WP 10 Consolidation of the requirements CLS team: F. Soulat, G. Dibarboure and A. Ollivier

1. WP 10 Consolidation of the requirements • CLS team: F. Soulat, G. Dibarboure and A. Ollivier • E. Cardellach, J. Wickert, L. Bertino, A. Camps, N. Catarino, • B. Chapron, G. Foti, C. Gommenginger, H. Park, A. Rius, • M. Semmling, A. Sousa, J. Xie

2. Outline • GEROSS-ISS Scientific Objectives • User Requirements: a Critical Review • Simulator Requirements Overview 2

3. Scientific Objectives  Characterizing surfaces on Earth: • Ocean /atmosphere interface • Ocean topography • Ocean roughness • Cryosphere • Land/soil moisture 3

4. Ocean Topography (1/2) • Global ocean circulation • Global ocean surface velocities are now routinely estimated from precise altimeter measurements. • Spatial scales larger than 200 km (large distance between RA tracks). • Limits in the quantitative reconstruction of the velocities field using exclusively existing altimeters (errors in the accurate location of currents). • The objective is to quantitatively contribute to fill-in the 100 km “altimetry temporal gap” by combining lower resolution conventional altimeter data with GNSS-R measurements much denser in time. Cyclonic eddy (Gulf Stream) white tracks: float (14 days) color scale: SLA + MDT A. Pascual, Y. Faugère, G. Larnicol, and P. Y. Le Traon. Improved description of the ocean mesoscale variability by combining four satellite altimeters. Geophys. Res. Lett, 33(2):611, 2006. 2 satellites 4 satellites 4

5. Ocean Topography (2/2) • Extremeevents • Storm surge height & tropical cyclones • SSH anomalies can often reach several tenths of cm (above the GNSS-R altimetric uncertainty). • Taking benefit from the unique L-band rain-free capability and high temporal sampling. • Current scatterometers and radiometers affected by rain events, with relatively bad coverage of such events. 5-day sampling of the North Atlantic from one LEO receiver and the 3 constellation reflections (GPS, Galileo, Inmarsat), 500 km polar orbit and a 50° field of view. 5

6. SSH Surface Wakes of Igor Y. Quilfen / Ifremer 6

7. Ocean Roughness • Wind field & MSS • Extreme event monitoring (storm surge & tropical cyclones) • Wind temporal variability not caught. • Wind ringing effect: • HF winds impacting the inertial motions and large-scale oceanic circulation. • Impacts are unknown: internal tides, thermocline, … • Current limitations: • Analysis and monitoring not allowed with conventional RA sampling. • Most measurements corrupted by rain. • Better quantify the atmosphere-ocean coupling, as a complement to ocean winds measurements and wave models. • E.g., momentum and energy fluxes under extreme conditions for hurricane modeling. • Support to L-band radiometric missions (SMOS, Aquarius, …). • To separate roughness and salinity contributions to L-band radiometric measurements. 7

8. Cryosphere • Monitoring the Poles • Supporting the improvement of the existing techniques dedicated to the estimation of geophysical parameters: • ice extent / thickness • snow surface roughness (and internal interfaces) • snow depth and layer information • firn parameters (accumulation rates) • Cryospheric information could be complementary to those derived by current and forthcoming L-band spaceborne radiometers (SMOS, Aquarius) and by other sensors (TerraSarX, CosmoSkymed, Sentinel-1). 8

9. Land • Soil moisture • Relevant to study the water cycle, providing boundary conditions to numerical weather prediction models, hydrological models and prediction of flooding events. • Magnitude as function of the surface permittivity, which is in turn related to the moisture content in case of a bare soil surface. • Many experimental campaigns demonstrated that GNSS-R signals can be detected over land, and correlated to soil moisture [Masters et al., 2004], [Pierdicca et al., 2011], [Rodriguez-Alvarez et al., 2009], [Zavorotny and Voronovich, 2000]. • Complementarity of the GNSS-R observations with radiometric missions. 9

