The study demonstrates improved typhoon predictions using atmospheric motion vectors from geostationary satellites, suggesting enhanced disaster preparedness.
This study demonstrates the potential values of geostationary satellite‐derived mesoscale atmospheric motion vectors (MAMVs) to improve the analysis and forecast of typhoons. By utilizing high‐frequency, high‐resolution satellite imagery, MAMVs can provide novel important information on evolving wind fields that can influence typhoon structure, intensity, and track. This enhanced data, when assimilated, can lead to improve the skill of numerical weather prediction (NWP) models for typhoon prediction. To evaluate the effectiveness of MAMVs toward improving typhoon forecasts, this study assimilates MAMVs into NWP model and compares them with assimilating FY‐4B/AGRI operational atmospheric motion vectors (AMVs) and control experiments without MAMVs/AMVs assimilation. It is shown that for two typhoon cases, the assimilation of MAMVs can improve typhoon dynamic field and reduce typhoon track forecast errors. These case studies demonstrate a promising approach to advancing typhoon prediction that can improve disaster preparedness.
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Xia et al. (2025) studied this question.
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