1. Impact of sea surface roughness on SMOS measurements A new empirical model S. Guimbard & SMOS-BEC Team SMOS Barcelona Expert Centre Pg. Marítim de la Barceloneta 37-49, Barcelona SPAIN E-mail: sguimbard@icm.csic.es URL: www.smos-bec.icm.csic.es

2. Introduction to the fitting process Sea Surface roughness TB formulation: The estimation of from the data will depend strongly on: 1.Data quality: instrumental effects (antenna pattern, calibration, drift) & pre-proccessing issues (reconstruction at L1A, sun…) 2. 3.Gallactic scattered correction term (overestimated) 4.Type of statistical analysis & auxillary parameters considered

3. Study angle To construct the new roughness LUT and deal with the exposed previous points, we have proceed as follow:  Very careful data filtering to build our dataset (1. & 3.)  The OTT has been calculated as the mean bias between data and model over the time period of the dataset (2.)  Bin averaging of vs Inc. Angle (θ) & ECMWF Wind Speed (WS) (4.)

4. 4 / 14 Dataset caracteristics  Level 1 Operational Processor version 3.4  31 days of Full-pol data from August 2010  Only ascendent orbits have been considered  Land contamination detected at Level 1B: wider FOV  Only pure Ocean scenes are kept (less than 0.25% of land pixels)  RFI/outliers detection in the Alias-Free FOV: threshold on the allowed maximum departure from model (20 K)  Minimization of the galactic impact in the Alias-Free FOV : threshold on the scattered galactic modeled term (WS =3m/s) (5 K)  Minimization of the Faraday rotation impact : threshold on TEC parameter (5 K)

5. Global Statistics of the data set ~9 millions points Histograms: θ<3° & θ~15° low WS

6. 6 / 14 We have considered large bin size: dθ=5° & dWs=2m/s  Statistical purpose (Nb of events /bin > 1000)  Best trade-off between WS & θ dependencies of TB rough in both polarization (H & V) Drawback: we loose the incidence angle dynamic at high incidence angle in V-pol Inc. angle & wind speed dependency

7. Auxilary parameters:HS,SST,SSS  WS and Hs are highly correlated  (SST,SSS) and WS are anti- correlated For this data set, bin averaging in dimensional spaces > 2 (Hs,SST,SSS) do not reduce « inbox » dispersion.

8. FINAL LUT Cubic interpolation over a 2D grid with θ=1:1:75, WS=0:.25:50 Hs=0:.25:15 & Wind_dir=0:10:360 No dependancy in Hs and Wind direction Values of TB rough H & V-pol for θ>60° & WS > 20 m/s remain constant and equal to the value at (θ =60,WS=20)

9. 9 / 14 Conclusions  A new Look-Up-Table of the roughness correction term (model 3) has been provide to P. Spurgeon based on this analysis.  Time convergence in the salinity retrieval process and quality of the result seem to have been improved. (Convergence is now as fast as models 1 & 2, and retrieved salinity looks close to models 1 & 2). Two points to have in mind:  The use of a model dependent OTT introduce a possible “bad” incidence angle dependency.  Filtering process => all the data used to build the model are in the South hemisphere oceans. The model is then optimized for this region.

10. Perspective  Simple analytic formulation of the LUT Tb rough V & H pol terms vs (θ,WS) has been found since the delivery: can be usefull for extrapolation over the total range of (θ,WS) as it has been defined in the TB rough mod3  New statistical approach (NN) is in developement to take into account more dependencies (HS,SST,SSS,WS direction) and also to derive a fully empirical model.

11. Discussion As we seem to have some issues with our imput quality signal at level 2, the inversion approach based on a semi- geophysical model is meaningless. Find the « transfer » function that relie smos measurements with salinity? We know that this function is non-linear and depend on a huge number of parameters. We have to deal with Noisy data => Neural network seems to me very well suited to deal with this kind of problem.

12. SMOS Barcelona Expert Centre (SMOS-BEC) Pg. Marítim de la Barceloneta 37-49, E-08003 Barcelona, SPAIN Tel. (+34) 93 230 95 00; Fax. (+34) 93 230 95 55 URL: www.smos-bec.icm.csic.es

13. 13 / 14 OTT

14. 14 / 14 Neural Network as a fitting tool This network can be used as a general function approximator. It can approximate any function with a finite number of discontinuities arbitrarily well, given sufficient neurons in the hidden layer.

15. 15 / 14 Fitting process