dissipation dominates over scattering for periods longer than 19 s, Geophys. Res. Lett., 43, 5775–5783. doi:10.1002/2016GL068204. Ardhuin, F., L. Gualtieri, and E. Stutzmann (2015). How ocean waves rock the earth: two mechanisms explain seismic noise with periods 3 to 300 s, Geophys. Res. Lett., 42, 765–772. doi:10.1002/2014GL062782.
Relevant projects:	DUE Globwave, FP7/ERC IOWAGA, H2020 SWARP, US NOPP WAVE MODELLING, CMEMS ArcticMix, ONR DRI “Sea State in the Arctic”

Dr.	Yevgeny Aksenov, Ph.D., NOC, Southampton, UK
short bio. Y. Aksenov is a Senior Research Fellow at the National Oceanography Centre (NOC) and has 27 years of experience in sea ice and ocean research. He has obtained BSc and MSc in Fluid Dynamics from the State University of St. Petersburg (Russia), followed by PhD in Polar Oceanography, University of Cambridge (UK). He has published 36 papers and two book chapters. His research includes global modelling of ocean circulation and sea ice dynamics, implications for climate and impacts on sea ice and ocean in the Marginal Ice Zone. He participated in four high-latitude Arctic field campaigns. Dr Aksenov represents NOC in the NEMO Sea Ice and NEMO Wave Working Groups. He facilitates NOC participation in the Forum for Arctic Ocean Modeling and Observational Synthesis and represents NOC in the UK Metoffice Joint Sea Ice Modelling Programme. Dr Aksenov supervised Postdoctoral Researchers and PhD and MSc students. Dr Aksenov was a NOC local PI and a work package leader on the EU FP7 grant “Ships and waves reaching polar regions – SWARP” and is NOC local PI on three UK grants and in a collaborative Australian Antarctic Science Programme (AASP).
Project role	Co-I, Modelling of the MIZ, sea ice and ocean currents
Experience	2016 Invited Visiting Scientist at the Ocean University of China, China 2013 Visiting Scientist at the Alfred Wegener Institute (AWI), Germany 2009 Visiting Scientist, Univ. catholique de Louvain-La-Neuve (UCL), Belgium 2004-pres. Senior Research Fellow, Marine Systems Modelling / Ocean Modelling and Forecasting, National Oceanography Centre, UK. 1999-2004 Research Fellow, Southampton Oceanography Centre, UK 1999 Visiting Scientist for Frontiera Research Program for Global Change, University of Alaska, Fairbanks AK, USA 1996-1999 PhD, University of Cambridge, UK 1995 Nippon Foundation Visiting Scientist, National Institute of Polar Research (NIPR), Japan. 1987-1996 Junior to Senior Scientist, Arctic and Antarctic Res. Inst., St. Petersburg, Russia
Publication record
Citation report as of Apr 2017 (from ISI Web of science), for a total of 32 items: Sum of the Times Cited: 501, Average Citations per Article: 16. h-index: 14 Selected relevant papers: Aksenov, Y., Popova, E. E., Yool, A., Nurser, A. G., Williams, T. D., Bertino, L., & Bergh, J. (2017). On the future navigability of Arctic sea routes: High-resolution projections of the Arctic Ocean and sea ice. Marine Policy, 75, 300-317. doi.org/10.1016/j.marpol.2015.12.027 Aksenov, Y., B. M. Karcher, A. Proshutinsky, R. Gerdes, de Cuevas, E. Golubeva, , F. Kauker, A. Nguyen, G. Platov, , M. Wadley, E. Watanabe and A.C. Coward (2016), Arctic Pathways of the Pacific Water: Arctic Ocean Model Intercomparison Experiments, J. Geophys. Res. Oceans, 2016, 121, 27–59. doi:10.1002/2015JC011299. Armitage, T. W., Bacon, S., Ridout, A. L., Thomas, S. F., Aksenov, Y., & Wingham, D. J. (2016). Arctic sea surface height variability and change from satellite radar altimetry and GRACE, 2003–2014. Journal of Geophysical Research: Oceans, 121(6), 4303-4322. doi: 10.1002/2015JC011579. Luneva, M. V., Aksenov, Y., Harle, J. D., & Holt, J. T. (2015). The effects of tides on the water mass mixing and sea ice in the Arctic Ocean. Journal of Geophysical Research: Oceans, 120(10), 6669-6699. doi: 10.1002/2014JC010310.
Relevant projects:	UK Natural Environment Research Council (NERC) Project “Poles Apart: why has Antarctic sea ice increased, and why can't coupled climate models reproduce observations?”; EU FP7 “Ships and waves reaching polar regions – SWARP”; UK NERC "Atmosphere to ocean momentum transfer by sea ice"; UK NERC Industrial Innovation Project “Safer Operations at Sea - Supported by Operational Simulations (SOS-SOS)”; Australian Antarctic Science Programme Project “Drift of the Antarctic ice edge”.
Selected commitments in community service
2007-2015. Member of the System Team for the Nucleus for European modeling (NEMO) 2009-pres. Theme leader for the Forum for Arctic Ocean Modeling and Observational Synthesis (FAMOS) 2011-pres. Organiser of the EGU session OS1.2/OS1.4/CR6.8 “Changes in Arctic sea ice and ocean” 2013-pres. Member of the UK National Centre for Ocean Forecasting Data Assimilation 2016-pres. NOC member of the NEMO Sea Ice Working Group 2014-pres. Member of the Atmosphere-waves-ocean NEMO Working Group 2014-pres. NOC member of the UK NERC-UKMO Joint Sea Ice Modelling Programme

Dr.	Lotfi Aouf, Météo-France, France
short bio. Lotfi Aouf is a senior scientist leading the wave modeling and data assimilation at Département Marine et Océanographie, Directions des Opérations (DirOP/MAR) of Météo-france. He has a strong experience in satellite wave data and has been involved in several CAL/VAL of altimetry and SAR missions (Jason 1, 2 & 3, Envisat, Saral, Sentinel-1A & 1B, Sentinel-3A). He is also a member of the science team of CFOSAT mission. He participated in relevant European FP7 Research projects. He is involved in the global and IBI Copernicus Marine Environment and Monitoring Service (CMEMS) for the ocean/waves coupling
Project role	Co-I : Air-Sea interactions and Marine meteorology
Experience	since 2001-today Scientist at Météo-France
Publications record
relevant papers : 1- Aouf L., and J. M. Lefèvre (2015), On the Impact of the Assimilation of Saral/Altika wave data in the operational wave model MFWAM. Marine Geodesy. 02/2015; DOI:10.1080/01490419.2014.1001050. 2 - Aouf L., J. M. Lefèvre (2012), On the assimilation of the ASAR L2 wave spectra in the operational wave model MFWAM, Proceedings of Advances in SAR oceanography, SEASAR 2012, Tromso 18-22, 2012. 3 - Hasselmann, K., B. Chapron, L. Aouf, F. Ardhuin, F. Collard, G. Engen, S. Hasselmann, P. Heimbach, P. Janssen, H. Johnsen, H. Krogstad, S. Lehner, J.-G. Li, X.-M. Li, W. Rosenthal, and J. Schulz-Stellenfleth (2012). The ERS SAR wave mode: a breakthrough in global ocean wave observations, in ERS Missions: 20 Years of Observing Earth, 165–198, European Space Agency, Noordwijk, The Netherlands. 4 - Ardhuin, F., Rogers, E., Babanin, A., Filipot, J.-F., Magne, R., Roland, A., van der Westhuysen, A., Queffeulou, P., Lefevre, J.-M., Aouf, L., and Collard, F., “Semi-empirical dissipation source functions for wind-wave models: part I, definition, calibration and validation,” J. Phys. Oceanogr., 40, 9, 1917–1941, 2010. 5 - Aouf L., Lefèvre J. M. and Hauser D. (2006), Assimilation of synthetic SWIMSAT directional wave spectra in wave model WAM. Journal of Atmospheric and Oceanic Technology Vol. 23, No. 3, pages 448–463, 2006.
relevant projects	MAXWAVE, Globwaves, Mywave, INCREO, CFOSAT

Prof.	Christopher J. Bean, DIAS, Dublin, Ireland
short bio. Chris Bean holds a BA in Mathematics, M.Sc. in Applied Geophysics and a PhD in Seismology. He is currently Professor of Geophysics in the School of Cosmic Physics, at the Dublin Institute for Advanced Studies (DIAS). Previous positions include Professor of Geophysics at the School of Geological Sciences, University College Dublin and postdoctoral/visiting positions at Karlsruhe University, IPGP Paris, Stanford University and the University of Granada. His research interests cover the quantification of ocean wave generated ambient microseism noise, ocean wave parameter estimation from microseisms, temporal monitoring of water column and near sea floor properties using time-lapse ambient noise seismo-acoustics. He and his group have also published widely on micro-seismicity associated with the failure of weak materials, especially on volcanoes. His primary funders include the European Commission, Science Foundation Ireland (SFI), the Irish Marine Institute and Industry.
Project role	Co-I : microseisms and sea floor mechanical processes
Experience	2015-Present: Senior Professor of Geophysics Group, DIAS, Ireland. 2011-2014: Director of the Earth Institute (100+ researchers) University College Dublin. 2000-2017: Co-PI/PI on 8 European Commission funded projects.
Publications record
selected papers : D. Craig, C.J Bean, I. Lokmer, M. Möllhoff (2016), Correlation of Wavefield‐Separated Ocean‐Generated Microseisms with North Atlantic Source Regions . Bulletin of the Seismological Society of America, doi:10.1785/0120150181. Möllhoff, M. and C.J. Bean (2016), Seismic Noise Characterization in Proximity to Strong Microseism Sources in the Northeast Atlantic, Bulletin of the Seismological Society of America, 106 (2), 464-477, doi: 10.1785/0120150204. E.P.S. Eibl, C.J. Bean, K.S. Vogfjörd, Y. Ying, I. Lokmer, M. Möllhoff, G.S. O’Brien, F. Pálsson, 2017. Tremor-rich shallow dyke formation followed by silent magma flow at Bárdarbunga in Iceland, Nature Geoscience, doi: 10.1038/NGEO2906 Thun, J., I. Lokmer, C.J. Bean, E.P.S. Eibl, B.H. Bergsson, and A. Braiden (2016), Micrometre-scale deformation observations reveal fundamental controls on geological rifting, Scientific Reports, http://www.nature.com/articles/srep36676 Bean, C.J., L. De Barros, I. Lokmer, J.P. Metaxian, G. O'Brien, S. Murphy (2014), Long-period seismicity in the shallow volcanic edifice formed from slow-rupture earthquakes, Nature Geoscience, Vol. 7, pp. 71-75, doi: 10.1038/ngeo2027.
relevant projects	WAVE-OBS (SFI), MaREI (www.marei.ie), SFI iCRAG Centre (www.icrag-centre.org).

