capabilities of data assimilation in correcting sea surface temperature in the persian gulf

predicting the quality of water and air is a particular challenge for forecasting systems that support them. in order to represent the small-scale phenomena, a high-resolution model needs accurate capture of air and sea circulations, significant for forecasting environmental pollution. data assimilation is one of the state of the art methods to be used for this purpose. due to the importance of thermal structure in monitoring the variations of environmental phenomena, the present study has used sea surface temperature (sst) in data assimilation method to optimize this parameter. sst is one of the most important factors to conduct researches on the ocean, the atmosphere, and their interaction, not to mention monitoring and forecasting air and ocean phenomena as well as commercial and fishing communities and weather forecasts. this study has aimed to present a satellite-derived sst based on pathfinder advanced very high resolution radiometer (avhrr) data assimilating in fvcom (finite volume community ocean model) on the persian gulf to examine the effect of data assimilation by using the cressman scheme. the performance of this method has been compared to the optimal interpolation sst (oisst) data, via both visual comparisons and statistical parameters. applying assimilation method improves correlation coefficient of the model from 0.92 to 0.99. results demonstrate that the modeled sst has been completely reconstructed by the data assimilated experiment via the cressman 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.