Deriving Overlapped Cloud Motion Vectors Based on Geostationary Satellite and Its Application on Monitoring Typhoon Mulan

Cuiping Liu, Wei Han, Feng Zhang, Jiaqi Jin, Qiong Wu, Wenwen Li, Chloe Yuchao Gao

Geophysical Research Letters · 30 June 2025 · China Meteorological Administration (CMA), Fudan University (FuXi)

In one sentence. Neural-network cloud-top heights from Himawari-8 combined with optical flow yield two-layer cloud motion vectors that resolve Typhoon Mulan's wind structure, matching dropsondes to 5.2 m/s.

Abstract

Abstract Accurate cloud motion vector retrieval in multi‐layer clouds faces persistent challenges due to ambiguous height assignment. We developed a novel overlapped cloud motion vectors (OCMVs) retrieval method using Himawari‐8 observations. Multi‐layer cloud top heights (CTHs) were retrieved based on multi‐spectral radiance using neural networks and were assigned to upper ice and lower water cloud layers. Subsequently, CTHs from the two layers were used as respective tracers for deriving OCMVs based on the optical flow algorithm. Applied to Typhoon Mulan (2022), OCMVs showed strong vertical wind shear within the inner region, further depicting the kinematic structure of Typhoon Mulan. The vortex center of lower water OCMVs provided more valuable information on determining typhoon center than that of single‐layer CMVs. Additionally, the OCMVs demonstrated good consistency with dropsonde observations, exhibiting Root‐Mean‐Square‐Errors (RMSEs) of wind direction at ∼18.5°and wind speed at ∼5.2 m/s.

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