Prof.	Kostas Belibassakis, NTU Athens, Greece
short bio. Kostas Belibassakis is Associate Professor at School of Naval Architecture and Marine Engineering of National Technical University of Athens. He has been actively participated in teaching and research concerning Modelling of Wave Phenomena in the Sea Environment, Non-linear Waves and Ship Dynamics. Wave climate and energy potential (statistical) description. Cv and publication list available from http://arion.naval.ntua.gr/~kbel
Project role	Co-I, wave-bottom-current interaction in nearshore and coastal regions
Experience	2011-today: Assoc.Prof. NTUA
Publication record
Citation report as of November 2016 (from ISI Web of Science) for a total of 40 items Sum of the times cited: 707, h-index: 13 Selected relevant papers 1. Makris Ch., Galiatsatou P., …., Belibassakis Κ., Rusu E., 2016, Climate change effects on the marine characteristics of the Aegean and Ionian Seas, Ocean Dynamics, DOI10.1007/s10236-016-1008-1. 2. Touboul J., Charland J., Rey V., Belibassakis K., 2016, Extended Mild-Slope equation for surface waves interacting with a vertically sheared current Coastal Engineering 116, 77–88. 3. Belibassakis, K.A., Athanassoulis, G.A., Gerostathis, Th., 2014, Directional wave spectrum transformation in the presence of strong depth and current inhomogeneities by means of coupled-mode model, Ocean Engineering 87, 84–96. 4. Belibassakis, K.A., Gerostathis, Th., Athanassoulis, G.A., 2011, A coupled-mode model for water wave scattering by horizontal, non-homogeneous current in general bottom topography, Applied Ocean Research 33, 384– 397
Relevant projects:	Eurowaves (MAST-III 1997-2000), Arion (EU-IST 2001-04), Envisat, (EU-ENVIRONMENT 2002-05), Marina platform (2009-14, FP7-ENERGY)
Selected commitments in community service
2007-2015. Member of the System Team for the Nucleus for European modeling (NEMO) 2009-pres. Theme leader for the Forum for Arctic Ocean Modeling and Observational Synthesis (FAMOS) 2011-pres. Organiser of the EGU session OS1.2/OS1.4/CR6.8 “Changes in Arctic sea ice and ocean” 2013-pres. Member of the UK National Centre for Ocean Forecasting Data Assimilation 2016-pres. NOC member of the NEMO Sea Ice Working Group 2014-pres. Member of the Atmosphere-waves-ocean NEMO Working Group 2014-pres. NOC member of the UK NERC-UKMO Joint Sea Ice Modelling Programme

Dr.	Laurent Bertino, Ph.D., NERSC, Bergen, Norway
short bio. LB is an expert of sequential data assimilation methods. He has reformulated them based on the concepts of geostatistics and opened new theoretical developments. He has since then applied the Ensemble Kalman Filter to the primitive equations HYCOM model and has been responsible for the development and operations of the TOPAZ ice-ocean forecasting system since January 2003. LB is leading the Arctic element of the Copernicus Marine Environment Monitoring Service, co-leading a Nordic Center of Excellence on ensemble-based methods for environmental monitoring and prediction, awarded by NordForsk. LB has been managing the FP7 SWARP project (“Ships and waves reaching polar regions”) focused on the Marginal Ice Zone. He has also managed industry-driven ocean modelling projects in the South China Sea, in the Gulf of Mexico and in the Barents and Kara Seas.
Project role	Co-I, data assimilation ocean and sea ice models
Experience	2010 – present Research Director, NERSC, Bergen, Norway 2003 – 2010 Group leader, Modelling and Data Assimilation, NERSC, Norway 2002 – 2003 Post-doc, NERSC, Norway 2001 – 2002 Post-doc ARMINES / Ecole des Mines de Paris, France 2001 PhD in Geostatistics from Ecole des Mines de Paris 1998 Master from Ecole des Mines de Paris, France.
Relevant Publications (out of a total of 55)
Xie, J., Bertino, L., Counillon, F., Lisæter, K. A., & Sakov, P. (2017). Quality assessment of the TOPAZ4 reanalysis in the Arctic over the period 1991–2013. Ocean Science, 13(1), 123–144. http://doi.org/10.5194/os-13-123-2017 Lien, V. S., Hjøllo, S. S., Skogen, M. D., Svendsen, E., Wehde, H., Bertino, L., Counillon, F., Chevallier, M., Garric, G. (2016). An assessment of the added value from data assimilation on modelled Nordic Seas hydrography and ocean transports. Ocean Modelling, 99, 43–59. http://doi.org/10.1016/j.ocemod.2015.12.010 Counillon, F., Bethke, I., Keenlyside, N., Bentsen, M., Bertino, L., & Zheng, F. (2014). Seasonal-to-decadal predictions with the ensemble Kalman filter and the Norwegian Earth System Model: a twin experiment. Tellus A, 66. doi:10.3402/tellusa.v66.21074 Williams, TD, Bennetts, LG, Squire, VA, Dumont, D, Bertino, L (2013). Wave-ice interactions in the marginal ice zone. Part 1: Theoretical foundations. Ocean Modelling 71, 81-91. Sakov, P., F. Counillon, L. Bertino, K. A. Lisæter, P. R. Oke, and A. Korablev (2012), TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic, Ocean Sci., 8, 633–656. Melsom A., F. Counillon, J. H. LaCasce and L. Bertino. 2012. Forecasting search areas using ensemble ocean circulation modelling. Oc. Dynamics. 62, 1-13. DOI: 10.1007/s10236-012-0561-5.
Relevant projects:	Leader of the Arctic Marine Forecasting Center of the Copernicus Marine Services. http://marine.copernicus.eu/ (Select “Arctic Ocean” to access products). Coordinator of the European FP7 project SWARP “Ships and Waves Reaching Polar Regions”. Coordination of the Nordic Center of Excellence EmblA “Ensemble-based Data Assimilation for Environmental Monitoring and Predictions”.

Prof.	Peter Brandt, Ph.D., Hab., Christian-Albrechts-Universität zu Kiel and GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany
short bio. P. Brandt is Professor for experimental oceanography at Christian-Albrechts-Universität zu Kiel and at the GEOMAR Helmholtz Centre for Ocean Research Kiel, Germany. He has a longstanding expertise in observing physical processes relevant for tropical Atlantic climate variability and climate change including moored observations in the central equatorial Atlantic, the tropical North Atlantic and at the western and eastern boundaries of the tropical South Atlantic. Specifically his interest focus on equatorial dynamics, mesoscale eddies, planetary waves, oceanic oxygen distribution and ventilation processes, internal waves and mixing, and more general the interannual to decadal circulation variability and the role of different processes in climate variability and climate change. He was chief scientist during 10 major research cruises into the tropical Atlantic and conducted several glider (swarm) experiments. He has supervised 6 PhD theses and 12 master theses in physical oceanography.
Project role	Co-I, tropical Atlantic circulation and impacts
Experience	since 2016 Full Professor (W3) at the Christian-Albrechts-Universität zu Kiel and at GEOMAR Helmholtz Centre for Ocean Research Kiel 2007 - 2016 Associate Professor (W2) at the Christian-Albrechts-Universität zu Kiel and at GEOMAR Helmholtz Centre for Ocean Research Kiel 1999 - 2007 Assistant Professor (C1, since 2004 C2) at the Christian-Albrechts-Universität zu Kiel and at the Leibniz Institute of Marine Sciences, Kiel 1998 - 1999 Postdoc., GKSS-Forschungszentrum, Geesthacht 1996 - 1998 Postdoc., Institut für Meereskunde, Hamburg
Relevant Publications
Citation report as of May 2017 (from ISI Web of science), for a total of 75 items: Sum of the Times Cited: 1335. h-index: 23 Selected relevant papers: 1 Brandt, P., M. Claus, R. J. Greatbatch, R. Kopte, J. M. Toole, W. E. Johns, and C. W. Böning (2016), Annual and semi-annual cycle of equatorial Atlantic circulation associated with basin mode resonance, J. Phys. Oceanogr., 46, 3011–3029, doi: 10.1175/JPO-D-15-0248.1. 2 Brandt, P., H. W. Bange, D. Banyte, M. Dengler, S.-H. Didwischus, T. Fischer, R. J. Greatbatch, J. Hahn, T. Kanzow, J. Karstensen, A. Körtzinger, G. Krahmann, S. Schmidtko, L. Stramma, T. Tanhua, and M. Visbeck (2015), On the role of circulation and mixing in the ventilation of oxygen minimum zones with a focus on the eastern tropical North Atlantic, Biogeosciences, 12, 489–512, doi:10.5194/bg-12-489-2015. 3 Brandt, P., A. Funk, A. Tantet, W. E. Johns, and J. Fischer (2014), The Equatorial Undercurrent in the central Atlantic and its relation to tropical Atlantic variability, Climate Dynamics, 43, 2985-2997, doi:10.1007/s00382-014-2061-4. 4 Brandt, P., A. Funk, V. Hormann, M. Dengler, R. J. Greatbatch, J. M. Toole (2011), Interannual atmospheric variability forced by the deep equatorial Atlantic Ocean, Nature, 473, 497-500, doi: 10.1038/nature10013.
Relevant projects:	EU Project: African Monsoon Multidisciplinary Analysis (AMMA), DFG Sonderforschungsbereich SFB754 “Climate – Biogeochemistry Interactions in the Tropical Ocean”, BMBF Trilateral (French/German/African) Projekt “Ecosystem Approach to the management of fisheries and the marine environment in West African waters (AWA)”, BMBF Verbundprojekt: WTZ Südliches Afrika – SPACES: SACUS (Southwest African Upwelling System and Benguela Niños), EU Project “Enhancing prediction of tropical Atlantic climate and its impacts (PREFACE)“
Selected commitments in community service
2006-2012: CLIVAR-TACE observations working group (chair) 2008-heute: PIRATA Scientific Steering Group (member) 2010-2013: Vice-President of the EGU Division on Ocean Sciences 2013-2017: President of the EGU Division on Ocean Sciences

