Bayesian Learning and Relevance Vector Machines Approach for Downscaling of Monthly Precipitation

Abstract

In this study, statistical downscaling of large-scale general circulation model (GCM) simulations to monthly precipitation of Kemer Dam, in Turkey, has been performed through relevance vector machines (RVMs). All possible regression methods along with statistical measures have been used to select potential predictors through reanalysis data providing air850, hgt850, and prate variables as the optimal. The determined explanatory variables are then used for training RVM-based statistical downscaling model. A least-squares support vector machine (LSSVM)-based downscaling model is also constructed to compare the downscaling performance of RVM through some performance evaluation measures such as