1. Galactic noise model adjustment Jean-Luc Vergely (ACRI-ST) Jacqueline Boutin (LOCEAN) Xiaobin Yin (LOCEAN)

2. Galactic model versus SMOS measurements Modelled signal is underestimated. Bias of about 1 K. Joe Tenerelli (CLS, 2011)

3. Aim of this study To better understand rugosity in L band To diagnose where the bias comes from To give leads in order to correct the bias To estimate the corrections for some operating points

4. The way to approach the galactic contribution Semi-empirical approach : to perform a new model. Formalization of the problem and simplification. Extraction of the reflected signal from relevant orbits. Estimation of the parameters of the new model.

5. Formalization of the problem (1) Model : By hypothesis : St3 and St4 = 0

6. Formalization of the problem (2) Weighting by the antenna lobe : Ground-antenna transformation

7. Approximation Assumption : Incident galactic signal is unpolarized : Tbgal_H=Tbgal_V=Tbgal With : Antenna lobe affects in theory directly the bistatic coefficients at the ground level. Approximation : to apply antenna lobe on the galactic map.

8. Inversion of the forward model (1) Data : SMOS Tbgal_refl_X and Tbgal_refl_Y – flat sea and roughness contribution – OTT – atmospheric contribution Forward model : With b=cos²(a) or b=sin²(a), a being the rotation angle ground->antenna Inversion shall be done at the antenna level : bayesian approach as for SSS retrieval. UNKNOWN : and

9. Inversion of the forward model (2) Different inversion schemes : -at small rotation angle (TB close to the track) : TBX=TBH and TBY=TBV. Possibility to retrieve independently σ H and σ V. -at high rotation angle : necessity to retrieve σ H and σ V simultaneously -with different parameterizations : different priors. Constraints or not on the integral. Constraint of positivity (non linear process). -considering axisymmetric bistatic coefficients which do not depend on : a/ WS azimuth b/ azimuth direction of the incidence plane according to the celestial sphere. c/ SSS and SST

10. Tests using simulated data Finding specular reflection points with same relative geometry and same WS Using SMOS data after averaging Deconvolution with strong a priori knowledge Residual TBs Assumptions : incident galactic noise is not polarized. WEF applied before reflection Bistatic retrieval : non parametric Bayesian approach with a priori correlation length.

11. SMOS data selection 28 descending half orbits in the south pacific in the period 12/09/2010 – 12/10/2010 => strong galactic signal is expected. Selection of data : no contamination by land, TB valid, geometric rotation < 10° : TBH and TBV processed independently. Place the data in (ra, dec, WS, theta) super cube : average and standard deviation in each cell of the cube.

12. Data presentation (1) SMOS orbit 09/10/2010 Polar XPolar Y

13. SMOS orbit 09/10/2010 X polar Theory (current model) SMOS residues Data presentation (2) Y polar

14. Comparison of SMOS data with current model Data presentation (3) X polarY polar

15. Comparison of SMOS data with current model : orbit with low wind speed. Data presentation (4) X polarY polar

16. X polar Data presentation (5) Y polar SMOS data according wind speed.

17. X polar Data presentation (6) Y polar SMOS data according incidence angle (selection in ra/dec toward GC, afFOV)

18. Data presentation (7) Definition of 20 cells in (WS, theta) space : WS : 4 intervals [1.5m/s 4.5m/s], [1.5m/s 4.5m/s], [1.5m/s 4.5m/s], [1.5m/s 4.5m/s] θ : 5 intervals [0° 10°], [10° 20°], [20° 30°], [30° 40°], [40° 50°] Sampling in the (ra, dec) space : 0.5 °x 0.5 ° boxes Cumul of 28 descending orbits Galactic plane crosses the orbits at different x_swath positions : 12/10/2010 29/09/2010 14/09/2010 RA DEC

19. Data presentation (8) Data averaging in cells. Polar X Average data. WS = 3m/s, θ=15° Average data. WS = 6m/s, θ=45°

20. Data presentation (9) Data averaging in cells : Statistic properties. Data number. WS = 3m/s, θ=15° Data number. WS = 6m/s, θ=45°

21. Data presentation (10) Data averaging in cells : Statistic properties. Exemple of TB histogram from one cell in the cube (ra,dec,WS,theta) Std of histogram is expected to be close to the radiometric noise. Effective std is between 2.3 and 3 K for 100 data => error of the mean is about 0.3 K

22. Preliminary retrieval results polar H

23. Preliminary retrieval results polar V

24. Preliminary retrieval results Data Fitting in polar H (θ=45°)

25. Preliminary retrieval results Data Fitting in polar V (θ=45°)

26. Preliminary retrieval results polar Hpolar V Reflection coefficients RH and RV

27. Bias in the current model : where it comes from ? Bias in the modelled bistatic coefficients ? Wrong assumptions (lobe, axisymmetry) ? Bias in the ECMWF auxiliary data (wind speed) ? Bias in the OTT correction ? Bias in the galactic map ? Bias in the L1 reconstruction ? Bias due to the target heterogeneity ? Other sources ?