Prof.	Øyvind Breivik, Ph.D., MET Norway, Bergen, Norway
short bio. ØB is a senior scientst at MET Norway and a professor at the University of Bergen. He is an expert on coupled wave-ocean modelling, air-sea interaction, wave climate and stochastic trajectory modeling. He has more than 20 years of experience with wave, ocean and climate models and was responsible for implementing wave effects in the coupled IFS-WAM-NEMO forecasts system of ECMWF (Breivik et al, 2015). He is currently involved in the WAVE2NEMO project funded by CMEMS where a regional WAM-NEMO model system is being developed (Staneva et al 2017). He has worked extensively on the impact of the Stokes drift on upper ocean mixing and the transport of substances and objects in the ocean surface boundary layer (Breivik et al, 2014, 2016, Jones et al 2016, Li et al, 2017).
Project role	Co-I, Ocean circulation (F1.2), Ocean waves (F1.3) and Air-sea interactions (F1.5)
Experience	2014 - present: Senior scientist, MET Norway, Bergen, Norway 2016 - present: Professor II, Geophysical Institute, University of Bergen 2012-2014: Scientist at the European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK 2011-2012: Associate Professor II, Geophysical Institute, University of Bergen 2001-2012: Scientist and since 2009 Senior Scientist (professor level), MET Norway, Bergen, Norway 1998-2000: Postdoc, NERSC, Bergen, Norway 1998: Dr Scient (PhD) in physical oceanography, Geophysical Institute, University of Bergen. 1995: Cand Scient (equivalent to M Sc) in physical oceanography, Geophysical Institute, University of Bergen
Relevant Publications (out of a total of 39)
Breivik, Ø, J-R Bidlot and PAEM Janssen (2016). A Stokes drift approximation based on the Phillips spectrum, Ocean Modell, 100, 49-56, doi:10.1016/j.ocemod.2016.01.005 Breivik, Ø, PAEM Janssen and J-R Bidlot (2014). Approximate Stokes Drift Profiles in Deep Water, J Phys Oceanogr, 44, 2433-2445, doi:10.1175/JPO-D-14-0020.1 Breivik, Ø, K Mogensen, J-R Bidlot, MA Balmaseda and PAEM Janssen (2015b). Surface Wave Effects in the NEMO Ocean Model: Forced and Coupled Experiments, J Geophys Res: Oceans, 120, 2973-2992, doi:10.1002/2014JC010565 Jones, C, K-F Dagestad, Ø Breivik, B Holt, J Röhrs, K Christensen, M Espeseth, C Brekke and S Skrunes (2016). Measurement and Modeling of Oil Slick Transport, J Geophys Res: Oceans, 121, 7759-7775, doi:10.1002/2016JC012113 Li, Q, B Fox-Kemper, Ø Breivik and A Webb (2017). Statistical Models of Global Langmuir Mixing, Ocean Modell, 113, 95-114, doi:10.1016/j.ocemod.2017.03.016
Relevant projects:	Wave2NEMO CMEMS Service Evolution project Partner in the Arctic Marine Forecasting Center of the CMEMS, http://marine.copernicus.eu/

Dr.	Bertrand Chapron, Ph.D., LOPS, Brest, France
short bio. B. Chapron graduated from Institut National Polytechnique de Grenoble, France, in 1984 and a Ph.D. degree in physics (fluid mechanics) from Aix-Marseille University, France, in 1988. He spent three years as a Postdoctoral Research Associate with the NASA Goddard Space Flight Center, Greenbelt, MD, and the Wallops Flight Facility, Wallops Island, VA. He is a Research Scientist at the Laboratoire d'Océanographie Physique et Spatiale (LOPS), Institut Francais de Recherche et d'Exploitation de la Mer (Ifremer). He has experience in applied mathematics, physical oceanography, and electromagnetic wave theory and its application to ocean remote sensing. He was the recipient of a russian Megagrant award in 2011 leading to the creation of the SOLAB remote sensing group in St Petersburg.
Project role	Co-I, Doppler & backscatter, current and waves algorithms
Experience	since 199? researcher at Ifremer, France 2001-2016 Head of Laboratoire d’Océanographie Spatiale, Ifremer. 2012-2014 Head of SOLAB, St Petersburg, Russia 2005-2008 Visiting professor, RSMAS, Miami, FL
Publication record
Citation report as of May 2017 (from ISI Web of science), for a total of 235 items: Sum of the Times Cited: 3634, Average Citations per Article: 15. h-index: 32 Four relevant papers: Chapron, B., H. Johnsen, and G. Garello (2001). Wave and wind retrieval from SAR images of the ocean, Ann. Telecommun., 56, 682–699. Chapron, B., F. Collard, and F. Ardhuin (2005). Direct measurements of ocean surface velocity from space: interpretation and validation, J. Geophys. Res., 110, C07008. doi:10.1029/2004JC002809. Chapron, B., Kerbaol, V., Vandemark, D., and Elfouhaily, T., Importance of peakedness in sea surface slope measurements, J. Geophys. Res., 105, C7, 17195–17202, 2000. Hasselmann, K., B. Chapron, L. Aouf, F. Ardhuin, F. Collard, G. Engen, S. Hasselmann, P. Heimbach, P. Janssen, H. Johnsen, H. Krogstad, S. Lehner, J.-G. Li, X.-M. Li, W. Rosenthal, and J. Schulz-Stellenfleth (2012). The ERS SAR wave mode: a breakthrough in global ocean wave observations, in ERS Missions: 20 Years of Observing Earth, 165–198, European Space Agency, Noordwijk, The Netherlands.
Relevant projects:	EnviWAVE, DUE Globwave, MPC Sentinel 1, GlobCurrent, SARONG

Prof.	Cédric Chavanne, Ph.D., ISMER / UQAR, Rimouski, Canada
short bio. Prof. Chavanne is a physical oceanographer whose research interests focus on mesoscale (tens to hundreds of kilometers) and submesoscale (hundreds of meters to tens of kilometers) oceanic motions. He uses observations and theoretical models to investigate the ocean dynamics at these scales and their effects on biological processes. One of his observational tools are high-frequency radars, which hourly map ocean surface currents with a resolution of a few kilometers over areas of thousands of square kilometers. He received his PhD from the University of Hawaii in the USA in 2007 where he studied the impact of mesoscale currents on the propagation of internal tides generated at the Kauai ridge. He then held a postdoctoral position at the University of East Anglia in the UK where he investigated the dynamics of the slope front in the southeastern Weddell Sea in Antarctica. Since 2011 he is Professor in physical oceanography at UQAR-ISMER.
Project role	co-I in ocean circulation
Experience	since 2011 Professor at ISMER-UQAR, Rimouski, Canada 2009-2011 Postdoctoral fellow at University of East Anglia, Norwich, UK 2007-2008 Assistant Researcher at University of Hawaii, Honolulu, USA
Publication record
Citation report as of June 2017 (from ISI Web of science), for a total of 20 items: Sum of the Times Cited: 261, Average Citations per Article: 13. h-index: 9 Selected relevant papers: Chavanne C., P. Klein (2016). Quasi-Geostrophic Diagnosis of Mixed-Layer Dynamics Embedded in a Mesoscale Turbulent Field. J. Phys. Oceanogr., 46:275-287. Chavanne, C., and P. Klein (2010). Can oceanic submesoscale processes be observed with satellite altimetry? Geophys. Res. Letters, 37:L22602. Chavanne, C., P. Flament, and K.-W. Gurgel (2010). Interactions between a submesoscale anticyclonic vortex and a front. J. Phys. Oceanogr., 40:1802-1818. Chavanne, C., P. Flament, D. Luther, and K.-W. Gurgel (2010). The surface expression of semi-diurnal internal tides near a strong source at Hawai`i. Part II: interactions with mesoscale currents. J. Phys. Oceanogr., 40:1180-1200.
Relevant projects:	NSERC (Canada, submesoscale ocean observatory), MEOPAR (Canada, surface drift forecasting)

Dr.	Sophie Cravatte, Ph.D., LEGOS, Toulouse, France
short bio. see http://www.legos.obs-mip.fr/members/cravatte Sophie Cravatte is a physical oceanographer, expert in ocean dynamics and climate variability in the Tropical Pacific. Her current research interests are on oceanic circulation variability in the SouthWest Pacific, and on the equatorial current system at surface, thermocline and intermediate levels, mainly from observations. She is the recipient of the 2011 “Christian le Provost” award from the French Science Academy. She is member of the TPOS2020 international project steering committee, aiming at redesigning the observing system in the Tropical Pacific.
Project role	Co-I, tropical Pacific circulation
Experience	Since december 2014: IRD (French Research Institute for Development ) Researcher at LEGOS 2011-2016: Researcher at IRD center in Nouméa, New Caledonia
Relevant Publications
Citation report as of May 2017 (from ISI Web of science), for a total of 31 items: Sum of the Times Cited: 931. h-index: 17 Selected relevant papers: 1 Cravatte, S., G. Madec, T. Izumo, C. Menkes, and A. Bozec (2007), Progress in the 3-D circulation of the eastern equatorial Pacific in a climate ocean model, Ocean Modelling, 17(1), 28-48 2-Cravatte, S., T. Delcroix, D. Zhang, et al. (2009), Observed freshening and warming of the western Pacific Warm Pool, Climate Dynamics (33), 565-589. 3- Cravatte, S., A. Ganachaud, Q. P. Duong, W. S. Kessler, G. Eldin, and P. Dutrieux (2011): Observed circulation in the Solomon Sea from SADCP data. Progress in Oceanography, 88, 116-130 4. Kessler, W. S. and S. Cravatte (2013), ENSO and Short-Term Variability of the South Equatorial Current Entering the Coral Sea, J. Phys. Oceanogr., 43, 956-969. 5. Cravatte, S., W. S . Kessler, N. Smith, S.E. Wijffels, and Contributing Authors (2016). First Report of TPOS 2020.GOOS-215, 200 pp.[Available online at http://tpos2020.org/first-report/.]
Relevant projects:	CLIVAR-SPICE: South Pacific Ocean and Climate Circulation Experiment Solwara: the Southwest Pacific and the Solomon Sea in the climate system ZEBRE: Observation of zonal jets in the Tropical Pacific Ocean
Selected commitments in community service
2014-present: member of the TPOS2020 steering committee, co-chair of the Backbone Task Team 2016-present: co-chair of the Earth Science commission of the French Research Institute for Development (IRD)

