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Spatial downscaling of precipitation using adaptable random forests
Author(s) -
He Xiaogang,
Chaney Nathaniel W.,
Schleiss Marc,
Sheffield Justin
Publication year - 2016
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 - 10.1002/2016wr019034
Subject(s) - downscaling , precipitation , variogram , spatial ecology , environmental science , random forest , climatology , spatial dependence , multivariate interpolation , spatial variability , common spatial pattern , kriging , computer science , meteorology , bilinear interpolation , mathematics , statistics , artificial intelligence , geography , geology , ecology , biology
Abstract This paper introduces Prec‐DWARF ( Prec ipitation D ownscaling W ith A daptable R andom F orests), a novel machine‐learning based method for statistical downscaling of precipitation. Prec‐DWARF sets up a nonlinear relationship between precipitation at fine resolution and covariates at coarse/fine resolution, based on the advanced binary tree method known as Random Forests (RF). In addition to a single RF, we also consider a more advanced implementation based on two independent RFs which yield better results for extreme precipitation. Hourly gauge‐radar precipitation data at 0.125° from NLDAS‐2 are used to conduct synthetic experiments with different spatial resolutions (0.25°, 0.5°, and 1°). Quantitative evaluation of these experiments demonstrates that Prec‐DWARF consistently outperforms the baseline (i.e., bilinear interpolation in this case) and can reasonably reproduce the spatial and temporal patterns, occurrence and distribution of observed precipitation fields. However, Prec‐DWARF with a single RF significantly underestimates precipitation extremes and often cannot correctly recover the fine‐scale spatial structure, especially for the 1° experiments. Prec‐DWARF with a double RF exhibits improvement in the simulation of extreme precipitation as well as its spatial and temporal structures, but variogram analyses show that the spatial and temporal variability of the downscaled fields are still strongly underestimated. Covariate importance analysis shows that the most important predictors for the downscaling are the coarse‐scale precipitation values over adjacent grid cells as well as the distance to the closest dry grid cell (i.e., the dry drift). The encouraging results demonstrate the potential of Prec‐DWARF and machine‐learning based techniques in general for the statistical downscaling of precipitation.