Artificial Neural Network (ANN) Based Empirical Interpolation of Precipitation

Abstract

Various climate impact studies need to generate estimates of climate variables at a given location based on values from other locations. It is well established fact that there are strong sensible physical linkages between global climate and local scale weather phenomenon. Therefore, empirical interpolation or downscaling has emerged as a prospective tool to relate atmospheric circulation patterns to surface variables for forecasting regional climate from GCM and RCM output dataset. In this paper, application of Artificial Neural Networks (ANNs) based soft computing model for empirical interpolation of precipitation in Himalayan region is attempted. This method uses ANNs to generate precipitation estimates for 11 districts of Uttarakhand state (India) given information from a lattice of surrounding locations. In the present paper, we have used Feed Forward Back Propagation (FFBP) algorithm to develop a Multilayer Perceptron ANN model for empirical downscaling of precipitation in Himalayan region. The model is developed using climate data of Climate Research Unit (CRU) and observed data for past 110 years (1901-2010). The robustness and suitability of the developed ANN model is verified by testing its applicability for 11 districts of Uttarakhand state. 80% of the data are used for training of the model and remain 20% are used for testing of the model. The performance evaluation of the model is tested by RMSE value. The study show that the model works quite well for climatic records of most of the district after bias correction.