用红外云图估算热带气旋短时雨量

以热带气旋降水云区的云顶亮温和1小时云顶亮温差为BP人工神经网络的输入层，以实况1小时的雨量为人工神经网络的输出层，建立了4类人工神经网络预报模型，分别为点模型、线模型、小面模型和大面模型。通过大量的人工神经网络试报表明小面模型能较好地抓住热带气旋红外云图中的主要降水影响因子（移向、移速、云顶亮温、发展率、云顶亮温梯度），具有“过拟合”现象低、泛化性能高、预报能力强等特点。

Estimation of Rainfall of Tropical Cyclone with Infrared Satellite Image

Taking the cloud top temperatures and their differences from previous an hour temperature, which cause corresponding rainfall over the area of the automatic observatory, as the input layer and the hourly rainfalls as output layer, four kinds of artificial neural network prediction models, which include dot model, line model, little-area model and large-area model are built for June-September tropical cyclone rainfall forecasting in Zhejiang province. After a lot of tests the results show that little-area model could grasp main rainfall factors in infrared satellite image, which include moving direction, moving speed, temperature, developing rate and TBB gradient. It features better ability of forecast and TBB preventing over-fitting.