ASAR Wave Mode Level 2 Product Validation Harald Johnsen, G. Engen, Bertrand Chapron*,

1. ASAR Wave Mode Level 2 Product Validation Harald Johnsen, G. Engen, Bertrand Chapron*, Yves-Louis Desnos,** Nick Walker***, Josep Closa ****, Norut IT, Ifremer*, ESA**, SERCO spa for ESA/ESRIN ***, Altamira for ESA/ESRIN****

2. Objectives Introduction Level 2 Algorithm and Product Description Validation Approach Validation Results Summary Content

3. To perform a geophysical validation of ASAR Wave Mode Level 2 product against independent co-located wave spectra and wind measurements. To establish RMS errors and range of validity of wind and wave parameters extracted from the ASAR Wave Mode Level 2 product. To optimise the processor settings of the Level 2 processor Objectives

4. Introduction • Achievements with ERS Wave Data: • Analytic Ocean-to-SAR-transform(Hasselmann et al, 1991 JGR), (Krogstad, 1997 JGR) • Different inversion schemes exist (Hasselmann et al, 1996 JGR, Engen et al, 1994 TGARS, Krogstad et al, 1994 Atm.Ocean, Mastenbroek 1999 JGR) • Assimilation experiments performed (Breivik et al, 1998 JGR),(Hasselmann et al, 1997 JGR) • Spectral wave climate applications (Mastenbroek, CEOS SAR, 1998) • Cross spectra inversion developed (Engen et al, 1995 TGARS), (Dowd et al, 2001 TGARS) • Successfull implementation and performance of Level 1 X-spectra product (Johnsen et al, 1998 CEOS SAR 1998) • Main conclusions: • Assimilation works well in near real time framework • Independent information in the swell part of SAR wave spectra • Positive impact of assimilation in individual cases on swell part of spectra • Insignificant averaged impact Needs: • Better data coverage • Better processed and calibrated data • Better assimilation methods

5. Calibration and radar cross section estimation (RCS) Image detrending Equalize fourier domain, extract 3 looks and compute co- and cross-spectra Compute and remove speckle bias from co-spectra Estimate wind speed using RCS, CMOD and model wind direction Estimate wave age from azimuth spectral width Use look-up table & wind field to remove non-linear contribution in spectra, and solve quasi-linearily the remaining part Level 2 Algorithm and Product Description Level 1 Product (SLC) Processing Set-Up File Look-Up Table Radar Cross Section Estimation Cross Spectra Estimation Wind Speed Retrieval Clutter & SNR Estimation Ocean Wave Spectra Retrieval Level 2 Product Generation Level 2 Product

6. Speckel Bias Removal from Co-Spectra Spectral Model:

7. Input Co- and Cross-Spectra SAR Ocean Image

8. Inversion Model &Algorithm where

9. Level 2 Product • SAR ocean wave spectra on log-polar grid (24 wavelengths, 36 directions) geographical oriented, • Wind speed, wave age, ”wind direction” • Additional parameters (radar cross section, azimuth cut-off, orbital velocity variance, non-linear spectral width, confidence measure, image mean and variance, SNR)

10. Co-locate ASAR WM Level 2 product with buoy measurements and/or model predictions of wave spectra and wind field, over different climate regions. Compute integrated wave spectral parameters from co-located spectra and perform comparison. Establish RMS errors and range of validity of wind and wave parameters extracted from the ASAR Wave Mode Level 2 product. Validation Approach

11. Heave Spectra: Directional Spectra: Mean Wave Direction Spectra: Significant Waveheight: Mean Wave Period: Mean Wave Direction: Selected Parameters

12. Orbital Velocity Variance/Azimuth Cut-Off: Wind Speed: Performance Measures: Bias – mean difference Root mean square difference Standard deviation Correlation

13. Validation Results 1 D- Spectra

14. Significant Waveheight

15. Significant Waveheight

16. Mean Wave Period

17. Mean Wave Direction

18. Wind Speed

19. Orbital Velocity Variance/Azimuth Cut-Off

20. WAM – ASAR Level 2 Summary

21. ASAR Level 2 product validated using co-located WAM spectra. Spectral parameters computed and RMSand Bias values established. Conclusions so far: Similar performance as was achieved from ERS Wave Mode data processed into ASAR Level 2 product (Johnsen, et. Al., 2002, Proc. EUSAR 2002) Best agreement in mean wave period, but small positive bias Largest spread in mean wave direction, but no bias observed Slightly saturation in Hs at higher wind speeds Next: Add more co-located data, and perform tripple co-location and analysis (SAR, WAM, Buoy) Optimize Level 2 processing set-up Select special events for analysis Summary