基于融合TC-WREM模型的热带气旋大风半径估算研究OA北大核心CSTPCD

In this paper,a fusion TC Wind Radii Estimation Model(TC-WREM)that combines a Multi-Layer Perceptron net(MLP)and a Convolutional Neural Network(CNN)is established by using the Tropical Cyclone(TC)best track data set and the static satellite cloud images.This model utilizes MLP and CNN to pre-extract the core features associated with TC wind radius from TC attribute data and satellite cloud images and ultimately performs gale wind radius estimation.The fused TC-WREM model in this study can achieve deep and objective mining of TC attribute data and underlying features of satellite cloud images,whose estimation error is reduced by about 7%-24%compared to individual MLP and CNN models.Taking the estimation of 17.2 m·s-1 wind radius of TC In-fa in 2021 as an example,the fused TC-WREM model has higher estimation accuracy than the independent MLP and CNN model.Independent sample testing shows that the mean absolute estimation error in 4 quadrants is 39,33,40,and 51 km,respectively,with an average of 41 km,respectively,which is superior to that of other similar research.The fused TC-WREM model is advantageous due to its utilization of easily obtainable TC attribute information and geostationary meteorological satellite cloud images as inputs.This makes it suitable for operational use and addresses the current lack of domestic TC gale radius estimation models.