Study of SST Bias Revision Based on ConvGRU and Self-Attention
Sea surface temperature(SST)is an important physical quantity of the ocean,and accurate forecasting of SST is crucial for research in marine-related fields such as aquaculture and predicting information about the marine environment,and numerical prediction is now a common method for predicting SST.However,the prediction results produced by numerical prediction models often deviate from the actual observations,so it is necessary to revise the deviation of numerical prediction products.In this paper,a new spatio-temporal hybrid SST revision model(ConvGRU-SA)is proposed to be constructed by combining ConvGRU neural network and the attention mechanism to revise the deviation of SST forecast data in the South China Sea,which is suitable for revising the numerical SST prediction products using satellite remote sensing data.Comparison with network models such as ConvLSTM and ConvGRU proves the superiority of the ConvGRU-SA model,and different hyper-parameters are set to conduct experiments to improve the model revision accuracy.The root-mean-square error(RMSE)of the region is reduced from 0.52℃to 0.32℃after the revision,and the accuracy is improved by 38.4%.
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