[24-Feb-2016] Alvera-Azcarate, A., Parard, G., Barth, A., and Beckers, J.
Presented at the 2016 Ocean Sciences MeetingSea surface salinity (SSS) is being measured by the Soil Moisture and Ocean Salinity (SMOS) satellite mission since 2010, providing unprecedented information about the spatial and temporal variability of the salinity at the ocean surface. Certain zones are affected by large noise and error sources, as for example coastal regions, which hinders the extraction of data on these zones. An analysis of daily SSS from the SMOS satellite mission using DINEOF (Data Interpolating Empirical Orthogonal Functions) is presented for the North Atlantic Ocean in 2013. DINEOF allows to reconstruct missing data using a truncated EOF basis, while reducing the amount of noise and errors in geophysical datasets. A procedure combining quality flags, outlier detection and reconstruction using DINEOF is applied in order to obtain a full estimate of the SSS with reduced noise, even along zones previously not well covered by the SSS SMOS estimates, as the French, Portuguese and Spanish coasts. Results show that a reduction of the error and the amount of noise is obtained in the DINEOF SSS data compared to the initial SMOS SSS data. The signature of the Douro and Gironde rivers is detected in the DINEOF SSS. It is shown that the minimum SSS observed in the Gironde plume corresponds to a flood event in June 2013, and the shape and size of the Douro river shows a good agreement with satellite chlorophyll-a concentration data. The spatial and temporal variability of these signals will be presented. These examples show the capacity of DINEOF to remove noise and provide a full SSS dataset with reduced error, including the possibility to retrieve physical signals in zones with large errors.