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 2 SMOS Barcelona Expert Centre (SMOS-BEC), Institute of Marine Sciences, Barcelona, Spain Operational SMOS Bayesian-based inversion scheme for the optimal retrieval of salinity and wind speed at sea 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
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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 - PowerPoint PPT Presentation
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A. Montuori1, M. Portabella2, S. Guimbard2, C. Gabarrò2, M. Migliaccio1
1Dipartimento per le Tecnologie (DiT), University of Naples Parthenope, Italy2SMOS Barcelona Expert Centre (SMOS-BEC), Institute of Marine Sciences, Barcelona, Spain
Operational SMOS Bayesian-based inversion scheme
for the optimal retrieval of salinity and wind speed at sea
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
OUTLINESMOS Mission Overview
SMOS Bayesian-based Cost Function:
General Formulation Sensitivity AnalysisMultiple-minima AssessmentEffects of constraints
Levenberg-Marquardt (LM) procedure (Monte-Carlo simulations)Optimization for both SSS and wind speed (U10) retrieval purposes
Ideal Optimum Lower Bound Accuracy Sea surface contribution onlyNo Effects of other source contributions (e.g. T.E.C., Galaxy, Sun, R.F.I.)Realistically-simulated marine scenarios (reference values from DPGS)
SMOS MISSION OVERVIEWSMOS 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).
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).
General Complete Formulation
SMOS Bayesian-based Cost Function
Forward Model for sea surface contribution only
Klein and Swift, 1997 Guimbard et al., 2012
Zine et. al, 2008
p = polarization q = incidence angleSSS = Sea Surface SalinitySST = Sea Surface TemperatureU10 = Wind Speed at 10mNobs = Number of observables
SMOS Bayesian-based Cost Function
Sensitivity Analysis Ideal case
Low sensitivity of noise-free and un-biased SMOS TB observables with respect to SSS, SST and U10
When only one parameter is restricted with an auxiliary a priori information, both the cost function minimum and the corresponding
SSS, SST and U10 solution values are better defined.
When all the constraints are used, both the cost function minimum and the corresponding SSS, SST and U10 solution