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

• DOI: 10.1175/jtech-d-17-0226.1
• Publication Date: 2018-11-09T16:29:47Z
• Authors: Senliang Bao; Ren Zhang; Huizan Wang; Hengqian Yan; Yang Yu; Jian Chen
• Keywords: Halocline; Temperature salinity diagrams

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