S. SUMMARY AND OUTLOOK 
The use of ANSER for rainfall estimates is an important 
tool for the enhancement of accuracy for automatic rainfall 
estimation from satellite data. In this paper, the main 
research efforts focused on the development of: 
(1) The architecture for the ANSER system for rainfall 
estimates; 
(2 The parallel and nonlinear reasoning networks for 
the estimation of rainfall; 
(3) A 1/2 hour training algorithm for the reasoning 
network to estimate rainfall; 
(4) Several experimental tests of rainfall estimation 
using the ANSER system. 
Because the ANSER is a massive parallel processing 
system, it makes estimating rainfall 10 times faster. In 
these cases, after all the information was received, the 
satellite-derived estimation of rainfall required about from 
2 to 10 seconds of HDS 9000 CPU time to execute. 
Because the ANSER is a nonlinear reasoning system, the 
average error of rainfall estimates is reduced to less than 
30%, the currently achievable accuracy. In this work, the 
average errors of the rainfall estimates were always less 
than 10.0%. 
Decision trees are series process techniques and run step 
by step and node by node. So decision tree techniques 
require more running time than that of parallel techniques. 
Decision trees are not able to use all rules, models, 
knowledge and factors that are stored in the different 
nodes at the same time. This is especially true when the 
rules, models, knowledge and factors are very complicated 
and nonlinear. So decision tree techniques always give 
"rough" results. 
ANN techniques have demonstrated superior performance 
relative to classical methods for predicting the future 
behavior of a pseudo-random time series. There are many 
practical applications where, rainfall estimation and 
forecasting, can be of great value. Forecasting natural 
phenomena is a great area for using artificial neural 
network expert system techniques. 
This study only considered rainfall estimation using the 
ANSER technique for some special cases. Further studies 
will consider the satellite signatures that comprise the 
convective rainfall estimation algorithm using ANN 
techniques. 
ACKNOWLEDGEMENTS 
The authors thank Donald B. Miller, Frances Holt, Joanna 
L. Newby, Tim Bellerby of Satellite Application 
Laboratory of NESDIS/NOAA for their constructive 
criticism, Juying Xie for providing satellite-derived input 
data and Dick Pritchard of Planning Research Corporation 
for providing satellite pictures, Paige Bridges for 
propagation of the illustrations, and Lori Paschal for typing 
the manuscript. 
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