A Nonparametric Statistical Technique for Spatial Downscaling of Precipitation over High Mountain Asia
Yiwen Mei, Viviana Maggioni, Paul R. Houser, Yuan Xue, Tasnuva Rouf
Abstract
The accurate representation of the local-scale variability of precipitation plays an important role in understanding the hydrological cycle and land-atmosphere interactions in the High Mountain Asia region. Therefore, the development of hyper-resolution precipitation data is of urgent need. In this study, we propose a statistical framework to downscale the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) precipitation product using the random forest classification and regression algorithm. A set of variables representing atmospheric, geographic, and vegetation cover information are selected as model predictors, based on a recursive feature elimination method. The downscaled precipitation product is validated in terms of magnitude and variability against a set of ground- and satellite-based observations. Results suggest improvements with respect to the original resolution MERRA-2 precipitation product and comparable performance with gauge-adjusted satellite precipitation products.