Rainfall Prediction in Statistical Downscaling Using Tweedie Compound Response and Lasso Penalty

Abstract

Statistical Downscaling (SD) is a technique in climatology to analyze the relationship between large-scale (global) data and small-scale (local) data using statistical modeling. The SD technique is used to overcome the inability of global scale data in the form of the General Circulation Model (GCM) as a low resolution predictor to predict local scale climatic conditions in the form of high resolution rainfall as a direct response. Rainfall consists of two components, namely continuous and discrete. The continuous component describes the intensity of rainfall while the discrete component describes the occurrence of rain. both components have an important role in predicting rainfall so it is necessary to choose the right distribution. One distribution that is able to handle both rain components is the mixed Tweedie distribution, namely the Gamma and Poisson distribution, hereinafter referred to as the Tweedie compound. GCM generally has multicollinearity problems in SD modeling. This can be handled using the Lasso penalty. This study aims to predict rainfall and rainfall events by taking into account the multicollinearity problem in the model for locations on different plains. Based on the research results, it was found that Cigugur Station from the highland gets the smallest RMSEP value and the biggest r-correlation. This model is not good enough to use for moderate plains rainfall data.