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A Nonparametric Statistical Technique for Spatial Downscaling of Precipitation Over High Mountain Asia
Author(s) -
Mei Yiwen,
Maggioni Viviana,
Houser Paul,
Xue Yuan,
Rouf Tasnuva
Publication year - 2020
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2020wr027472
Subject(s) - precipitation , climatology , downscaling , environmental science , land cover , satellite , vegetation (pathology) , meteorology , water cycle , scale (ratio) , random forest , computer science , land use , geography , geology , cartography , machine learning , medicine , ecology , civil engineering , pathology , aerospace engineering , engineering , biology
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.