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A. Montuori 1 , M. Portabella 2 , S. Guimbard 2 , C. Gabarrò 2 , M. Migliaccio 1

Operational SMOS Bayesian -based inversion scheme for the optimal retrieval of salinity and wind speed at sea. A. Montuori 1 , M. Portabella 2 , S. Guimbard 2 , C. Gabarrò 2 , M. Migliaccio 1 1 Dipartimento per le Tecnologie ( DiT ), University of Naples Parthenope, Italy

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A. Montuori 1 , M. Portabella 2 , S. Guimbard 2 , C. Gabarrò 2 , M. Migliaccio 1

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  1. Operational SMOS Bayesian-basedinversionscheme for the optimalretrieval of salinity and windspeedatsea A. Montuori1, M. Portabella2, S. Guimbard2, C. Gabarrò2, M. Migliaccio1 1Dipartimento per le Tecnologie (DiT), University of Naples Parthenope, Italy 2SMOS Barcelona Expert Centre (SMOS-BEC), Institute of Marine Sciences, Barcelona, Spain VII Riunione Annuale CeTeM-AIT sul  telerilevamento a Microonde: sviluppi scientifici ed implicazioni tecnologiche Villa Larocca, via Celso Ulpiani, 27 - Bari, 4-5 Dicembre 2012

  2. SMOS Mission Overview • SMOS Bayesian-based Cost Function: • General Formulation • Sensitivity Analysis • Multiple-minima Assessment • Effects of constraints • SMOS Bayesian-based minimization procedure Assessment: • Levenberg-Marquardt (LM) procedure (Monte-Carlo simulations) • Optimization for both SSS and wind speed (U10) retrieval purposes OUTLINE • Ideal Optimum Lower Bound Accuracy • Sea surface contribution only • No Effects of other source contributions (e.g. T.E.C., Galaxy, Sun, R.F.I.) • Realistically-simulated marine scenarios (reference values from DPGS)

  3. SMOS makes global observations of soil moisture over Earth's landmasses and salinity over the oceans. L-band full-polarized Microwave Imaging Radiometer using Aperture Synthesis (MIRAS). SMOS MISSION OVERVIEW Data Product Generation System (DPGS) provides consistent SSS, SST and SSR (e.g. U10) retrievals through the SMOS Level 2 Salinity Prototype Processor (L2PP) by processing geolocated TBs provided at the SMOS Level 1C (L1C) after the image reconstruction step. Assessment of the Operational SMOS Bayesian-based inversion procedure to develope a parallel simplified version of the SMOS DPGS inversion scheme for the optimal retrieval of SSS and wind speed at sea (U10).

  4. SMOS Bayesian-based Cost Function • General Complete Formulation • p = polarization • = incidence angle • SSS = Sea Surface Salinity • SST = Sea Surface Temperature • U10 = Wind Speed at 10m • Nobs = Number of observables • Forward Model for seasurfacecontributiononly Zine et. al, 2008 Klein and Swift, 1997 Guimbard et al., 2012

  5. SMOS Bayesian-based Cost Function Sensitivity Analysis  Ideal case SSS=35psu, SST=20°C, U10=5m/s σSSS=2psu, σSST=2°C, σu10=2.5m/s Lowsensitivity of noise-free and un-biased SMOS TB observables with respect to SSS, SST and U10 Whenonlyoneparameterisrestricted with an auxiliary a priori information, boththe costfunction minimum and the corresponding SSS, SST and U10solutionvalues are betterdefined. Whenall the constraints are used, boththe costfunction minimum and the corresponding SSS, SST and U10 solutionvalues are the best defined.

  6. SMOS Bayesian-based Cost Function Multiple Minima Assessment (Real noisy TB measurements) SSS=35psu, SST=20°C, U10=5m/s σSSS=2psu, σSST=2°C, σu10=2.5m/s Contour Plot of CostFunction True Value Estimated Value Contour Plot of CostFunction True Value Estimated Value Contour Plot of CostFunction True Value Estimated Value A unique absolute minimum numerical value is retrieved when only the observational term is considered. A unique absolute minimum numerical value is retrieved when the SSS-U10 constraint is considered. A unique absolute minimum numerical value is retrieved when the SSS-U10 constrained cost function configuration is considered.

  7. SMOS Bayesian-based Cost Function Effect of SST constraint SSTEstim- SSTTrue • Un-constrainedcostfunction (OBS term) • SST constrainedcostfunction (OBS + SST Background) • Very low sensitivities of realistically simulated TB measurements with respect to SST variations. • The retrieved SST values tend to be at extremes of the forward model look-up table (LUT). • This large SST retrieval error does impact the SSS and U10retrievals. • Fixing or constraining the SST in the SMOS cost function is clearly required to optimize SMOS SSS and U10 retrievals.

  8. SMOS Bayesian-based Cost Function Effect of constraints SSS=35psu, SST=20°C, U10=5m/s σSSS=0.3psu, σSST=1°C, σu10=2m/s SSS-U10-SST constrainedcostfunctionconfiguration • Retrieved - True • Retrieved - Prior • SST fixed or constrained • To derive SSS from SMOS data, SSS (U10) constraints can be used.

  9. SMOS Bayesian-based Cost Function Effect of constraints SSS=33psu, SST=0°C, U10=14m/s σSSS=0.3psu, σSST=1°C, σu10=2m/s SSS-U10-SST constrainedcostfunctionconfiguration • Retrieved - True • Retrieved - Prior Exept for SSS retrieval in cold water.

  10. Cost Function Configuration Assessment • Levenber-Marquardt (Monte-Carlo Simulations approach) • Optimization for SSS and U10 retrieval: • SST constrained of fixed to an auxiliary a priori value • SSS un-constrained for SSS retrieval

  11. SMOS Bayesian-based inversion Assessment SSS retrievaloptimization (σSSS=100psu) (σSSS=100psu) (σSSS=100psu) (σSSS=100psu) (σSSS=100psu) (σSSS=100psu) (σSSS=100psu) (σSSS=100psu) (σSSS=100psu) (σSSS=100psu) (σSSS=100psu) (σSSS=100psu) DPGS (SST-SSS (σSSS=100psu) -U10) costfunctionconfigurationisoptimal for SSSretrieval

  12. SMOS Bayesian-based inversion Assessment U10retrievaloptimization (σSSS=100psu) (σSSS=0.3psu) (σSSS=0.3psu) (σSSS=100psu) (σSSS=0.3psu) (σSSS=0.3psu) (σSSS=100psu) (σSSS=0.3psu) (σSSS=0.3psu) (σSSS=100psu) (σSSS=0.3psu) (σSSS=0.3psu) Fullyconstrained (SST-SSS-U10) costfunctionconfigurationisoptimal for U10 retrieval

  13. SMOS Bayesian-based inversion Assessment AF & EAF-FOV Nadir

  14. SMOS Bayesian-based inversion Assessment AF & EAF-FOV Edge (300km)

  15. Internal SMOS Bayesian-based processing chain for SSS and U10 retrieval purposes has been developed. • Low sensitivities of SMOS TB measurements with respect to geophysical parameter changes, especially for SST. • Unique absolute minimum value for all the cost function configurations  Unique triplet solution of SSS-U10-SST. • Fixing or constraining SST to an auxiliary value improves the retrieval of SSS and U10. • Successful assessment of LM minimization procedure for the retrieval of SSS and U10 by means of realistically simulated SMOS TB measurements. • SSS optimal retrieval  DPGS [SST-U10] configuration. • U10 optimal retrieval  Fully [SST-SSS-U10] constrained configuration. • Future test with both real SMOS TB data. Conclusions

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