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Statistical precipitation bias correction of gridded model data using point measurements

Research output: Journal contributions › Journal articles › Research › peer-review

Standard

Statistical precipitation bias correction of gridded model data using point measurements. / Haerter, Jan O.; Eggert, Bastian; Moseley, Christopher et al.
In: Geophysical Research Letters, Vol. 42, No. 6, 28.03.2015, p. 1919-1929.

Research output: Journal contributions › Journal articles › Research › peer-review

Harvard

Haerter, JO, Eggert, B, Moseley, C, Piani, C & Berg, P 2015, 'Statistical precipitation bias correction of gridded model data using point measurements', Geophysical Research Letters, vol. 42, no. 6, pp. 1919-1929. https://doi.org/10.1002/2015GL063188

APA

Haerter, J. O., Eggert, B., Moseley, C., Piani, C., & Berg, P. (2015). Statistical precipitation bias correction of gridded model data using point measurements. Geophysical Research Letters, 42(6), 1919-1929. https://doi.org/10.1002/2015GL063188

Vancouver

Haerter JO, Eggert B, Moseley C, Piani C, Berg P. Statistical precipitation bias correction of gridded model data using point measurements. Geophysical Research Letters. 2015 Mar 28;42(6):1919-1929. doi: 10.1002/2015GL063188

Bibtex

@article{7e1ae4c1b98140588eb3078b5a00f83f,
title = "Statistical precipitation bias correction of gridded model data using point measurements",
abstract = "It is well known that climate model output data cannot be used directly as input to impact models, e.g., hydrology models, due to climate model errors. Recently, it has become customary to apply statistical bias correction to achieve better statistical correspondence to observational data. As climate model output should be interpreted as the space-time average over a given model grid box and output time step, the status quo in bias correction is to employ matching gridded observational data to yield optimal results. Here we show that when gridded observational data are not available, statistical bias correction can be carried out using point measurements, e.g., rain gauges. Our nonparametric method, which we call scale-adapted statistical bias correction (SABC), is achieved by data aggregation of either the available modeled or gauge data. SABC is a straightforward application of the well-known Taylor hypothesis of frozen turbulence. Using climate model and rain gauge data, we show that SABC performs significantly better than equal-time period statistical bias correction.",
keywords = "climate model, extreme events, precipitation, rain gauge, statistical bias correction, Environmental Governance",
author = "Haerter, {Jan O.} and Bastian Eggert and Christopher Moseley and Claudio Piani and Peter Berg",
note = "Publisher Copyright: {\textcopyright}2015. American Geophysical Union. All Rights Reserved.",
year = "2015",
month = mar,
day = "28",
doi = "10.1002/2015GL063188",
language = "English",
volume = "42",
pages = "1919--1929",
journal = "Geophysical Research Letters",
issn = "0094-8276",
publisher = "John Wiley & Sons Inc.",
number = "6",

}

RIS

TY - JOUR

T1 - Statistical precipitation bias correction of gridded model data using point measurements

AU - Haerter, Jan O.

AU - Eggert, Bastian

AU - Moseley, Christopher

AU - Piani, Claudio

AU - Berg, Peter

N1 - Publisher Copyright: ©2015. American Geophysical Union. All Rights Reserved.

PY - 2015/3/28

Y1 - 2015/3/28

N2 - It is well known that climate model output data cannot be used directly as input to impact models, e.g., hydrology models, due to climate model errors. Recently, it has become customary to apply statistical bias correction to achieve better statistical correspondence to observational data. As climate model output should be interpreted as the space-time average over a given model grid box and output time step, the status quo in bias correction is to employ matching gridded observational data to yield optimal results. Here we show that when gridded observational data are not available, statistical bias correction can be carried out using point measurements, e.g., rain gauges. Our nonparametric method, which we call scale-adapted statistical bias correction (SABC), is achieved by data aggregation of either the available modeled or gauge data. SABC is a straightforward application of the well-known Taylor hypothesis of frozen turbulence. Using climate model and rain gauge data, we show that SABC performs significantly better than equal-time period statistical bias correction.

AB - It is well known that climate model output data cannot be used directly as input to impact models, e.g., hydrology models, due to climate model errors. Recently, it has become customary to apply statistical bias correction to achieve better statistical correspondence to observational data. As climate model output should be interpreted as the space-time average over a given model grid box and output time step, the status quo in bias correction is to employ matching gridded observational data to yield optimal results. Here we show that when gridded observational data are not available, statistical bias correction can be carried out using point measurements, e.g., rain gauges. Our nonparametric method, which we call scale-adapted statistical bias correction (SABC), is achieved by data aggregation of either the available modeled or gauge data. SABC is a straightforward application of the well-known Taylor hypothesis of frozen turbulence. Using climate model and rain gauge data, we show that SABC performs significantly better than equal-time period statistical bias correction.

KW - climate model

KW - extreme events

KW - precipitation

KW - rain gauge

KW - statistical bias correction

KW - Environmental Governance

UR - http://www.scopus.com/inward/record.url?scp=84927720455&partnerID=8YFLogxK

U2 - 10.1002/2015GL063188

DO - 10.1002/2015GL063188

M3 - Journal articles

AN - SCOPUS:84927720455

VL - 42

SP - 1919

EP - 1929

JO - Geophysical Research Letters

JF - Geophysical Research Letters

SN - 0094-8276

IS - 6

ER -

DOI

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