Dr.	Sophie Cravatte, Ph.D., LEGOS, Toulouse, France
short bio. see http://www.legos.obs-mip.fr/members/cravatte Sophie Cravatte is a physical oceanographer, expert in ocean dynamics and climate variability in the Tropical Pacific. Her current research interests are on oceanic circulation variability in the SouthWest Pacific, and on the equatorial current system at surface, thermocline and intermediate levels, mainly from observations. She is the recipient of the 2011 “Christian le Provost” award from the French Science Academy. She is member of the TPOS2020 international project steering committee, aiming at redesigning the observing system in the Tropical Pacific.
Project role	Co-I, tropical Pacific circulation
Experience	Since december 2014: IRD (French Research Institute for Development ) Researcher at LEGOS 2011-2016: Researcher at IRD center in Nouméa, New Caledonia
Relevant Publications
Citation report as of May 2017 (from ISI Web of science), for a total of 31 items: Sum of the Times Cited: 931. h-index: 17 Selected relevant papers: 1 Cravatte, S., G. Madec, T. Izumo, C. Menkes, and A. Bozec (2007), Progress in the 3-D circulation of the eastern equatorial Pacific in a climate ocean model, Ocean Modelling, 17(1), 28-48 2-Cravatte, S., T. Delcroix, D. Zhang, et al. (2009), Observed freshening and warming of the western Pacific Warm Pool, Climate Dynamics (33), 565-589. 3- Cravatte, S., A. Ganachaud, Q. P. Duong, W. S. Kessler, G. Eldin, and P. Dutrieux (2011): Observed circulation in the Solomon Sea from SADCP data. Progress in Oceanography, 88, 116-130 4. Kessler, W. S. and S. Cravatte (2013), ENSO and Short-Term Variability of the South Equatorial Current Entering the Coral Sea, J. Phys. Oceanogr., 43, 956-969. 5. Cravatte, S., W. S . Kessler, N. Smith, S.E. Wijffels, and Contributing Authors (2016). First Report of TPOS 2020.GOOS-215, 200 pp.[Available online at http://tpos2020.org/first-report/.]
Relevant projects:	CLIVAR-SPICE: South Pacific Ocean and Climate Circulation Experiment Solwara: the Southwest Pacific and the Solomon Sea in the climate system ZEBRE: Observation of zonal jets in the Tropical Pacific Ocean
Selected commitments in community service
2014-present: member of the TPOS2020 steering committee, co-chair of the Backbone Task Team 2016-present: co-chair of the Earth Science commission of the French Research Institute for Development (IRD)

Dr.	Frederic Dias, University College Dublin, Ireland
short bio. Frederic Dias graduated from Ecole Centrale de Paris, a French engineering school, in 1984. He received a PhD in Civil and Environmental Engineering from the University of Wisconsin, Madison, USA, in 1986. He started his career in the US before coming back to France to join CNRS in 1990. In 2000, he moved to Ecole Normale Superieure de Cachan and has been a Professor of Applied Mathematics since. In 2009, he went to University College Dublin (UCD) on leave to work on wave energy converters in close collaboration with industry. He is now leading a team of 12 people at UCD. In 2012, he received an advanced grant from the European Research Council (ERC) to work on extreme wave events. In 2014, he received a proof of concept grant from the ERC to work on wave measurement. His stay at UCD has been extended until 2019. In 2016, Prof. Frédéric Dias was elected as a member of the Royal Irish Academy. In 2014, Prof. Frédéric Dias has been awarded the Emilia Valori prize for applications of science by the French Academy of Sciences. Prof. Frédéric Dias is co-chief editor of the European Journal of Mechanics B/Fluids and has been Secretary General of the International Union of Theoretical and Applied Mechanics (IUTAM) from 2008 to 2016. Board appointments include: Scientific Council of the Fondation des Treilles, Technical Committee of Eurogia+, the EUREKA Cluster for low-carbone energy technologies.
Project role	Co-I, wave statistics and nonlinear wave evolution
Experience	since 2009 Professor, UCD, Ireland since 2000 Professor, ENS-Cachan, France
Publication record
Citation report as of June 2017 (Google scholar): Sum of the Times Cited: 6616, i10-index: 126, h-index: 43 Selected relevant papers: Gallagher S., Tiron R., Whelan E., Gleeson E., Dias F. & McGrath R., The nearshore wind and wave energy potential of Ireland: a high resolution assessment of availability and accessibility. Renewable Energy 88, 494-516 (2016) Gallagher S., Tiron R. & Dias F. (2014). A long-term nearshore wave hindcast for Ireland: Atlantic and Irish Sea coasts (1979-2012). Ocean Dynamics 64, 1163–1180. Fedele F., Brennan J., Ponce de Leon S., Dudley J. & Dias F. 2016 Real world ocean rogue waves explained without the modulational instability. Scientific Reports 6, 27715 Clancy C., O'Sullivan J., Sweeney C., Dias F. & Parnell A.C. 2016 Spatial Bayesian hierarchical modelling of extreme sea states. Ocean Modelling
Relevant projects:	FP7/ERC MULTIWAVE, FP7/ERC PoC WAVEMEASUREMENT, FP7 ASTARTE

Prof.	Dany Dumont, Ph.D., ISMER / UQAR, Rimouski, Canada
short bio. Prof. Dumont is a physical oceanographer doing research on coupled wave-ice-ocean numerical modeling and sea ice dynamics, including landfast ice formation and break-up, wave-interactions in marginal ice zones, and the representation of floe-size and thickness in numerical models. He has studied, using coupled sea ice-ocean numerical models, the phenomenon of ice arching in restricted channels. He has developed the first coupled wave-ice interaction model that is now being incorporated in WAVEWATCH III. He has participated to a number of ship-based scientific expeditions in the Arctic (Baffin Bay, Beaufort Sea, Laptev and Kara Sea) and in the Gulf of St. Lawrence, carrying sea ice-related field work. Since 2011, he developed innovative methods to study sea ice dynamics in a coastal environment, including the use of ice canoes and photogrammetric observatories enabling time-resolved observations of a number of ice mechanical and non-mechanical processes.
Project role	co-I in marginal ice zones: currents, ice & waves
Experience	since 2011 Professor at ISMER / UQAR, Rimouski, Canada since 2015 Leader of the MEOPAR Prediction Core and member Research Management Committee since 2016 Theme Director Réseau Québec Maritime (RQM) and Institut France-Québec Maritime (IFQM) 2009-2011 Postdoctoral fellow NERSC, Norway
Publication record
Citation report as of June 2017 (from ISI Web of science), for a total of 21 items: Sum of the Times Cited: 241, Average Citations per Article: 11. h-index: 9 Selected relevant papers: Dumont, D., A.L. Kohout and L. Bertino (2011) A wave-based model for the marginal ice zone including a floe breaking parameterization, J. Geophys. Res., 116, C04001, doi:10.1029/2010JC006682. Williams, T.D., L.B. Bennetts, V.A. Squire, D. Dumont and L. Bertino (2013) Wave-ice interactions in the marginal ice zone Part 1 : Theoretical foundations, Ocean Model., 71, 81-91, doi:10.1016/j.ocemod.2013.05.010. Williams, T.D., L.B. Bennetts, V.A. Squire, D. Dumont and L. Bertino (2013) Wave-ice interactions in the marginal ice zone Part 2 : Numerical implementation and sensitivity studies along 1D transects of ocean surface, Ocean Model., 71, 92-101, doi:10.1016/j.ocemod.2013.05.011. Lemieux, J.-F, B. Tremblay, F. Dupont, M. Plante, G.C. Smith and D. Dumont (2015) A basal stress parameterization for modeling landfast ice, J. Geophys. Res., 120(4), 3157–3173, doi:10.1002/2014JC010678 Kamli, E., C. Chavanne, D. Dumont (2016) Experimental assessment of the performance of high frequency CODAR and WERA radars to measure ocean currents in partially ice-covered waters, J. Atm. Ocean Tech., 33(3), 539-550, doi:10.1175/JTECH-D-15-0143.1.
Relevant projects:	FP7/SWARP, WIFAR, Improving Surface Drift Forecasts (MEOPAR), Experimental study of waves-in-ice in the St. Lawrence (FRQNT)

Dr.	Lucile Gaultier, OceanDataLab, France
short bio. Lucile Gaultier graduated from a general engineering school (ENSTA, Ecole Nationale des Techniques Avancées, with a specialization in environment) in 2009. She received her Phd in 2013 on Improving the estimation of ocean surface currents usage sea surface temperature and chlorophyll high resolution images. She spent two years at JPL (Jet Propulsion Laboratory, NASA / Caltech, Pasadena, US) working on using the synergy of remote sensing observation to improve the estimation of the surface of the ocean. She has also been involved in the scientific definition of SWOT mission, in charge of implementing a portable simulator of SWOT-like data for the scientific community. She is still maintaining the ocean SWOT simulator code. In late 2015, she has joined OceanDataLab and she is now a Research Engineer, working on improving the reconstruction of ocean surface current using various remote sensing data and Lagrangian diagnostics.
Project role	Co-I, L2 simulator tools
Experience	since 2016 Research Engineer, OceanDataLab, France 2014-2015 Post-Doct., Jet Propulsion Laboratory, NASA / Caltech, CA 2011-2013 PhD, Laboratoire des Ecoulements Géophysiques (LEGI), France
Relevant Publication
Gaultier L, Ubelmann C, Fu LL (2015) The challenge of using future swot data for oceanic field reconstruction. J Atmos Ocean Technol 33:119–126
Relevant projects:	ESA GlobCurrent, SWOT

