Bias correction of ENSEMBLES precipitation data with focus on the effect of the length of the calibration period

Reiter, Philipp; Gutjahr, Oliver; Schefczyk, Lukas; Heinemann, Günther; Casper, Markus

doi:10.1127/metz/2015/0714

Cited by 20 publications

(27 citation statements)

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“…The remaining biases in the validation data were larger for the less robust methods eQM and GQM than for the more robust methods gQM and PTF . This lesser robustness was also found for a general reduction of the sample size used for calibration (Reiter et al ., ).…”

Section: Discussionmentioning

confidence: 99%

“…For all data sets, we analysed the daily precipitation data for Germany and its bordering areas ( Figure 1). A detailed description of the data sets is given in Reiter et al (2016).…”

Section: Data Sets and Domainmentioning

confidence: 99%

“…Quantile mapping (QM) is a well performing (Themeßl et al, 2011;Gudmundsson et al, 2012;Teutschbein and Seibert, 2012;Räty et al, 2014;Fang et al, 2015;Teng et al, 2015) and widely used (Gudmundsson et al, 2012;Maraun, 2013;Wilcke et al, 2013) method for correcting daily precipitation data. It is often calibrated with the entire precipitation time series as a whole (Piani et al, 2010a;Oettli et al, 2011;Gudmundsson et al, 2012;Chen et al, 2013;Gutjahr and Heinemann, 2013;Sunyer et al, 2015;Reiter et al, 2016). The annual cycle of daily precipitation is, however, supposed to improve when QM is calibrated with subsamples of the time series (Maraun et al, 2010;Piani et al, 2010b;Yang et al, 2010;Themeßl et al, 2011;Berg et al, 2012;Wilcke et al, 2013;Ruffault et al, 2014;Gennaretti et al, 2015;Teng et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…The drawback of using finer subsampling timescales is that the calibration sample size decreases. Small calibration sample sizes were found to result in a less robust QM calibration because the sampling uncertainty increases (Boé et al, 2007;Berg et al, 2012;Räisänen and Räty, 2013;Räty et al, 2014;Rajczak et al, 2016;Reiter et al, 2016). An additional source for sampling uncertainty is the selection of the calibration data (Li et al, 2010;Lafon et al, 2013), which has to be considered as well.…”

Section: Introductionmentioning

confidence: 99%

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Does applying quantile mapping to subsamples improve the bias correction of daily precipitation?

Reiter

Gutjahr

Schefczyk

et al. 2017

Intl Journal of Climatology

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Quantile mapping (QM) is routinely applied in many climate change impact studies for the bias correction (BC) of daily precipitation data. It corrects the complete distribution, but does not correct for errors in the annual cycle. Therefore, QM is often applied separately to temporal subsamples of the data (e.g. each calendar month), which reduces the calibration sample size. The question arises whether this sample size reduction negates the benefit from applying QM to temporal subsamples. We applied four QM methods in a cross‐validation approach to 40 years of daily precipitation data from 10 regional climate model (RCM) hindcast runs, without and with (semi‐annual, seasonal, and monthly) subsampling. QM subsampling improved the BC of daily RCM precipitation; less distinct for independent data but considerably for the calibration data. The optimal subsampling timescale for the correction of independent data depended on the chosen QM method and ranged between semi‐annual and monthly. Overall, a sub‐annual QM improves the forcing for climate change impact studies and thus their reliability.

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Section: Discussionmentioning

confidence: 99%

“…For all data sets, we analysed the daily precipitation data for Germany and its bordering areas ( Figure 1). A detailed description of the data sets is given in Reiter et al (2016).…”

Section: Data Sets and Domainmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Does applying quantile mapping to subsamples improve the bias correction of daily precipitation?

Reiter

Gutjahr

Schefczyk

et al. 2017

Intl Journal of Climatology

Self Cite

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“…This small portable rainfall simulator was always calibrated for a rainfall intensity of 40 mm h -1 to allow comparing both areas. This can be considered occasional rainfall intensity for events with low occurrence in the Saar-Mosel valley (Reiter et al, 2016;Rodrigo Comino, 2015). We assumed the rainfall intensity (kinetic energy and drop size distribution) using the calibration of Iserloh et al (2012Iserloh et al ( , 2013a to be able to compare results from regions with differing natural rainfall intensities.…”

Section: Small Portable Rainfall Simulation and Plotsmentioning

confidence: 99%

Soil erosion in sloping vineyards under conventional and organic land use managements (Saar-Mosel Valley, Germany)

Kirchhoff

Rodrigo‐Comino

Seeger

et al. 2017

CIG

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Novel application of a process convolution approach for calibrating output from numerical models

et al. 2023

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Output from numerical models at high spatial and temporal resolutions is critical for modeling applications in a variety of disciplines. Prior to its use in modeling, output from climate models must be brought to a finer spatial resolution and calibrated with respect to observations. The calibration of model output, referred to as bias‐correction, poses many statistical challenges. Here, we develop a bias‐correction method in which systematic biases in the mean and standard deviation of model output are corrected. In addition, we employ a novel process convolution approach to correct bias in temporal dependence. We apply this approach to temperature simulations generated by a regional climate model over the Northeastern USA. The goal of this study was to correct systematic bias in model simulations over historical (1976–2005) and future (2006–2099) time periods while simultaneously preserving future trends resulting from carbon emissions scenarios. We compare the proposed method to a quantile mapping method (empirical quantile mapping, EQM). The proposed method resulted in a more effective correction of seasonal biases and temporal dependence compared to EQM.

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Bias correction of ENSEMBLES precipitation data with focus on the effect of the length of the calibration period

Cited by 20 publications

References 0 publications

Does applying quantile mapping to subsamples improve the bias correction of daily precipitation?

Does applying quantile mapping to subsamples improve the bias correction of daily precipitation?

Soil erosion in sloping vineyards under conventional and organic land use managements (Saar-Mosel Valley, Germany)

Novel application of a process convolution approach for calibrating output from numerical models

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