Characterization of scattered celestial signals in SMOS observations over the Ocean

1. Characterization of scattered celestial signals in SMOS observations over the Ocean J. Gourrion1, J. Tenerelli2, S. Guimbard1, V. Gonzalez1 1 SMOS-BEC, ICM/CSIC • 2 CLS gourrion@icm.csic.es, jtenerelli@cls.fr

2. Concept SMOS • an Earth Explorer mission (ESA) for Soil Moisture and Ocean Salinity • Sun synchronous orbit, 3-days repeat cycle, 32.5º tilt • MIRAS payload : a polarimetric and interferometric radiometer at L-band, 72 receivers Field of View

3. Retrieving salinity

4. Retrieving salinity

5. Forward model

6. The problem Spatio-temporal systematic biases in SMOS salinities sun-synchronous orbit  the orbital plane makes one complete rotation relative to the stars per year. the instrument views roughly the same portion of the sky in successive orbits.

7. The problem Spatio-temporal systematic biases in Aquarius salinities Version 2.0 Inner beam Descending passes

8. Fitted galactic model GEOMETRICAL OPTICS GALACTIC MODEL High-frequencylimit of the Kirchhoff approximation for scattering cross sections: Express in terms of slopeprobability distribution: Assume Gaussianslopepdf. Then fit the slope variance (MSS):

9. GO model impact SMOS descending passes example SSS bias using the old (pre-launch) galatic model

10. GO model impact SMOS descending passes example SSS bias using the new galatic model

11. GO model impact AQUARIUS inner beam (ascending passes only) Aquarius galactic reflection model

12. GO model impact AQUARIUS inner beam (ascending passes only) SMOS ascending pass fit galactic reflection model

13. GO model adjustment to SMOS data Ascending Descending • Geometrical optics fitted to the data - slope variance : • increases with wind speed (expected) • changes with incidence angle (unexpected) • changes with pass orientation (unexpected)

14. Remaining issues with the GO mss fit 1 - Systematic underestimation at high incidence angle • Imperfect model physics (GO, Gaussian slope pdf ?) the fitted mss depends on incidence angle and pass orientation

15. Remaining issues with the GO mss fit 2 - Potential phasing between biases and model errors •  Need to uncouple bias estimation and model improvement tasks • Estimate biases from expected low model errors dataset • Update galactic model using a corrected dataset

16. 1 - phasing between TB biases and model errors Variability reduction strategy Variability source Reduction approach • use 1st Stokes parameter • cancel contribution using Klein-Swift model • latitude band with low SST seasonal variations • thin interval of wind speed values • thin bands of latitude (sea state) • model for emission/attenuation contributions • remove sun alias and sun tails (0.05) • average over 18-days and longitude [-180º -100º] • ? Faraday : Dielectric : Roughness : Atmosphere : Direct sun : Radiometric noise : Celestial reflections :

17. 1 - phasing between TB biases and model errors 1st Stokes TB time series : an example centered (zero mean) Galactic (KA)

18. 1 - phasing between TB biases and model errors Variability reduction strategy Variability source Reduction approach • use 1st Stokes parameter • cancel contribution using Klein-Swift model • latitude band with low SST seasonal variations • thin interval of wind speed values • thin bands of latitude (sea state) • model for emission/attenuation contributions • remove sun alias and sun tails (0.05) • average over 18-days and longitude [-180º -100º] • select xi/eta points with low annual variations (as predicted by the KA) Faraday : Dielectric : Roughness : Atmosphere : Direct sun : Radiometric noise : Celestial reflections :

19. 1 - phasing between TB biases and model errors Temporal bias estimates highly consistent throughout the field of view

20. 2 - Underestimation at high incidence angle 55º incidence angle

21. 2 - Underestimation at high incidence angle A fully-empirical correction of the bistatic coefficients

22. 2 - Underestimation at high incidence angle data GO modified GO 55º incidence, 8 m/s

23. 2 - Underestimation at high incidence angle 55º incidence, 4 m/s data GO modified GO 55º incidence, 12 m/s

24. Non gaussian slope distribution • The model is still physically inconsistent : • the slope variance is still incidence angle dependent • the slope variance is still a filtered value (the surface slope pdf is frequency-dependent) • Preliminary results for a more physically-based approach … • Use a non gaussian pdf which variance is based on Cox and Munk’s results

25. Non gaussian slope distribution 35º incidence, 8 m/s data GO non Gaussian GO using Cox and Munk’s results 45º incidence, 8 m/s mss = 0.044 for all incidence angles 55º incidence, 8 m/s

26. Summary • Accurate modeling of the scattered celestial signal is of importance for salinity retrieval at L-band • The pre-launch Kirchhoff scattering model strongly underpredicts the scattered brightness near the galactic plane and overpredicts the brightness away from the galactic plane under most surface roughness conditions • A gaussian GO model fitted to SMOS residuals significantly improves the scattering description near the specular lobe BUT remaining inaccuracy at high incidence angle AND physically inconsistent slope variance values • A methodology was developed to characterize separately TB biases and sea surface scattered celestial signal  consistent SMOS celestial residuals • It is shown that problems come from off-specular contributions modeling • Preliminary results suggest that most inconsistencies may be removed when accounting for non-gaussian slope statistics