Assessing satellite sea surface salinity from ocean color radiometric measurements for coastal hydrodynamic model data assimilation

Abstract

Improving forecasts of salinity from coastal hydrodynamic models would further our predictive capacity of physical, chemical, and biological processes in the coastal ocean. However, salinity is difficult to estimate in coastal and estuarine waters at the temporal and spatial resolution required. Retrieving sea surface salinity (SSS) using satellite ocean color radiometry may provide estimates with reasonable accuracy and resolution for coastal waters that could be assimilated into hydrodynamic models to improve SSS forecasts. We evaluated the applicability of satellite SSS retrievals from two algorithms for potential assimilation into National Oceanic and Atmospheric Administration’s Chesapeake Bay Operational Forecast System (CBOFS) hydrodynamic model. Of the two satellite algorithms, a generalized additive model (GAM) outperformed that of an artificial neural network (ANN), with mean bias and root-mean-square error (RMSE) of 1.27 and 3.71 for the GAM and 3.44 and 5.01 for the ANN. However, the RMSE for the SSS predicted by CBOFS (2.47) was lower than that of both satellite algorithms. Given the better precision of the CBOFS model, assimilation of satellite ocean color SSS retrievals will not improve CBOFS forecasts of SSS in Chesapeake Bay. The bias in the GAM SSS retrievals suggests that adding a variable related to precipitation may improve its performance.