Journal
WATER RESOURCES RESEARCH
Volume 52, Issue 10, Pages 8217-8237Publisher
AMER GEOPHYSICAL UNION
DOI: 10.1002/2016WR019034
Keywords
precipitation downscaling; machine learning; random forests; NLDAS-2
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Funding
- NOAA [NA14OAR4310218]
- NSF [1534544]
- Swiss National Science Foundation [P300P2_158499]
- Swiss National Science Foundation (SNF) [P300P2_158499] Funding Source: Swiss National Science Foundation (SNF)
- Direct For Social, Behav & Economic Scie
- Divn Of Social and Economic Sciences [1534544] Funding Source: National Science Foundation
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This paper introduces Prec-DWARF (Precipitation Downscaling With Adaptable Random Forests), 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 degrees from NLDAS-2 are used to conduct synthetic experiments with different spatial resolutions (0.25 degrees, 0.5 degrees, and 1 degrees). 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 degrees 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.
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