Dr.	Christine Gommenginger, National Oceanography Centre, UK
short bio. Christine received a Diplome d'Etudes Approfondies in Electromagnetics, Telecommunications and Remote Sensing from the University of Toulon, La Garde, France-University of Nice Sophia Antipolis, Nice, France, followed by a Ph.D from the University of Southampton, Southampton, U.K on microwave radar remote sensing of the ocean at low grazing angles. She has worked at the National Oceanography Centre, Southampton, UK, for over 20 years. Her interests focus on active and passive microwave remote sensing of the ocean, understanding interactions of microwave signals with the ocean surface, remote sensing of ocean winds, waves, currents and sea level and developing new Earth Observation technologies and applications. Her work includes research in altimetry for surface winds and sea state, along-track and hybrid SAR interferometry for winds and currents, GNSS Reflectometry for surface winds and sea state, SAR altimetry, salinity from space with L-band radiometers and wide-swath ocean altimetry.
Project role	Co-I, Doppler & backscatter, current and waves algorithms
Experience	since 2012 Team leader for Satellite Oceanography, Marine Physics & Ocean Climate group, NOC-Southampton, UK 2005-2012 Senior research fellow, Southampton Oceanography Centre/NOC-Southampton 1998-2005 Research fellow, Southampton Oceanography Centre
Relevant Publications
Martin, A. C. H., C. P. Gommenginger, J. Marquez, S. Doody, V. Navarro, and C. Buck, 2016: Wind-wave-induced velocity in ATI SAR ocean surface currents: First experimental evidence from an airborne campaign. J. Geophys. Res. Oceans, 121, doi:10.1002/2015JC011459. Gommenginger, C. P., M. A. Srokosz, P. G. Challenor, and P. D. Cotton, 2003: Measuring ocean wave period with satellite altimeters: A simple empirical model, Geophysical Res. Lett., 30, doi:10.1029/2003GL017743. Kalantzi, G. D., Gommenginger, C., and Srokosz, M.: Assessing the performance of dissipation parameterisations in WAVEWATCH III using collocated altimetry data. J. Phys. Ocean, 29, 2800-2819, 2009. Foti, G., C. P. Gommenginger, P. Jales, M. Unwin, A. G. P. Shaw, Y. C. Robertson, and J. Rosello, 2015: Spaceborne GNSS-Reflectometry for ocean winds: First results from the UK TechDemoSat-1 mission. Geophys. Res. Lett., 42, 5435–5441, doi:10.1002/2015GL064204.
Relevant projects:	ESA Wavemill Product Assessment (WaPA), ESA SAMOSA, ESA COASTALT, ESA Cryosat-Plus for Oceans (CP4O), H2020 CEASELESS

Prof.	Johnny Johannessen, Ph.D., NERSC, Bergen, Norway
short bio. Prof. Johannessen has 35 years experience in satellite remote sensing in oceanography and sea ice research. In particular, he has focused on the use of synthetic aperture radar (SAR) to advance the understanding of mesoscale processes along the marginal ice zone and associated with open ocean fronts and eddies. In the last 10 years he has also been involved in development and implementation of operational oceanography both at national and international level. This has included a central involvement in the development and implementation of the European Marine Service under the Global Monitoring for Environment and Security (GMES, since 2013 renamed to Copernicus) initiative. The operation of the Arctic Modelling and Forecasting component of the Copernicus resides jointly between the Nansen Center and the Norwegian Meteorological Institute (met.no). Johannessen has authored/co-authored more than 150 scientific and technical publications, reports and book articles of which 85 papers are published (or in press) in International Review Journals and 22 are published in peer review book articles.
Project role	Co-I, current remote sensing
Experience	2010: Vice director Nansen Environmental and Remote Sensing Center 2008-2010. Research Director Mohn-Sverdrup Center at NERSC 1999-2007: Research Director in Ocean Remote Sensing Section, NERSC 2000: Appointed Professor 2 in Satellite Oceanography, Geophysical Institute, University of Bergen, Norway. 1994-1999: Head of Ocean and Sea Ice Unit, Earth Sciences Division, ESA/ESTEC, Noordwijk, The Netherlands. 1992-1994: Research Director, Marine Monitoring and Remote Sensing Unit, Nansen Environmental and Remote Sensing Center 1987-1991: Senior Scientist,NERSC, Bergen, Norway. 1988-1989: Visiting Scientist at Environmental Research Institute of Michigan 1983-1986: Research Scientist, Geophysical Institute, University of Bergen 1981-1982: Research assistant to Arctic Chair, Naval Postgraduate School, Monterey, CA 1979-1981: Research Assistant, Geophysical Institute, University of Bergen.
Relevant Publications
Selected relevant papers: Johannessen, J.A. et aL. (1987) Mesoscale eddies in the Fram Strait marginal ice zone during the 1983 and 1984 Marginal Ice Zone Experiments, J. Geophys. Res., 92, 6754. Godø, O. R. , A. Samuelsen, G. J. Macaulay, R. Patel, S. S. Hjøllo, J. Horne, S. Kaartvedt, J. A. Johannessen, (2012) Mesoscale eddies are oases for higher trophic marine life, Public Library of Science, PLoS ONE 7(1): e30161. doi:10.1371/journal.pone.0030161. ...
Relevant projects:	...
Selected commitments in community service
- Co-chairman of the Nansen-Tutu Center at University of Cape Town, South Africa. - Member of ESA Science Strategy Review panel (2015-2016) - Chairman of the ESA Science and Advisory Committee (2006-2010). - Member of the MyOcean Board (terminated in 2013) - Member of the Ocean Observing Panel for Climate (OOPC) under the Global Climate Observing System (GCOS). - Member of EuroGOOS board. - Member of GOCE Mission Advisory Group under ESA (up to 2012). - Member of Sentinel-1 Mission Advisory Group under ESA. - Member of EUMETSAT Advisory Expert Group for Ocean Topography and Ocean Imaging Satellite Missions.

Prof.	Alberto C. Naveira Garabato, Ph.D., University of Southampton, UK
short bio. A.Naveira Garabato has led a research group in Physical Oceanography for 11 years, currently with a Marie Curie research fellow, postdoctoral researchers and PhD students. He co-led a major U.K. – U.S. consortium (DIMES). Other recent highlights of Alberto’s work include the first observational assessment of the physical and biogeochemical exchanges in the Arctic Ocean and Weddell Gyre; detection of the sea level footprint of the contemporary acceleration in Antarctic glacial discharge; the observation of a seasonal cycle of submesoscale turbulence in the ocean surface boundary layer. Alberto has led 5 research expeditions as Principal Scientist and participated in other 7. He is presently leads an international project on small-scale dynamics of the Antarctic Bottom Water outflows. Alberto is the Director of UK NEXUSS Centre for Doctoral Training in the use of Smart and Autonomous Observing Systems (SAOS). He serves as a member of the NERC Peer Review College and the NERC Cruise Programme Review Group and is a member of the CLIVAR / CliC / SCAR Southern Ocean Panel since 2006, and of the Scientific Steering Committee of the Southern Ocean Observing System since 2012. He chaired the Steering Committee of the SCAR ‘Antarctica and the Global Climate System’ (AGCS) Research Programme 2008-2012. Alberto has been an active committee member and reviewer for the national environmental science agencies of Australia, Brazil, Canada, Chile, France, Germany, the Netherlands, New Zealand, Norway, South Africa, Spain, Sweden, the U.S. and the European Research Council.
Project role	Co-I, Ocean currents, wave-ocean Langmuir interactions.
Experience	1999 - 2002 Senior Research Associate, University of East Anglia 2002 - 2005 NERC Postdoctoral Research Fellow, University of East Anglia 2005 - 2010 NERC Advanced Research Fellow, University of Southampton 2007 - 2010 Principal Research Fellow, University of Southampton 2008 Visiting Associate Professor, Massachusetts Institute of Technology 2010 - 2011 Reader in Physical Oceanography, University of Southampton 2011 - present Chair in Physical Oceanography, University of Southampton 2013 Visiting Fellow, Institute of Marine and Antarctic Studies, University of Tasmania
Publication record
Citation report as of June 1 2017 (from ISI Web of science), for a total of 108 items: Sum of the Times Cited: 1425, Average Citations per Article: 22. h-index: 23 Four relevant papers: Thompson, A. F., A. Lazar, C. Buckingham, A. C. Naveira Garabato, G. Damerell & K. J. Heywood, 2016. Open-ocean submesoscale motions: A full seasonal cycle of mixed layer instabilities from gliders. J. Phys. Oceanogr. 46, 1285-1307. Buckingham, C. E., A. C. Naveira Garabato, A. F. Thompson, L. Brannigan, A. Lazar, D. P. Marshall, A. J. G. Nurser, G. Damerell, K. J. Heywood & S. E. Belcher, 2016. Seasonality of submesoscale flows in the ocean surface boundary layer. Geophys. Res. Lett. 43, doi:10.1002/2016GL068009. Bulczak, A. I., S. Bacon, A. C. Naveira Garabato, A. Ridout, M. J. P. Sonnewald & S. W. Laxon, 2015. Seasonal variability of sea surface height in the coastal waters and deep basins of the Nordic Seas. Geophys. Res. Lett. 42, doi:10.1002/2014GL061796. Rye, C., A. C. Naveira Garabato, P. R. Holland, M. P. Meredith, A. J. G. Nurser, C. W. Hughes, A. C. Coward & D. J. Webb, 2014. Rapid sea-level rise along the Antarctic margins in response to increased glacial discharge. Nature Geosci. 8, doi:10.1038/NGEO2230.
Relevant projects:	UK Natural Environment Research Council (NERC) projects: Next Generation Unmanned System Science (NEXUSS), NERC / EPSRC Centre for Doctoral Training; Mechanisms responsible for Mesoscale Eddy Energy Dissipation (MerMEED); Dynamics of the Orkney Passage Outflow (DynOPO); RidgeMix: A nutrient and carbon pump over mid-ocean ridges; Ocean2ice: Processes and variability of ocean heat transport toward ice shelves in the Amundsen Sea Embayment; Ocean Surface Mixing, Ocean Submesoscale Interaction Study (OSMOSIS); The Environment of the Arctic: Climate, Ocean and Sea Ice (TEA- COSI).

