A common approach used for multi-source observation data blending is the fusion method. This study assesses the applicability of the first-generation fusion sea surface temperature (SST) product of the China Meteorological Administration (CMA) in the Yellow–Bohai Sea region for numerical weather predictions. First, daily and 6 h fusion SST measurements are compared with data derived from 21 buoy sites for 2019 to 2020. The error analysis results show that the root-mean-square error (RMSE) of the daily SST ranges from 0.64 to 1.36 °C (overall RMSE of 0.996 °C). The RMSE of the 6 h SST varies from 0.64 to 1.73 °C (overall RMSE of 1.06 °C). According to the simulation result, the SST difference could affect the value and location distribution of liquid water content in the fog area. A lower SST is favorable for increasing the liquid water content, which fits the mechanisms of advection fog formation by warm air flowing over colder water.

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.

The thickness of sea ice is an important parameter for characterizing its severity. Therefore, we determined sea ice thickness in Bohai Bay, northeastern China, by using the reflected signal of the global navigation satellite system along with electromagnetic wave theory equations. Horizontal and vertical polarization data were used in the retrieval procedure. The retrieved sea ice thickness was verified via observational experiments in three steps: The first step involved pre-processing the observation data of the reflected signal to correct the antenna gain error. Then, seawater and sea ice were distinguished based on their respective polarization indices. Finally, sea ice thickness was retrieved using the reflected signal data except for the sea water signal. The verification results showed that this method of retrieving sea ice thickness can be used to determine thicknesses of up to 0.25 m, for which the estimation error is less than 15%. In addition, the error can be decreased by improving retrieval-based algorithms and the precision of the sea ice dielectric constant. The retrieved thickness can be applied to determine whether sea ice could damage maritime facilities.