Improving the Persian Gulf sea surface temperature simulation by assimilating the satellite data via the ensemble Kalman

One of the indicators representing the permissible performance of a marine model is the capability of model in providing an accurate spatiotemporal distribution of the thermal structure of domain. Data assimilation is taken into account as an expedient platform in order to achieve this goal. In this paper, the ensemble Kalman filter (EnKF) was applied as a data assimilation scheme to enhance the sea surface temperature (SST) simulation and whereupon the underwater temperature of the Persian Gulf in the finite volume community ocean model (FVCOM). The daily satellite measured SST data that obtained from advanced very high-resolution radiometer pathfinder were considered as observational assimilation data. The comparisons between the results of both proposed models including FVCOM and SST measurements were carried out to evaluate the efficiency of data assimilation. The comparisons revealed a meaningful improvement in the assimilated simulation of the spatiotemporal SST variability in the whole domain, especially in the shallow parts and near the Hormuz Strait. The root-mean-square error reduced significantly in assimilation run. The statistical comparisons of the results bias denote a positive impact of the data assimilation.