Dr.	Aylmer (George) Nurser, Ph.D., NOC, Southampton, UK
short bio. G. Nurser is a Principal Scientist at the National Oceanography Centre (NOC). He obtained an MA in Mathematics from the University of Cambridge, followed by a PhD in coastal oceanography from the University of East Anglia (UK). He has worked on fundamental thermocline theory, mixed-layer–thermocline interaction, watermass transformation theory, ocean surface mixed-layers, ocean ventilation and the theory and modelling of eddy transports of tracer and mass and their parameterisation. As a member of the CLIVAR Ocean Model Development Panel he worked on the major CORE-II ocean model intercomparison effort, and is currently involved with the UK CMIP6 effort. He has developed code for the NEMO ocean model, and is on the NEMO Wave Working Group. He has worked on tracking surface objects using NEMO in the context of the search for the crashed flights AF447 and MH370. He has been studying submesoscale flows as part of the UK OSMOSIS program. He is currently implementing into NEMO an ocean boundary layer model that takes account of Langmuir circulation, as part of his work on the OSMOSIS and EU PRIMAVERA programs.
Project role	Co-I, Ocean currents, wave-ocean Langmuir interactions.
Experience	1984–1991 Research Assistant/Fellow working in the Space and Atmospheric Physics Group, Imperial College, London 1991–1993 Research Fellow working at the Rennell Centre, Southampton 1993–present Member of staff in the Marine Systems Modelling division, at the National Oceanography Centre, Southampton
Publication record
Citation report as of June 1 2017 (from ISI Web of science), for a total of 64 items: Sum of the Times Cited: 1425, Average Citations per Article: 22. h-index: 23 Relevant papers: Nurser, A. J. G. (1996), A review of models and observations of the oceanic mixed layer, Internal Document 14, Southampton Oceanography Centre, European Way, Southampton SO14 3ZH. Ollitraut, M., B. Blanke, C. Chen, N. Diansky, M. Drevillon, E. Greiner, F. Lefevre, R. Limeburner, S. Louazel, G. Nurser, D. Paradis, and R. B. Scott (2010), Estimating the Wreckage Location of the Rio-Paris AF447, Tech. rep., Bureau d’Enquetes et d’Analyses pour la Securite de l’Aviation Civile. Brannigan, L., D. P. Marshall, A. Naveira-Garabato, and A. J. George Nurser (2015), The seasonal cycle of submesoscale flows, Ocean Modell., 92, 69–84, doi:10.1016/j.ocemod.2015.05.002. Buckingham, C. E., A. C. Naveira Garabato, A. F. Thompson, L. Brannigan, A. Lazar, D. P. Marshall, A. J. George Nurser, G. Damerell, K. J. Heywood, and S. E. Belcher (2016), Seasonality of submesoscale flows in the ocean surface boundary layer, Geophys. Res. Lett., p. 2016GL068009, doi:10.1002/2016GL068009.
Relevant projects:	UK Natural Environment Research Council (NERC) Project “OSMOSIS: Ocean Surface Mixing, Ocean Sub-mesoscale Interaction Study”; EU Horizon 2020 “PRIMAVERA”: a new generation of advanced and well-evaluated high-resolution global climate models.

Dr.	Melisa Menendez, Ph.D., EHI-UC. Santander, Spain
short bio. M. Menendez is a research scientist of the Environmental Hydraulics Institute (EHI), a joint research unit of the Universidad de Cantabria (UC). She received the Marine Science degree from the university of Cadiz, Spain (2001) and the Ph.D. degree in Marine Sciences and Technologies from the Civil Engineering School of UC, in Santander-Spain (2008). Her Thesis work was recognized by the University of Cantabria with the Outstanding Doctorate Award. Currently she is awarded by the Spanish RyC programme, aimed at encouraging the creation of permanent jobs for researchers with outstanding experience. She has been working on topics related to marine climate analysis and coastal applications. She focuses on meteo-oceanographic variables, mainly ocean waves and sea level dynamics. Her research topics are: met-ocean extreme events, wave modeling, development of mathematical-statistical tools for climate analysis and the assessment of climate change impact on the coast and marine structures. She has experience in the analysis of in-situ and satellite data sources as well as numerical modeling outputs. M. Menendez is member of COWCLIP (Coordinated Ocean Wave Climate Project) and GESLA (Global Extreme Sea Level Analysis project).
Project role	Co-I, Ocean waves and sea level, extreme analysis and coastal hazards.
Experience	since January 2016: Spanish national researcher, Ramon y Cajal programme 2011-2015: Permanent scientist researcher of EHI-UC, Santander, Spain 2007: Research Fellow of Scripps 'Institution of Oceanography' and the 'Hydrologic Research Center' of California, USA 2009: Research Fellow of the National Tidal and Sea Level Facility of the National Oceanography Centre of Liverpool, UK. 2005-2010 Junior to senior Scientist of the Universidad de Cantabria. Spain.
Relevant Publications
Citation report as of May 2017 (from ISI Web of science), for a total of 30 items: Sum of the Times Cited: 716, Average Citations per Article: 24. h-index: 15 Four relevant papers: 1. Woodworth, P. L., & Menendez, M. (2015). Changes in the mesoscale variability and in extreme sea levels over two decades as observed by satellite altimetry. Journal of Geophysical Research: Oceans, 120(1), 64-77. 2. Perez, J., Menendez, M., & Losada, I. J. (2017). GOW2: A global wave hindcast for coastal applications. Coastal Engineering, 124, 1-11. 3. Izaguirre, C., Méndez, F. J., Menéndez, M., & Losada, I. J. (2011). Global extreme wave height variability based on satellite data. Geophysical Research Letters, 38(10). 4. Sanchez-Vidal, A., Canals, M., Calafat, A. M., Lastras, G., Pedrosa-Pàmies, R., Menéndez, M., ... & Alcoverro, T. (2012). Impacts on the deep-sea ecosystem by a severe coastal storm. PLoS One, 7(1), e30395.
Relevant projects:	European advances on CLImate services for coasts and SEAs (ECLISEA, ERA4CS, JPI Climate, 2017-2020); Innovative Multi-purpose off-shore platforms: planning, Design and operation (MERMAID, FP7-OCEAN-2011, 2012-2016); Towards COast to COast NETworks of marine protected areas (from the shore to the high and deep sea), coupled with sea-based wind energy potential (CoCoNet, FP7-OCEAN-2011, 2012-2016); Marine Renewable Energies (MAREN1 and MAREN2, 2000-06 & 2007-13 Atlantic Area (AA) Programme). Innovative technologies for safer European coasts in a changing climate(THESEUS, FP7-THEME 6, 2009-2013)

Dr.	Pierre-Marie Poulain, Ph.D., OGS, Trieste, Italy
short bio. Dr. Pierre-Marie Poulain obtained his Ph. D in Oceanography from the University of California San Diego in 1989. After a postdoc at the University of Miami, he worked as a physical oceanographer for a NATO research laboratory, for the Naval Postgraduate School and finally for an Italian state-funded research institute (OGS). Since his graduate studies Dr. Poulain’s research activities have been focused on the monitoring of ocean circulation and water mass properties using mobile autonomous instruments, such as drifter, profiling floats and gliders. He has published the results on his studies on ocean dynamics in more than 100 research papers in peer-reviewed journals. At OGS, Dr. Poulain is coordinating the drifter and profiling float programs in the Mediterranean and Black Sea in order to guarantee an efficient and sustained observational array based on freely-drifting instruments. Dr. Poulain is an operational partner of the Global Drifter Program since its infancy in the late 1980’s. Dr. Poulain was recently appointed as the Chairman of the Management Committee of the Euro-Argo research infrastructure, an important member to the international Argo program. In addition, Dr. Poulain has some expertise in the field of HF radars and satellite passive and active remote sensing.
Project role	Co-I, surface drift, validation
Experience	2000-present: Senior Scientist in Department of Oceanography, Istituto Nazionale di Oceanografia e di Geofisica Sperimentale, Trieste, Italy. 2004-2010: Associate Affiliate Graduate Faculty, University of Hawaii at Manoa, Honolulu, Hawaii. 1996-2000: Assistant Professor of Oceanography, Naval Postgraduate School, Monterey, USA. 1992-1996: Scientist in Applied Oceanography Group, SACLANT Undersea Research Centre, La Spezia, Italy. 1989-1991: Postdoctoral Associate in Physical Oceanography, University of Miami, USA.
Selected Relevant Publications
Citation report as of June 2017 (from ISI Web of science), for a total of 64 items: Sum of the Times Cited: 2512. h-index: 27 Selected relevant papers: Poulain, P.-M., M. Menna and E. Mauri (2012) Surface geostrophic circulation of the Mediterranean Sea derived from drifter and satellite altimeter data. Journal of Physical Oceanography, 42, 973–990. Rio, M.-H., P.-M. Poulain, A. Pascual, E. Mauri, G. Larnicol and R. Santoleri (2007) A Mean Dynamic Topography of the Mediterranean Sea computed from altimetric data, in-situ measurements and a general circulation model. Journal of Marine Systems, 65, 484-508. Alpers W., Brandt P., Lazar A., Dagorne D., Sow B., Faye S., Hansen M. W., Rubino A., Poulain P.-M. and Brehmer P. (2013) A small-scale oceanic eddy off the coast of West Africa studied by multi-sensor satellite and surface drifter data. Remote Sensing of Environment, 129, 132-143. DOI 10.1016/j.rse.2012.10.032 Menna M. and Poulain P.-M. (2014). Geostrophic currents and kinetic energies in the Black Sea estimated from merged drifter and satellite altimetry data. Ocean Science, 10, 155-165, doi:10.5194/os-10-155-2014 Notarstefano G., E. Mauri and P.-M. Poulain (2006) Near-surface thermal structure and surface diurnal warming in the Adriatic sea using satellite and drifter data. Remote Sensing of environment, 101, 194-211.
Relevant projects:	Dynamics of Localized Currents and Eddy Variability in the Adriatic (2002 –2004); Office of Naval Research, GOCE Italy (ESA Endorsement: GOCE) (2009 –2012); Italian Space Agency

