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中文摘要: 使用人工神经网络(ANN)模型探讨了利用静止卫星多通道资料估算地面降水的一种新方法,对淮河和长江典型区域(24～36°N ,108°E以东)一次暴雨过程的卫星遥感数据和地面水文站逐时降水资料的应用分析表明,该方法提供的客观定量的降水量估算的平均相关系数为0.57,较现行业务使用的方法效果更佳。
中文关键词: 人工神经网络，静止卫星多通道资料，定量估算降水
Abstract:The rainfall estimation technique based on the artificial neural network model is developed. Using the hourly GMS four channel′s data and rainfall records from hydrolgraphical station,the study of a heavy rain case which occurred in Huai River and Yangtze River basins (east of 108°E,from 24°to 36°N) shows that the average correlative coefficient between the quantitative precipitation estimation and the observation rainfall is 0.57,which is far higher than that in operation.
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基金项目:湖北省气象局“利用卫星和地面气象资料估算面雨量研究”课题资助
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引用文本：
熊秋芬,胡江林,夏军,2002.神经网络方法在静止卫星多通道资料估算降水中的应用[J].气象,28(9):17-21.
,2002.A Rainfall Estimation Technique Based on the Stationary Satellite Mutli-channel Data Using Artificial Neural Network Models[J].Meteor Mon,28(9):17-21.
熊秋芬,胡江林,夏军,2002.神经网络方法在静止卫星多通道资料估算降水中的应用[J].气象,28(9):17-21.
,2002.A Rainfall Estimation Technique Based on the Stationary Satellite Mutli-channel Data Using Artificial Neural Network Models[J].Meteor Mon,28(9):17-21.