Ocean Salinity

1. Ocean Salinity Commissioningreprocessinganalysis New processorversion: improvements and problemsdetected/solved Present performance Futureevolution: ongoingstudies

2. Land sea contamination correction J. Martínez, V. González, C. Gabarró, J. Gourrion and BEC–TEAM SMOS Barcelona Expert Centre Pg. Marítim de la Barceloneta 37-49, Barcelona SPAIN E-mail: jfont@icm.csic.es URL: www.smos-bec.icm.csic.es

3. Land contamination • Impact of correction implemented by Deimos on the strong halo around continental surfaces • to avoid multiplying the first Fourier parameter by the element of area (sqrt(3) * Distance_ratio * Distance_ratio/2) • L1PP run at BEC without and with correction • 71 ascending orbits, 71 descending from 17-21 August 2010 • Tb at 42.5º; filtering 40 < Tb < 200 • Tb maps: average per ISEA GP and then average for 1º*r*cos(lat). • SSS semi-orbits (problem in running several orbits at a time)

4. Tb ascending maps

5. Tb descending maps

6. Impact on SSS • SSS 3 semi-orbits • Run with patched L1PP and L2OS 3.17 • Specific OTT computed from uncorrected and corrected L1

7. Uncorrected

8. Corrected

9. Uncorrected

10. Corrected

11. Conclusion • The correction has removed the first order problem (strongest signal) • Back to the original scene dependant bias issue (A. Camps 2005)?

12. Pre-launch semi-empirical roughness model (SSS3) was derived from data obtained during the WISE experiments (2000-2001) on an oil platform in the NW Mediterranean • New fitting using actual SMOS data (residual after removing the rest of modelled emission components) • Guimbard et al., “SMOS semi-empirical ocean forward model adjustment” submitted to TGRS SMOS special issue New semi-empirical roughness model

13. New semi-empirical roughness model

14. OTT sensitivity study J. Gourrion, M. Portabella, R. Sabia, S.Guimbard SMOS-BEC, ICM/CSIC

15. OTT sensitivity • DPGS OTT • Impacton OTT quality of differentfactors: • Number of snapshotsused • Temporal variability and apparentdrift • Latitudinal variability • Alternative OTT estimationstrategy • Methodand preliminaryresults

16. OTT sensitivity Impact of number of snapshots • For a 16-days period dataset (Aug. 3rd – Aug 18th), about 12000 snapshots are available after comprehensive filtering (land, outliers, descending overpasses) • N OTTs are computed by randomly selecting n snapshots among all available. (N-1) rms difference of the N OTTs are then computed. • N decreases with increasing n, leading to N=2 when n=6000, i.e., about half of the total amount in the 16-days period. • For consistency, the same experiment is repeated for two additional 16-days periods (Aug. 19th – Sep 3rd, Sep. 4th – Sep 19th). The overall rms values are obtained by averaging the 3 16-day period scores. • As expected, OTT robustness depends on number of snapshots used. Current operational OTT has a 0.25K error only due to sampling.

17. OTT sensitivity Temporal variability • A 48-days period dataset (August-Sept 2010) is used and split into 8-days subsets. Same filtering than previous experiment. • The reference situation is given by the first 8-days subset. • For each subset, a fixed number of snapshots are randomly selected to compute an OTT, n = 6250. • The OTT rms increase (relative to reference) indicates an increasing data inconsistency with time, i.e., apparent drift.

18. OTT sensitivity Latitudinal variability Salinity ? Rain ? Roughness residuals ? New model 3 SSA/SPM model • A 16-day period dataset (Aug. 3rd – Aug 18th) is used and split into 6° latitudinal band subsets. • The reference situation is given by the [36° S, 30° S] latitudinal band subset. • For each subset, a fixed number of snapshots are randomly selected to compute an OTT, n = 610. • The OTT rms differences (relative to reference) mainly indicate potential forward model and auxiliary data errors. Ocean/ice transition

19. OTT sensitivity OTT as mean departure from full forward model: summary • OTT robustnesssignificantlydependsonsampling. Current OTT computationshould use a largernumber of snapshots. • Temporal inconsistenciesdueto non-modelled instrumental/reconstructioninstability and imperfectForeignSourcesmodelling • Latitudinal inconsistenciesduetoimperfectmodellingorauxiliaryparameters • OTTsestimatedfromdifferentdatasetswillvarydependingonthedistribution of sampledgeophysicalconditions • Withcurrent OTT methodology, the data are adjustedto reproduce the mean forward modelbehaviour (e.g., angular dependency): updated forward models are NOT independentfrom pre-launchversions (usedto compute theOTT)

20. OTT sensitivity New OTT estimation method: basics (1) • Objective: Estimate systematic errors in the antenna frame while avoiding use of forward models as much as possible • Main differences with current OTT: • do not use forward models • do not assume that geophysical variability is negligible BUT • select specific environmental conditions (U,SST,SSS,low galactic,…) • MEAN angular dependency is fitted with a simple polynomial function and removed from the mean scene to obtain the systematic error pattern • Work in progress: only five days of data processed in this study.

21. OTT sensitivity New OTT estimation method: comparison INCONSISTENT ANGULAR DEPENDENCE BETWEEN SMOS DATA AND PRE-LAUNCH FORWARD MODELS

22. OTT sensitivity New OTT estimation method: stability (1) Selecting different wind speed conditions RMS VALUES CONSISTENT WITH EXPECTED VALUES FROM NUMBER OF SAMPLES – GRANULAR PATTERNS

23. OTT sensitivity New OTT estimation method: summary • Adequate data selection techniques + mean angular dependence removal allows to obtain ROBUST OTT estimates WITHOUT introducing systematic errors from imperfect forward model and auxiliary information • Temporal drift effects still need to be accounted for. • Angular dependence of the corrected images is consistent with the original SMOS data • Work in progress: • Use more data • Further analyze latitudinal and temporal variations • New GMF fit using new OTT • Near-future work will compare the goodness of either additive or multiplicative formulations.