10. Mission Requirements Review • Ocean topography: SSH requirements (MRD) • Measurement performance • “Accuracy” of 50 cm • Sampling (along-track): 10 km (XT) x 100 km (AT) • Revisit time: 4 days • Ocean scale target: 10 – 100 km • Product latency: N/A • SSH uncertainty definition: the bias and noise levels should be clarified • Accuracy and precision should be differenciated. • Ex: 3 cm noise on a Jason-like product addresses instrumental and range errors, geophysical corrections, … 10

11. Mission Requirements Review • Oceantopography: SSH requirements (MRD) • Current altimeter constellation • Can only resolve the ocean circulation at resolutions larger than 200-300 km [Lee-Lueng Fu et al., 2012; Chelton et al., 2011]. • Scales of interest • GEROS_ISS observations shall address the dynamics of ocean variability at scales ranging from 50 to 300 km at the minimum and up to 1000 km, the mesoscale and sub-mesoscale processes, such as the formation, evolution and dissipation of eddy variability (including narrow currents, fronts and quasi-geostrophic turbulence) and its role in the air-sea interaction. The requirements for wavelengths ranging from 300 to 1000 km and above guarantees that mesoscale observation is not corrupted by spectral leakage from longer wavelengths. The GEROS-ISS error budget should be decomposed into a noise term and a bias term, both of which are needed to merge SSH data from GEROS-ISS and radar altimeters through a mapping or assimilation process. 11

12. Mission Requirements Review • Ocean topography: SSH spectrum The assessment of the error budget can be addressed through the error level estimation at different ocean wavelengths, i.e., through the spectral allocation of the measurement error. Along-track resolving capability defined as the intersection of the SSH spectrum with the noise error spectrum. SSH Power Spectral Density Spectral noise level wavenumber 12

13. Mission Requirements Review • Oceantopography: reaching 100 km scales - From what we know • RA: 3 cm noise  spectral noise floor of 150 cm2/cy/km • 100 km resolving capability  spectral noise floor of 300 cm2/cy/km  Measurement error ~0.25 cm @ 50kmx50km - GEROS-ISS MRD • Error: 50 cm @ 10kmx100km  Equivalent error ~32 cm @ 50kmx50km - GARCA study • This illustrates the need for differentiating bias- which can be calibrated, or post-processed - and noise- which is a strong limiting factor to observe the smaller scales - when refining the performance of the GEROS-ISS mission. 13

14. Mission Requirements Review • Ocean topography: revisit time • Expected good temporal revisit of 4 days. • Considering the worst case (precision=50 cm), the use of 3 successive measurements would allow to monitor ocean structures of strong variability and considered steady within 12 days. • Acceptable noise reduced to 50/sqrt(3) = 17 cm  we would already monitor fairly well the 1-sigma dynamics in turbulent regions such as western boundary currents, or 3-sigma structures in other areas. Distribution of the ocean mesoscale variability [Dibarboure et al., 2012] 14

15. Mission Requirements Review • Ocean roughness We would recommend that the Level-2 standard data products also include the radar σ0 measurements on the same grid as the SSH measurements. 15

16. Mission Requirements Review • Data Products • Altimeter data products should be consistent with the Jason-series Geophysical Data Records (GDR’s). • Missing items: • MSS (and MDT), bathymetry model • Wind from ECMWF • TBD: dataused for CalVal, SSH from AVISO • SLA The SLA contains information about: • Real changes in ocean topography related to ocean currents • Dynamic response to atmospheric pressure • Differences between the mean sea surface model and the true mean sea surface at the measurement locations • Unmodeled or mismodeled measurement effects (skewness, sea state bias, altimeter errors, tropospheric corrections, ionospheric correction, etc.) • Orbit errors  16

17. Mission Requirements Review • Product latency • No requirement for offline demonstrations. • Less than 5 days (goal: 48h) for near real time assimilation experiments. 17

18. GEROS-ISS Simulator • General requirements • Orbit and geometry • Instrument • Surface scattering • Altimetry • Scatterometry • Existing Building Blocks • GAT tool • P2EPS • Validation test plan • GEROS-SIM • Retrieval algorithms 18

