LAUSR

Conventional scatterometer wind retrieval has been proved effective for low and moderate wind conditions. Under high wind conditions, especially for typhoons, the air–sea interactions are fierce, and usually the sea surface backscattering is complicated by rain, leading to degraded scatterometer wind quality. Under such circumstances, radiometers in different frequencies can provide information of rains and the complex air–sea interface. In this paper, an artificial neural network (ANN) was employed to combine the observations of the two sensors onboard HY-2A satellite: radiometer and scatterometer (HSCAT) to achieve wind field retrieving under typhoon conditions. The ANN was trained by using global/regional assimilation and prediction system (GRAPES) wind field data as true values. The established network is then verified by an independent dataset excluded from the training data. It is shown that the wind speeds and directions retrieved from the ANN are better than those of the operational HSCAT products as compared to GRAPES winds. Further comparison with the H*Wind product proves that the proposed ANN is effective in terms of high wind field retrieval. The research of this paper provides a nice reference for the typhoon wind retrieval from the HY-2A satellite and for the data processing of the coming HY-2 satellite series.