Improving the accuracy of Sea Surface Temperature Prediction by Data Assimilation over the Persian Gulf

Nowadays, data assimilation is used in the most numerical geoscience models in order to increase the accuracy of their results. In this method, the model merges the observational data with its outputs to create an optimal initial condition for the next simulation cycle. In this study the satellite sea surface temperature (SST) data as observational data have been merged with the finite volume community ocean model (FVCOM) to improve the SST prediction in the Persian Gulf. The merging scheme is nudging scheme. The results showed that the model SST was completelyreorganized by the end of the run. The comparisons with the measured temperature data showed the significant reduction in the error. Applying assimilation method improves correlation coefficient of the model from 0.92 to 0.99. Results demonstrate that the modeled SST has beencompletely reconstructed by the data assimilated experiment via the Nudging scheme for this region. The spatial and temporal pattern of SST reveals a significant improvement in the entire domain during the investigated period in the gulf.

کلیدواژه ها:

Data Assimilation ، Sea Surface Temperature ، Nudging ، FVCOM ، Persian Gulf