Recent research has shown that neural network techniques can be used successfully for ground rainfall estimation from radars. Application of neural network involves training the network based on past/present data. A neural network may have to be changed with season for best performance. However retraining the network can be a tedious task. In this paper the authors have developed a dynamic neural network which can be changed adaptively with every rainfall regime. A dynamic neural network whose parameters can be adapted in an adaptive manner based on the most recent information is a good compromise solution to the dilemma of accuracy and generalization. A scheme of dynamically updating the structure and parameters of the neural network which enables the network to handle the non-stationary relationship between radar measurements and precipitation estimation with change of season, location and other environment conditions, is developed. The advantages of such a network are shown using data analysis. Data collected by a NEXRAD radar and a network of raingages over Florida is applied to this network to demonstrate the advantage of adaptive neural network for rainfall estimation.
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