Dr.	Erik van Sebille, Ph.D., University of Utrecht, The Netherlands
short bio. E van Sebille is an Associate Professor in physical oceanography at Utrecht University. His research focusses on the transport of particulates by ocean currents. He has recently been awarded a European Research Council Starting Grant on a five-year project ‘Tracking Of Plastic In Our Seas’. He has worked with marine ecologists to map the risk of plastic to marine life, and has been part of the GESAMP Working Group on microplastics. He has testified for UK Parliament on the impact of microbeads on the marine environment, and the effects of a potential ban of these materials. In 2016, he won the Outstanding Young Scientist Award from the European Geosciences Union’s Ocean Division. https://www.erik.vansebille.com/science/
Project role	Co-I, surface transport, microplastics
Experience	since 2017: Associate Professor, oceanography and climate change, Utrecht University, the Netherlands. 2015-2017: Lecturer at the Grantham Institute & Department of Physics, Imperial College London, UK. 2011-2015: Lecturer and Research Fellow at the Climate Change Research Centre (CCRC), University of New South Wales (UNSW), Sydney, Australia. 2009-2011: Postdoctoral associate at the University of Miami, USA.
Relevant Publications
Citation report as of May 2017 (from ISI Web of science), for a total of 64 items: Sum of the Times Cited: 843. h-index: 17 Selected relevant papers: Doblin and Van Sebille (2016), Proc. Natl. Acad. Sci., 113, 5700-5705. Drift in ocean currents impacts intergenerational microbial exposure to temperature van Sebille, Erik; England, Matthew H.; Froyland, Gary (2012). Origin, dynamics and evolution of ocean garbage patches from observed surface drifters, Env. Res. Lett., 7(4), 044040. Hellweger, FL, E. van Sebille, N.D. Fredrick, (2014). Biogeographic patterns in ocean microbes emerge in a neutral agent-based model, Science, 345(6202), 1346-1349, doi:10.1126/science.1254421 van Sebille, E., S. Waterman, A. Barthel et al. (2015). Pairwise surface drifter separation in the western Pacific sector of the Southern Ocean, JGR-Oceans, 120(10), 6769-6781. van Sebille, E (2014), The oceans’ accumulating plastic garbage, Physics Today, 68, 60-61.
Relevant projects:	European Research Council H2020 Starting Grant “Tracking of Plastic In Our Seas” (TOPIOS): April 2017-March 2022.
Selected commitments in community service
Member of the UNESCO/UNEP Joint Group of Experts on the Scientific Aspects of Marine Environmental Protection (GESAMP) working group on marine pollution.

Dr.	Joanna Staneva, Ph.D., Helmholz Zentrum Geesthacht, Germany
short bio. Dr. Joanna Staneva (female) is a senior researcher and wave modelling theme coordinator of the Institute for Coastal Research, HZG, Germany Her field of experience is circulation and wave modelling, coastal ocean predictions, coupled model systems, modelling of marine environment, wave dynamics, coastal and regional oceanography. She received her PhD in Physical Oceanography (1998) and MSc., in Meteorology and Geophysics (1993) in the University of Sofia, Bulgaria. She is member of different editorial and scientific boards and reviewer of scientific programmers, projects (H2020, DFG, FP7) and journals. She is responsible for development of coupled model forecasting system at HZG and involved in model development for COSYNA (Coastal Observing System for Northern and Arctic Seas) system. She is also involved in development of tools based on coupled model system to assess the impact of different pressures (e.g. climate, anthropogenic) on ocean state (Black Sea, North Sea) and data assimilation for the coastal ocean forecasting systems.
Project role	Co-I ocean circulation, Ocean waves, including wave-current interactions
Experience	since May 2013 Senior researcher, Wave modelling topic Coordinator, Institute for Coastal research, HZG, Germany 2008-2013 researcher, HZG, Germany 2006-2008 University of Oldenburg, Germany
Publication record
Selected relevant papers: Alari V, Staneva J, Breivik O, Bidlot JR, Mogensen K and Janssen PAEM (2016). Response of water temperature to surface wave effects in the Baltic Sea: simulations with the coupled NEMO-WAM model. Ocean Dynamics, DOI 10.1007/s10236-016-0963-x Stanev E., Schulz-Stellenfleth J., Staneva J., Grayek S, Grashorn S., Behrens A, Koch W., and Pein J. (2016). Ocean forecasting for the German Bight: from regional to coastal scales, Ocean Sci., 12, 1105–1136, 2016, doi:10.5194/os-12-1105-2016 Staneva J., Alari V., Breivik O, Bidlot J.-R. and Mogensen K., (2016). Effects of wave-induced forcing on a circulation model of the North Sea. Ocean Dynamics, DOI 10.1007/s10236-016-1009-0. Staneva J, Wahle K, Koch W, Behrens A, Fenoglio-Marc L., and Stanev E., (2016). Coastal flooding: impact of waves on storm surge during extremes – a case study for the German Bight, Nat. Hazards Earth Syst. Sci., 16, 2373-2389, doi:10.5194/nhess-16-2373-2016. Wahle K., Staneva J, Koch W., Fenoglio-Marc L., Ho-Hagemann H., and Stanev E. (2017). An atmosphere-wave regional coupled model: improving predictions of wave heights and surface winds in the Southern North Sea. Ocean Sci., doi:10.5194/os-2016-51.
Relevant projects:	H2020 CEASELESS, CMEMS Wave2NEMO, CMEMS: BS-MFC, FP7: FIELD-AC, DANUBS, SESAME, PERSEUS, CoCoNET, myOcean, myWave, COSYNA, WIMO, Earth System Modelling

Dr.	Clément Ubelmann, Collecte Localisation Satellites, France
short bio. Clément Ubelmann Ubelmann is a research engineer at CLS, Toulouse in the Oceanography division. His main interests are the production of new high-level altimetry products (L3 and L4) and derived Ocean indicators. He is strongly involved in the preparation of future missions and investigate new reconstruction methods for fine scale sea surface height mapping.
Project role	Co-I, Level 3a/b reconstruction of velocity maps
Experience	since 2016 Research Engineer, CLS, France 2011-2015 Research Scientist, Jet Propulsion Laboratory, NASA / Caltech, CA 2009-2011 Post Doctorate Jet Propulsion Laboratory 2005-2008 PhD, Laboratoire des Ecoulements Géophysiques (LEGI), France
Relevant Publications: C.Ubelmann, B. Cornuelle and L-L. Fu : A dynamic mapping of along-track Sea Surface Height: method and results from Observing System Simulation Experiments, JTECH, 2016 C. Ubelmann, P. Klein and L-L. Fu: Potential perspectives of a dynamic interpolation scheme for future high-level ocean altimetry products, Journal of Physical Oceanography, 2014 G. Dibarboure and C. Ubelmann: Investigating the performance of four empirical cross-calibration methods for SWOT, Transactions on Geosciences and Remote Sensing (IEEE, TGRS), 2014. L-L Fu and C. Ubelmann: "On the transition from profile altimeter to swath altimeter for observing global ocean surface topography", 2013 JTECH
Relevant projects:	DUACS, SWOT

F.3 Institutions profiles

In alphabetical order except for that of the PI which is presented first.

The “Laboratoire d’Océanographie Physique et Spatiale” (LOPS) was formed in 2016, combining pre-existing physical oceanography and remote sensing teams in Brest, France. LOPS is a joint research unit, with personnel (140 total, 85 staff) and resources provided by Ifremer, CNRS, University of Brest and IRD. LOPS is very active in remote sensing (SMOS, Sentinel 1, CFOSAT, SWOT, ASCAT… ) with particular expertise in active and passive microwave methods for the analysis of wind, waves, currents, sea ice, salinity… Other activities include the development of numerical models (CROCO, WAVEWATCH III, MesoNH, NEMO) and their application in a coupled context, and sea-going activities with a strong focus on monitoring the North Atlantic thermohaline circulation, including the Argo program, and high frequency coastal dynamics.

The Bedford Institute of Oceanography (BIO) was created in 1962 by the government of Canada; it has grown since to become the largest oceanographic institute in Canada with more than 600 employees. BIO is a multidisciplinary institute that carries out research in biological, chemical, physical and geological oceanography. Science is the largest of the five branches that are grouped within BIO. Its mission is to address issues related to oceans, such as ecosystem management and fisheries.

CLS employs 650 people, at its headquarters in Toulouse and in its 25 other sites around the world. The company works in six strategic areas of activity: sustainable fisheries management, environmental monitoring, maritime surveillance, fleet management, energy & mining, space & ground systems. CLS process environmental data and positions, ocean and inland waters observations. In addition, CLS monitor land and sea activities by satellite. Rapid growth in recent years has motivated the CLS Group to set itself ambitious objectives through the opening up of new markets. Some hundred ocean specialists provide daily data to the international scientific community for measuring, observing, modelling and forecasting sea states both on the surface and subsurface.

The Dublin Institute for Advanced Studies (DIAS, www.dias.ie) is a statutory corporation established in 1940 under the Institute for Advanced Studies Act of that year. It is an Irish government publicly-funded independent academic centre for research within three schools, Theoretical physics, Cosmic Physics (Astronomy and Astrophysics, Geophysics) and Celtic Studies. The Geophysics Section’s main research areas are marine geophysics, global geophysical modelling, volcanoes and earthquakes and geophysical imaging. The institute has approximately 100 academic, research and support staff (about 40 in Geophysics) across three sites in Dublin city. It runs graduate programmes only and training through research is a core activity of the organisation. DIAS is also responsible for running the Irish National Seismic Network.

GEOMAR Helmholtz Centre for Ocean Research Kiel is one of the world’s leading institutes in the field of marine sciences. The institute investigates the chemical, physical, biological and geological processes of the seafloor, oceans and ocean margins and their interactions with the atmosphere. With this broad spectrum GEOMAR is unique in Germany. Additionally, the centre has successfully bridged the gap between basic and applied science in a number of research areas. GEOMAR is a public foundation jointly funded by the federal (90 %) and state (10 %) governments. GEOMAR has a staff of 1,000 (as of 31 March 2017) and a yearly budget of around 75 M€.

The scientists at the Helmholtz-Zentrum Geesthacht’s Institute of Coastal Research have made it their aim to understand and assess the diverse processes and interactions on the coast and to evaluate how the global coasts will develop in the future. The common goal is to contribute to a better understating of the natural processes and human influences on the coast by means of observations, analyses and modelling. Using such methods, the Institute of Coastal Research provides a scientific basis for sustainable development and future-oriented coastal management. Whether using satellites, wave measurement buoys or research vessels – the scientists at the Helmholtz-Zentrum Geesthacht always keep the coasts in focus. Their coastal observation system COSYNA (Coastal Observing System for Northern and Arctic Seas) is one the most comprehensive monitoring systems worldwide.

The Environmental Hydraulics Institute "IHCantabria" is a joint research center that carries out research, knowledge transfer and training in the various facets of the water cycle. IHCantabria is at the forefront of national and international organizations working in the fields of fresh and saltwater. Currently, IHCantabria employs over 140 researchers and the center has over thirty years of experience. The institute is fully involved with R&D Spanish or international programmes independently or jointly with universities, institutes and companies worldwide, with an intense publication activity in peer reviewed journals and international conferences.

