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Spatial downscaling of precipitation using adaptable random forests

Spatial downscaling of precipitation using adaptable random forests
Spatial downscaling of precipitation using adaptable random forests
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° 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.
0043-1397
8217-8237
He, Xiaogang
04cc8809-035c-487a-b09d-0d50821c5f22
Chaney, Nathaniel
a4df6277-1692-4475-bf94-0a29e8b8c06e
Schleiss, Marc
09360128-013c-400d-93e2-1eef4119c431
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
He, Xiaogang
04cc8809-035c-487a-b09d-0d50821c5f22
Chaney, Nathaniel
a4df6277-1692-4475-bf94-0a29e8b8c06e
Schleiss, Marc
09360128-013c-400d-93e2-1eef4119c431
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b

He, Xiaogang, Chaney, Nathaniel, Schleiss, Marc and Sheffield, Justin (2016) Spatial downscaling of precipitation using adaptable random forests. Water Resources Research, 52 (10), 8217-8237. (doi:10.1002/2016WR019034).

Record type: Article

Abstract

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° 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.

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Accepted/In Press date: 28 September 2016
e-pub ahead of print date: 3 October 2016
Published date: 27 October 2016
Organisations: Global Env Change & Earth Observation

Identifiers

Local EPrints ID: 405286
URI: http://eprints.soton.ac.uk/id/eprint/405286
ISSN: 0043-1397
PURE UUID: 2394750a-8e5a-4ca0-b35d-7d3f53c03d44
ORCID for Justin Sheffield: ORCID iD orcid.org/0000-0003-2400-0630

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Date deposited: 02 Feb 2017 12:04
Last modified: 07 Oct 2020 02:08

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