1. Estimation of wave spectra with SWIM on CFOSAT – illustration on a real case C. Tison (1), C. Manent (2), T. Amiot (1), V. Enjolras (3), D. Hauser (2), L. Rey (3), P. Castillan (1) (1) CNES, « Altimetry and Radar » department, France (2) UVSQ, CNRS, LATMOS-IPSL, France (3) Thalès Alenia Space, France celine.tison@cnes.fr

2. IGARSS’11 – July, 2011 2 Overview of the presentation ■SWIM instrument and measures ■Performance budget  SimuSWIM – an end-to-end simulator  A real sea state condition  Results

3. IGARSS’11 – July, 2011 3 The CFOSAT mission ■Status of the program:  Conception and Development phase  Launch planned end of 2014 ■SWIM  Measurement of the oceanic wave properties  Real-aperture radar with 6 beams (Ku band) ■SCAT  Measurement of wind sea surface  Real-aperture radar (bi polar, Ku band) ■KuROS  Airborne sensor developed by LATMOS  Validation of SWIM and SCAT More on the CFOSAT mission tomorrow – session WE4.T10 Altimetry I China France Oceanography SATellite

4. IGARSS’11 – July, 2011 4 SWIM instrument (1/2) SWIM: Surface Wave Investigation and Monitoring Ku-band radar (scatterometer) 6 beams

5. IGARSS’11 – July, 2011 5 SWIM instrument (2/2) Wind sea Swell  Nadir signal  SWH and wind speed - Accuracy SWH: max(10% of SWH, 50 cm) -Accuracy wind speed: 2 m/s  10°, 8° and 6° beams  wave spectrum - spatial sampling of 70 x 70 Km² - Detectable wave wavelength : λ ~ [70 - 500] m - Azimuth accuracy: 15° - Energy accuracy: 15%  All beams  backscattering coefficient profiles: - Absolute accuracy < +/- 1 dB - Relative accuracy (between beams) < +/- 0.1 dB

6. IGARSS’11 – July, 2011 6 Estimation of wave spectra Wave topography: ξ(x,y) Directional wave spectrum F(k x,k y ) Modulation of the backscattering coefficient Signal modulation Modulation spectrum P m Received power Wave slopes Link slope/signal modulation

7. IGARSS’11 – July, 2011 Simulations ■Simulations from the sea surface to the estimated signal  Input = sea state conditions  Output = wave spectrum  computation of backscattered intensity and processing similar to the future ground segment  use SWIM parameters End-to-end simulation tool: SimuSWIM 0°2°4°6°8°10° Max integration time (ms)51.831.5 22.530.137.4 Bc (MHz)320 PRF (Hz) 2125 fixed 6407-6739 variable 6378-6707 variable 6340-6667 variable Nimp (fixed)11060 144192237 SNR (dB)24.311.98.76.84.82.3

8. IGARSS’11 – July, 2011 8 Simulations End-to-end simulation tool: SimuSWIM Input spectrum (models, measurements) Surface computation Backscattered signal (knowing SWIM geometry and properties) At a given azimuth direction: -Computation of the N imp backscattered pulses 2 options: 1. Computation of the N imp pulses (with geometrical migrations and noises for each) 2. Computation of one pulse and additions of noise (thermal+speckle) to create the N imp pulses with central migrations -Addition of the N imp signals N imp pulses per cycles

9. IGARSS’11 – July, 2011 9 Simulations End-to-end simulation tool: SimuSWIM Input spectrum (models, measurements) Surface computation Backscattered signal (knowing SWIM geometry and properties) Estimated modulation spectrum Quality criteria

10. IGARSS’11 – July, 2011 10 Impact of migrations (1/2) N imp N imp /2 NR ER MR FR Δx MR Due to satellite advection and antenna rotation:  MIGRATIONS range (of each target) different at each impulse -3 kinds of migration: -Centre migration -Migration along elevation -Migration along wave front -NB: at the cycle scale, no impact of antenna rotation Corrected by chirp scaling Non correctable Two ways of simulation (for computation time constraints): 1.With only central migrations and elevation migrations 2.With all migrations

11. IGARSS’11 – July, 2011 11 Impact of migrations (2/2) (a) Reference 2D modulation spectrum (b) Estimated 2D modulation spectrum WITHOUT complete migration (c) Estimated 2D modulation spectrum WITH complete migrations DirectionWave- length Energy Swell0° 0% 3% 13% 10% Wind sea11° 14° 27% 16% 19% 12%

12. IGARSS’11 – July, 2011 A real sea state condition: “Prestige case” ■Case of November, 2002 storm in Atlantic ocean  Lead to the sinking of the Prestige (oil tanker) ■Very different conditions during the day  00:00: low wind sea + dominant swell  06:00: very young wind sea (high wind) + dominant swell  08:00: mature wind sea + dominant swell  15:00: crossed wind seas (old + young)  Wind sea rotated by about 120° Spain Galician coast © BSAM/Douanes françaises

13. IGARSS’11 – July, 2011 A real sea state condition: “Prestige case”

14. IGARSS’11 – July, 2011 A real sea state condition: “Prestige case” ■Available data  MFWAM output with ALADIN winds (Météo France models of wind and waves)  2D polar azimuth/frequency height spectrum  converted into 2D cartesian wavenumber by bilinear interpolation  Subset of results: 00, 06, 08, 10, 15 UTC (different wind and waves cases) ■Simulation conditions  Incidence angle: 10°  N imp = 237 pulses per cycle (  averaging for noise reduction)  Rotation speed = 5.7 rpm (  49 cycles / 360°)

15. IGARSS’11 – July, 2011 Simulation results 06:00 UTC Reference: 2D spectrum from WAM model CFOSAT/SWIM estimation (simulations from SimuSWIM)  Same detection of swell and wind sea partitions  Small underestimation

16. IGARSS’11 – July, 2011 1D modulation spectra 6h UTC Swell Φ=135° (SE-NW look direction) Sea wind Φ = 235° (NE-SW look direction) Φ = angle between satellite track (assumed S-N) and radar look direction

17. IGARSS’11 – July, 2011 00:00 UTC06:00 UTC08:00 UTC15:00 UTC10:00 UTC Reference: 2D spectrum from WAM model CFOSAT/SWIM estimation (simulations from SimuSWIM) Hs: 6.5 m U: 17.3 m/s Hs: 6.1 m U: 8.8 m/s Hs: 5.8 m U: 22.2 m/s Hs: 5.1 m U: 11.7 m/s Hs: 6.5 m U: 21.0 m/s Prestige SOS 14:00 UTC

18. IGARSS’11 – July, 2011 Performance quality SWELLWIND SEA Φ λE Φ λE 00h3°0%4%--- 06h0°0%13%11°27%19% 08h0°1%7%11°18%6% 10h0°8% 11°6%7% 15h3°12%6%7°19%5% Estimation errors on wave direction ( Φ), wave wavelength ( λ) and energy (E): <15° <10-20% <15% Requirements:

19. IGARSS’11 – July, 2011 Conclusions ■Simulations of SWIM wave products  End-to-end simulations  Software with realistic sensor conditions  Accurate results with a large variety of sea state conditions ■Next steps  Keep-on the definition of the inversion algorithms  Optimize inversion up to wave spectrum  estimation of the transfer function (α)