A Nonparametric Statistical Technique for Spatial Downscaling of Precipitation Over High Mountain Asia

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...

Full description

Bibliographic Details
Published in:Water Resources Research
Main Authors: Mei, Yiwen, Maggioni, Viviana, Houser, Paul, Xue, Yuan, Rouf, Tasnuva
Format: Article in Journal/Newspaper
Language:unknown
Published: NASA EOSDIS Land Processes DAAC 2020
Subjects:
High Mountain Asia
predictor selection
topographic correction
random forest
precipitation downscaling
Natural Resources and Environment
Science
Merra
The Cryosphere
Online Access:https://hdl.handle.net/2027.42/163571
https://doi.org/10.1029/2020WR027472
Description
Summary: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.Key PointsThis is the first use of recursive feature elimination in predictor selection for spatial downscaling of precipitationRandom forest classification is applied to create high‐resolution precipitation mask to identify whether the pixels are rainy or notValidation is performed against ground‐based precipitation observations and remote sensing precipitation products over High Mountain Asia Peer Reviewed http://deepblue.lib.umich.edu/bitstream/2027.42/163571/2/wrcr24938.pdf http://deepblue.lib.umich.edu/bitstream/2027.42/163571/1/wrcr24938_am.pdf

Similar Items