ISMAR conducts research in mediterranean, oceanic and polar regions, focusing on the following themes: 1) the evolution of oceans and their continental margins, studying submarine volcanoes, faults and slides and their potential impacts onshore, 2) the influence of climate change on oceanic circulation, acidification, bio-geochemical cycles and marine productivity, 3) submarine habitats and ecology, and the increasing pollution of coastal and deep-sea environments, 4) the evolution of fish stocks with a view to keeping commercial fishing within sustainable limits and improving mariculture and aquaculture practices, 5) natural and anthropogenic factors impacting economically and socially on coastal systems from pre-history to the industrial epoch.

LEGOS is a research laboratory placed under the supervision of CNES, CNRS, IRD (Institut de Recherche pour le Développement), and Toulouse University. It is also part of OMP, a research observatory constituted of 6 laboratories in environmental sciences. LEGOS includes approximately 125 people with 72 permanent researchers and technical staff.

As the national meteorological and climate service, Meteo-France has several major missions: the development and maintenance of an observation network, the collection and processing of climatological data, weather forecasting, elaborating climate projections and lastly research and training in the different areas of meteorology and climate. These missions and activities are supported by an ambitious research and innovation policy. In coordination with their French and international partners, the Meteo-France research teams are working on developing future numerical weather prediction models, reducing uncertainties on climate scenarios and encouraging the development of an integrated system coupling meteorology and climate.

The Norwegian Meteorological Institute (MET Norway) is the national meteorological service of Norway and represents Norway in ECMWF, EUMETSAT, EUMETNET, WMO and other international forums. It takes part in numerous international projects on marine and atmospheric research, including climate change. The institute employs about 400 persons, among them 80 scientists in the Research and Development department, doing research within numerical weather prediction, ocean modelling, remote sensing, product development, instrumentation, climatology and climate research. The IT department employs 15 engineers experienced in the development and maintenance of IT infrastructure, extensive data bases and download services. MET Norway is responsible for the operational forecasts for ocean and wave products for CMEMS ARC-MFC as well as dissemination of sea ice and SST products through the satellite archiving and processing facility for ocean and sea ice (OSI-SAF).

National Technical University Athens (NTUA) puts education and scientific research on the same level of priority. Funded by NTUA’s own resources, by the General Secretariat for Research and Technology (GSRT) and other public bodies, by the European Union or by private sector companies in Greece and other countries. NTUA is home to 130 laboratories under the supervision of the academic staff. The total number of postgraduate students is 4.950. Of these, 1.950 are enrolled in an Interdisciplinary Postgraduate Studies Programme (I.P.S.P.) that leads to an M.Eng. while 3.000 are Ph.D. students. Among other areas of research at NTUA the following are identified: energy and the environment, marine renewable energy, green ships. The Ship and Marine Hydrodynamics Laboratory of NTUA, has been involved in research concerning wave modeling and wave transformation, coastal hydrodynamics, ocean wave statistics, stochastic modeling of wave climate, wave energy, remote sensing, marine geographical information systems. Also it maintains close collaboration with Institute of Oceanography of Hellenic Center for Marine Research for monitoring and understanding oceanographic information in the Aegean and Ionian Sea regions.

The National Oceanography Centre (NOC) is a world-leading oceanographic research centre, wholly owned by the Natural Environment Research Council (NERC) and employing about 500 staff. In 2010 NOC was merged with the NERC Proudman Oceanographic Laboratory at Liverpool. NOC provides long-term marine science capability which includes major research vessels facilities, sustained ocean observations, high-resolution global ocean modeling, data management and scientific advisory. The centre hosts the undergraduate and postgraduate programs of the University of Southampton and University of Liverpool, educating ca. 700 undergraduate and postgraduate students a year.

The Nansen Environmental and Remote Sensing Center was formally established on November 28th 1986 as a non-profit research foundation affiliated with the University of Bergen, after initiative from Ola M. Johannessen. The Founders were The Scientific Foundation at the University of Bergen (UNIFOB), GC Rieber & Co A/S, Bergen and Tenneco Oil Inc, Stavanger. The Nansen Center conducts basic research and environmental research projects funded by the research councils, space agencies, EU, national and international government agencies, industry and private donations.

OceanDataLab is a private research laboratory that was established in 2013 by long time Ocean Remote Sensing and IT experts working in close collaboration with IFREMER teams (now part of LOPS). Our main objective is to generate added value to scientific work, but also to add visibility and provide efficient tools to enhance scientific productivity when multi-sensor or multi-modal analysis is required.

OGS is an Italian public research institute studying marine environment, solid earth geophysics and seismology. It is composed of about 155 permanent staff. The Division of Oceanography, currently, has more than 60 researchers, technologists, technicians and administrative staff. The activities of the division range from scientific research to technological development and include experimental and theoretical oceanography, with particular reference to physical and biological oceanography, biogeochemistry, marine ecology and modeling of marine systems. More specifically, the expertise of the staff is divided in four areas: physical oceanography (experimental, autonomous systems, coastal); biogeochemistry; marine biology and modeling of marine system.

The Department of Hydraulic Engineering generates and disseminates academic knowledge in an internationally highly acknowledged Dutch tradition. The aim is to solve present-day social problems, such as the reduction of flood risks and the development of coastal zones. Not only typical lowland-related problems, such as dike failure and evacuation in case of floodings are dealt with, but also global phenomena, like sea level rise and highly populated coastal areas. Furthermore, the department treats the behaviour of fluids, offshore technology, design of hydraulic structures and logistics of harbours and waterways. To support finding innovative solutions, own advanced experimental facilities and computational models are being used.In an ever-changing environment our curiosity is triggered to study the underlying processes, in order to find sustainable solutions and to educate future generations. The department closely cooperates with industry. In many sections staff of various companies work also part-time for the department of hydraulic engineering.

IHE Delft Institute for Water Education is the largest international graduate water education facility in the world and is based in Delft, Netherlands. The Institute confers fully accredited MSc degrees, and PhD degrees in collaboration with partners in the Netherlands. Since 1957 the Institute has provided graduate education to more than 15,000 water professionals from over 160 countries. IHE Delft was formerly known as UNESCO-IHE Institute for Water Education, which was created in 2003 from the previous IHE. IHE Delft is instrumental in the strengthening of efforts by other universities and research centres in increasing knowledge and skills of professionals working in the water sector. The knowledge and services of IHE Delft in human and institutional capacity-building are vital to the achievement of Sustainable Development Goals, especially SDG 6 (providing access to clean water and sanitation).

Université du Québec à Rimouski (UQAR), located on the southern shore of the St. Lawrence Estuary, Québec, Canada, has developed and fostered excellence in multidisciplinary marine sciences since its foundation in 1970. Its leadership is today established through its Institut des sciences de la mer de Rimouski (ISMER) employing 23 professors, 12 research professionals and technicians and over 140 graduate students. ISMER owns the 50-m R/V Coriolis II and other research platforms to study coastal and estuarine processes, including high-frequency radars.

University College Dublin. Is the largest research and educational institution in Ireland. The academic environment is excellent. UCD is #1 for research in Ireland. Research and innovation are essential drivers of a dynamic economy, an informed society and a vibrant culture. The spectrum of research and innovation at UCD encompasses individual scholars, small, medium and large research groups, start-up companies and large-scale collaborations with industry and other partners. Research is conducted within each of the University's extensive range of disciplines, and in multidisciplinary research programmes addressing challenges of global scale.

Climate research at Exeter: Climate change and sustainability are likely to be the most significant environmental phenomena of the 21st century and, working closely with the Met Office, we are positioned at the forefront of climate change research. We have diverse expertise encompassing: Earth systems science, Climate Dynamics, Attitudes and behaviour, Ecosystem impacts, Environment and health.

Utrecht University. The mission of the Institute for Marine and Atmospheric Research Utrecht (IMAU) is to contribute to the basic science of the oceans, atmosphere and cryosphere. Education is an essential part of our mission and our researchers are therefore active teachers in the bachelor and master programs of our university. The IMAU was established on 24 September 1991. It is based in the Faculty of Science, department of Physics and Astronomy, with a significant contribution from the Faculty of Geosciences. We offer a wide range of first-class courses There are about 15 staff members available to run the undergraduate and graduate teaching programmes and to carry out research projects. Through the years, the IMAU has been successful in acquiring funds from outside the university, notably from NWO (Netherlands Organization for Scientific Research), SRON (Space Research Organization Netherlands), several ministries and the European Union. With these funds a large number of Ph.D.-students and post-docs.

Annex G : additional electronic information

G.1 Recent publications

At the time of writing, four publications related to SKIM are under review, and one is in press:

Ardhuin et al. : Measuring currents, ice drift, and waves from space: the Sea Surface KInematics Multiscale monitoring (SKIM) concept.

Nouguier et al. : Sea surface velocities from near-nadir radars.

Ubelmann, C., F. Ardhuin, G. Dibarboure and L. Gaultier “Mapping the data of the proposed SKIM mission into surface current estimates with a Multivariate Optimal Interpolation scheme”

Peureux, C., A. Benetazzo, and F. Ardhuin “Note on the directional properties of meter-scale gravity waves,” Ocean Science Discussions, submitted.

Ardhuin, F., N. Rascle, B. Chapron, J. Gula, J. Molemaker, S. T. Gille, D. Menemenlis, and C. Rocha (2017). Small scale currents have large effects on wind wave heights, J. Geophys. Res., 122, 2017. in press. doi:10.1002/2016JC012413.

These manuscripts been posted on Arxiv or ResearchGate. The first and last are in the open review journal “Ocean Science Discussions”, and will appear shortly with a proper doi.

G.2 Modeled “truths” and simulated SKIM L2 / L3 products

Simulated currents and waves used for L2, L3 and L4 product definition

ftp://ftp.ifremer.fr/ifremer/ww3/HINDCAST/OTHER/SKIM/

G.3 Other links

SKIM web page

http://www.umr-lops.fr/Projets/Projets-actifs/SKIM

SKIM ResearchGate page

https://www.researchgate.net/project/SKIM-for-EE9-the-Sea-surface-KInematics-Multiscale-monitoring-mission

SKIM Facebook page

https://www.facebook.com/SKIM4EE9

Annex H: Accompanying letter from the French national space agency