19. GEROS-SIM Requirements • General requirements: • GUI web based • GUI integration in remote devices, like desktops • Integrated editor of variables • Password protected access with roles to classify the functionalities • The use of standard file formats (i.e. netcdf) 19

20. Orbit and Geometry Requirements GEROS-SIM shall... 20

21. 21

22. Instrumental Features GEROS-SIM shall... 22

23. Instrumental Features 23

24. Surface Scattering GEROS-SIM shall... 24

25. Other Systematic Effects GEROS-SIM shall... 25

26. Altimetric Retrieval GEROS-SIM shall... Brief descriptions of selected, LED and phase-delay, retrieval algorithms provided in GARCA TN-1 26

27. Scatterometric Retrieval GEROS-SIM shall... A brief review of the scatterometric algorithms reported in the literature is provided in GARCA TN-1 27

28. Existing Building Blocks • Instrument-to-L1 : P2EPS (PAU/PARIS End-to-end Performance Simulator) • Orbits and geometry : GAT (Geometry Analysis Tool) • L1-to-L2 retrieval algorithms 28

29. GPS: L1 → C/A, P, M and interferometric, L5 → C/A, P, M and interferometric Galileo: E1 → B, C, A+B+C interferometric, E5 → interferometric Beidou: B1I Ref.[B13] (optional) Feature: Existing core simulator: Transmitted signals Antenna phased array YES, by default hexagonal geometry as in PARIS IoD with configurable number of elements, other topologies can be easily programmed. Elementary antenna pattern YES, configurable by either providing a mathematical model including deviations among elements, or can be input from a file. Platform attitude and inter-antenna offset projections YES, provided platform attitude from Geometry Tool. Receivers' frequency response YES, receivers’ topology is arbitrary. It follows SMOS and PARIS IoD elementary receivers topology, which is quite generic and includes all type elements. From the frequency response computed mathematically or loaded from a file, the fringe-washing function can be computed. It can be modified if needed. Existing Building Blocks • Instrument-to-L1 : P2EPS (PAU/PARIS End-to-end Performance Simulator) 29

30. Instrument-to-L1 • Instrument-to-L1 : P2EPS (PAU/PARIS End-to-end Performance Simulator) 30

31. Other Existing Building Blocks 31

32. Validation Test Plan • The core simulator has been widely validated, including comparisons with UK-DMC data. • Further validation is foreseen using some of the data sets from the list below*: • UK-DMC data sets • UK-TDS1 TBC (?) • Interferometric GNSS-R (iGNSS-R) from PIR ground-based and PIRA flights • GOLD-RTR data sets (more than 40 flights and several ground-based campaigns available) • GFZ data sets (GEO-HALO airborne campaigns, ZOIS Zeppelin airship data, ASIRIS, Ny Ålesund ground-based) • Beidou B1 clean-replica GNSS-R data from TIGRIS campaign • Interferometric and conventional data from SPIR flights (April 2015) *TN-1 includes a more detailed list of data sets, organized according to the aspect to be validated 32

33. Validation Test Plan • These data sets are suitable to check and validate different aspects of the GEROS-SIM modelling: • TRANSMITTER AND MODULATION TYPE • NOISE FIGURES, DDM COVARIANCES, DDM TIME CORRELATION • POLARIMETRIC CO- AND CROSS-POLAR SCATTERING OVER OCEAN • POLARIMETRIC CO- AND CROSS-POLAR SCATTERING OVER SEA-ICE • POLARIMETRIC CO- AND CROSS-POLAR SCATTERING OVER LAND • ANTENNA-ARRAY AND BEAM-STEERING RESPONSE • COHERENT AND INCOHERENT COMPONENTS • PHASE-DELAY OBSERVABLES 33

34. Validation Test Plan • GENERAL APPROACH FOR ALGORITHM VALIDATION: To measure the actual difference between the simulated observables (of well-known ground-truth) and the output of the retrieval algorithm. This is, the difference between Module-N output (retrieval algorithm module) and input to Module-2 (instrumental to L1-data module). This methodology enables assessing the degradation of the algorithm as a function of different parameters, as well as the absorption of inaccurate systematic error corrections. 34