Sea Surface Salinity Retrievals from Aquarius Using Neural Networks

Abstract

Even though the Sea Surface Salinity (SSS) retrieved from Aquarius are generally very close to in-situ measurements, the level of similarity varies with the region and with the circumstances of the observations (wind speed, sea surface temperature, etc.). SSS is currently retrieved from the brightness temperatures measured by Aquarius and applying the current theoretical model for the propagation and emission of the natural thermal radiation. In this contribution we consider an alternative retrieval approach based on a Neural Network (NN) with the goal of improving the subsets of Aquarius SSS data that are in poorer agreement with in-situ measurements. The subset considered here are the SSS retrieved at latitudes higher than 30˚. The output of the NN approach are compared against in-situ measurements using four statistical metrics (correlation coefficient, bias, RMSD and 5% trimmed range). The output of the NN and the nominal Aquarius SSS are compared against SSS values from in-situ measurements and from ocean models. From these comparisons it appears that the output of the NN matches the in-situ measurements better than the nominal Aquarius SSS.