The numerical weather prediction (NWP) model about marine meteorological disasters requires sea surface temperature (SST) data which represents the parameters of marine dynamic process. However, the SST data of high spatial-temporal resolution are scarce in Huang Bohai sea in China. In order to acquire the high resolution SST data, the authors try to retrieve the high resolution SST data in Huang Bohai sea with FY2G-based satellite data. Moreover, the data differences of the satellite retrieval data with both buoys observation SST and NCEP optimal interpolation SST are compared, respectively, in terms of the statistical analysis method. Meanwhile the statistical equations are determined about the satellite retrieval SST with observed SST and of optimal interpolation SST. The equations are referred as a standard of data quality control equations when the differences of the revised values of the statistical equations and satellite inversion are calculated. While the data fusion is done, authors retain the data that the differences of the satellite retrieval SST and the statistical equation are less than 3.0°C. Above method could make that the satellite SST space characteristic information is merged into weekly average optimal interpolation SST data, and it improves SST spatial-temporal resolution in Huang Bohai sea. The work lays a foundation for numerical models using the high resolution SST in Huang Bohai area.

In marine and coastal climate prediction, the integration of multiple imperfect models can improve accuracy by leveraging their complementary strengths. This study investigates this potential by developing a hybrid data assimilation framework that couples a biased physical model with a deep learning model. A neural network learns an optimal fitting coefficient to weight the contributions of both models throughout the assimilation process. We evaluated the framework in twin experiments based on a five-variable coupled climate model and a trained LSTM. Evaluations using root-mean-square error, frequency histograms, and probability density functions consistently demonstrated that the multi-model synthesis achieves superior assimilation performance compared to the single-model approach. Furthermore, when employing different analysis values for prediction, the overall prediction error of the multi-model coupled scheme is reduced to approximately 50% of that from single-model predictions. The promising results from this conceptual model study preliminarily validate the potential of the multi-model coupling approach, offering valuable insights into its potential application to more realistic oceanographic models.