ARTIFICIAL NEURAL-NETWORK TECHNIQUES FOR ESTIMATING HEAVY CONVECTIVE RAINFALL AND RECOGNIZING CLOUD MERGERS FROM SATELLITE DATA

被引：29

作者：

ZHANG, M

SCOFIELD, RA

机构：

[1] NOAA/NESDIS Satellite Application Laboratory, NOAA Scicnce Center, Washington, DC

来源：

INTERNATIONAL JOURNAL OF REMOTE SENSING | 1994年 / 15卷 / 16期

基金：

美国海洋和大气管理局;

关键词：

D O I：

10.1080/01431169408954324

中图分类号：

TP7 [遥感技术];

学科分类号：

081102 ; 0816 ; 081602 ; 083002 ; 1404 ;

摘要：

This research presents an artificial neural network (ANN) technique for heavy convective rainfall estimation and cloud merger recognition from satellite data. An Artificial Neural network expert system for Satellite-derived Estimation of Rainfall (ANSER) has been developed in the NOAA/NESDIS Satellite Applications Laboratory. Using artificial neural network group techniques, the following can be achieved: automatic recognition of cloud mergers, computation of rainfall amounts that will be ten times faster, and average errors of the rainfall estimates for the total precipitation event that will be reduced to less that 10 per cent.

引用

页码：3241 / 3261

页数：21

共 41 条

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