Salinity Profile Estimation in the Pacific Ocean from Satellite Surface Salinity Observations

Abstract

A nonlinear empirical method, called the generalized regression neural network with the fruit fly optimization algorithm (FOAGRNN), is proposed to estimate subsurface salinity profiles from sea surface parameters in the Pacific Ocean. The purpose is to evaluate the ability of the FOAGRNN methodology and satellite salinity data to reconstruct salinity profiles. Compared with linear methodology, the estimated salinity profiles from the FOAGRNN method are in better agreement with the measured profiles at the halocline. Sensitivity studies of the FOAGRNN estimation model shows that, when applied to various types of sea surface parameters, latitude is the most significant variable in estimating salinity profiles in the tropical Pacific Ocean (correlation coefficient R greater than 0.9). In comparison, sea surface temperature (SST) and height (SSH) have minimal effects on the model. Based on FOAGRNN modeling, Pacific Ocean three-dimensional salinity fields are estimated for the year 2014 from remote sensing sea surface salinity (SSS) data. The performance of the satellite-based salinity field results and possible sources of error associated with the estimation methodology are briefly discussed. These results suggest a potential new approach for salinity profile estimation derived from sea surface data. In addition, the potential utilization of satellite SSS data is discussed.