Artificial neural network techniques for estimating heavy convective rainfall and recognizing cloud mergers from satellite data

Abstract

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.

Notes

∗This work was done while the author held a U.S.A. National Research Council Postdoctoral Research Associateship at the National Oceanic and Atmospheric Administration (NOAA), National Environmental Satellite, Data, and Information Service (NESDIS), Satellite Application Laboratory (SAL).