Computationally efficient radar image based forecasting using RBF neural networks

Abstract

Radial Basis Function neural networks (RBFNN) have been used for tracking precipitation in weather imagery. Techniques presented in the literature used RBFNN to model precipitation as a combination of localized envelopes which evolve over time. A separate RBFNN was used to predict future values of the evolving envelope parameters considering each parameter as a time series. Prediction of envelope parameters is equivalent to forecasting the associated weather events. Recently, the authors proposed an alternative RBFNN-based approach for modeling precipitation in weather imagery in a computationally efficient manner. However, the event prediction stage was not investigated, and thus any possible trade-off between efficiency and forecasting effectiveness was not examined. In order to facilitate such a test, an appropriate prediction technique is needed. In this work, an RBFNN series prediction scheme explores the dependence of envelope parameters on each other. Although different approaches can be employed for training the RBFNN predictor, a computationally efficient subset selection method is adopted from past work, and adjusted to support parameter dependence. Simulations are presented to illustrate that simultaneous prediction of the precipitation event parameters may be advantageous.