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Assessment of a Fusion Sea Surface Temperature Product for Numerical Weather Predictions in China: A Case Study
Atmosphere ( IF 2.9 ) Pub Date : 2021-05-06 , DOI: 10.3390/atmos12050604 Ping Qu , Wei Wang , Zhijie Liu , Xiaoqing Gong , Chunxiang Shi , Bin Xu
Atmosphere ( IF 2.9 ) Pub Date : 2021-05-06 , DOI: 10.3390/atmos12050604 Ping Qu , Wei Wang , Zhijie Liu , Xiaoqing Gong , Chunxiang Shi , Bin Xu
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.
更新日期：2021-05-06
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