SMOS L2 OS ATBD - ARGANS

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XIII ICM-CSIC LOCEAN/SA/CETP IFREMER SMOS L2 OS Algorithm Theoretical Baseline Document Doc: SO-TN-ARG-GS-0007 Issue: 3 Rev: 9 Date: 25 January 2013 Page:A2- XIII sea ice pixels) which is clearly above the noise on retrieved Acard over the ocean (typically 2 units). Chi2 parameter gives very poor information about flaws in the direct models as Chi2 simulated with the minimalist model is very close to Chi2 simulated with the whole model (Figure 9, right). Figure 9: Left: Acard retrieved with a minimalist model (no roughness, no galactic noise) versus Acard retrieved with the whole direct model. Right) Chi2 of Acard retrieved with a minimalist model (no roughness, no galactic noise) versus Chi2 of Acard retrieved with the whole direct model. Discussion and Conclusion: In order to accurately retrieve SSS from SMOS measurements, it will be essential to well model the direct radiometric signal, characterize flaws coming from instrumental drifts and from auxiliary parameters inaccuracies and to detect the presence of surface heterogeneities in pixels like presence of sea ice. A main difficulty for SMOS calibration/validation exercise will arise from the processing and the analysis of a great number of multiangular measurements performed in each pixel. In this paper we concentrate on the use of a pseudo-dielectric constant parameter to synthesize information contained in multiangular Tb. The retrieval of this parameter could be easily processed in parallel to the SSS retrieval and this parameter could be easily delivered together with retrieved SSS, contrary to all Tb measurements. In order to stay as close as possible to the measurements and not to introduce errors coming from imperfect auxiliary parameters or from imperfect direct models, we choose to make the retrieval of Acard assuming a flat sea surface; in addition, given the linearity between SSS
XIV ICM-CSIC LOCEAN/SA/CETP IFREMER SMOS L2 OS Algorithm Theoretical Baseline Document Doc: SO-TN-ARG-GS-0007 Issue: 3 Rev: 9 Date: 25 January 2013 Page:A2- XIV and Acard, retrieving Acard with the whole direct model is rather equivalent to computing Acard from SSS and SST retrieved with the whole direct model. Given that the flat sea assumption is not valid over much of the ocean, the parameter such retrieved is biased towards high values (see Figure 9): on the half orbit average, Acard is biaseb by 7.3 units when neglecting galactic noise and wind roughness. Therefore, it rather represents an ‘effective’ pseudo-dielectric constant. As with the addition of a roughness effect Tb no longer follows a Fresnel law, Chi2 values are as large as 1.4, while there is not necessary anomalies in the measurements nor in the direct model. Without any correction for geophysical effects, ‘effective’ Acard values should only allow to detect anomalies leading to very large positive Tb (e.g. mean bias on Tb larger than typically 10K) or to negative Tb (since all geophysical effects neglected in ‘effective’ Acard retrieval add a Tb signal). In order to use ‘effective’ Acard for detecting other anomalies, it will be first necessary to correct it for geophysical effects. From the dependency of ‘effective’ Acard with wind speed seen in academic tests (Figure 8, top), we anticipate that it should be possible to find the appropriate correction by fitting ‘effective’ Acard with geophysical parameters (like wind speed), after sorting Acard retrievals by dwell lines, eliminating large galactic noise events flagged in SMOS data product and averaging Acard over numerous retrievals to decrease noise. These empirical fits should also allow to validate the direct model used to retrieve salinity: if the model is correct, the dependencies of ‘effective’ Acard obtained by direct comparison with geophysical variables should be very close to the dependencies of the differences between ‘effective’ Acard and Acard computed from SSS and SST retrieved using the complete direct model, with geophysical variables. Additional constraint could come from comparisons with Acard deduced from SSS and SST independent of SMOS retrieval (e.g. climatology). In presence of a sea-ice heterogeneous pixel, when proportion of ice or of ocean is larger than 10%, 1)Acard decreases by more than 15 units with respect to the expected ocean value, 2) retrieved surface temperature decreases by more than 10°C with respect to the true value; 3) the quality of Acard retrieval strongly deteriorates: Chi2 parameter becomes larger than 1.4 thus being in most cases larger than the one due to the computation of an ‘effective’ Acard. All these indices should allow to retrieve a raw sea-ice fraction from ‘effective’ Acard in cold waters; in case roughness effects are corrected for, presence of sea ice could be flagged from Acard and Chi2 values even in pixels containing 5% of ice.
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Table of contents ICM-CSIC LOCEAN/S
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2 ICM-CSIC LOCEAN/SA/CETP IFREMER S
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Presence of ice Applying sea ice ma
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Classification of grid point: g Oce
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36 3.5. Outliers detection ICM-CSIC
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T T 42 ss ss ICM-CSIC LOCEAN/SA/CET
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52 4.1. Flat sea ICM-CSIC LOCEAN/SA
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54 SSS ICM-CSIC LOCEAN/SA/CETP IFRE
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Page 187 and 188: Tb_i 2 = Tb_meas_i 2 174 ICM-CSIC L
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