Abstract
Downscaling Global Circulation Model (GCM) output is important in order to understand the present climate as well as future climate changes at local scale. In this study, Radial basis function (RBF) neural network was used to downscale the mean monthly rainfall in an arid coastal region located in Baluchistan province of Pakistan. The RBF model was used to downscale monthly rainfall from National Center for environmental prediction (NCEP) reanalysis dataset at four observation stations in the area. The potential predictors were selected using principal component analysis of NCEP variables at grid points located around the study area. Power transformation method was used to remove the bias in the prediction. The results showed that the RBF model was able to establish a good relation between NCEP predictors and local rainfall. The power transformation method was also found to perform well to correct errors in prediction. It can be concluded that RBF and power transformation methods are reliable and effective methods for downscaling rainfall in an arid coastal region.
Published Version
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