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

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

doi:10.1002/joc.5283

Cited by 55 publications

(39 citation statements)

References 47 publications

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“…However, there are particular PP and WG methods with a similar performance. In this work we found that, in agreement with previous studies (Reiter, Gutjahr, Schefczyk, Heinemann, & Casper, ), introducing a seasonal component (e.g., training the methods separately each calendar season, month or moving window) improves the results. However, we found that all implementations (even a daily moving window) resulted in a relevant performance improvement, differently to Reiter et al (), where seasons were recommended for calibration.…”

Section: Discussionmentioning

confidence: 99%

“…In this work we found that, in agreement with previous studies (Reiter, Gutjahr, Schefczyk, Heinemann, & Casper, ), introducing a seasonal component (e.g., training the methods separately each calendar season, month or moving window) improves the results. However, we found that all implementations (even a daily moving window) resulted in a relevant performance improvement, differently to Reiter et al (), where seasons were recommended for calibration. The deterministic or stochastic nature of the method was the most relevant factor (together with seasonal training) for explaining the variability of results for biases in the standard deviation.…”

Section: Discussionmentioning

confidence: 99%

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An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross‐validation experiment

Gutiérrez

Maraun

Widmann

et al. 2018

Intl Journal of Climatology

203

254

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VALUE is an open European collaboration to intercompare downscaling approaches for climate change research, focusing on different validation aspects (marginal, temporal, extremes, spatial, process‐based, etc.). Here we describe the participating methods and first results from the first experiment, using “perfect” reanalysis (and reanalysis‐driven regional climate model (RCM)) predictors to assess the intrinsic performance of the methods for downscaling precipitation and temperatures over a set of 86 stations representative of the main climatic regions in Europe. This study constitutes the largest and most comprehensive to date intercomparison of statistical downscaling methods, covering the three common downscaling approaches (perfect prognosis, model output statistics—including bias correction—and weather generators) with a total of over 50 downscaling methods representative of the most common techniques. Overall, most of the downscaling methods greatly improve (reanalysis or RCM) raw model biases and no approach or technique seems to be superior in general, because there is a large method‐to‐method variability. The main factors most influencing the results are the seasonal calibration of the methods (e.g., using a moving window) and their stochastic nature. The particular predictors used also play an important role in cases where the comparison was possible, both for the validation results and for the strength of the predictor–predictand link, indicating the local variability explained. However, the present study cannot give a conclusive assessment of the skill of the methods to simulate regional future climates, and further experiments will be soon performed in the framework of the EURO‐CORDEX initiative (where VALUE activities have merged and follow on). Finally, research transparency and reproducibility has been a major concern and substantive steps have been taken. In particular, the necessary data to run the experiments are provided at http://www.value-cost.eu/data and data and validation results are available from the VALUE validation portal for further investigation: http://www.value-cost.eu/validationportal.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross‐validation experiment

Gutiérrez

Maraun

Widmann

et al. 2018

Intl Journal of Climatology

203

254

View full text Add to dashboard Cite

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“…A MAE value close to 0 indicates an unbiased prediction. Both the root mean square error (RMSE) and the mean absolute error (MAE) have extensively been employed in climate change and hydrological model evaluation studies [2,6,29,[98][99][100][101][102].…”

Section: Bias Correction Implementation and Performance Assessmentmentioning

confidence: 99%

Performance Evaluation of Bias Correction Methods for Climate Change Monthly Precipitation Projections over Costa Rica

Mendez

Maathuis

Hein-Griggs

et al. 2020

Water

138

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Six bias correction (BC) methods; delta-method (DT), linear scaling (LS), power transformation of precipitation (PTR), empirical quantile mapping (EQM), gamma quantile mapping (GQM) and gamma-pareto quantile mapping (GPQM) were applied to adjust the biases of historical monthly precipitation outputs from five General Circulation Models (GCMs) dynamically downscaled by two Regional Climate Models (RCMs) for a total of seven different GCM-RCM pairs over Costa Rica. High-resolution gridded precipitation observations were used for the control period 1951-1980 and validated over the period 1981-1995. Results show that considerable biases exist between uncorrected GCM-RCM outputs and observations, which largely depend on GCM-RCM pair, seasonality, climatic region and spatial resolution. After the application of bias correction, substantial biases reductions and comparable performances among most BC methods were observed for most GCM-RCM pairs; with EQM and DT marginally outperforming the remaining methods. Consequently, EQM and DT were selectively applied to correct the biases of precipitation projections from each individual GCM-RCM pair for a near-future (2011-2040), mid-future (2041-2070) and far-future (2071-2100) period under Representative Concentration Pathways (RCPs) 2.6, 4.5 and 8.5 using the control period . Results from the bias-corrected future ensemble-mean anticipate a marked decreasing trend in precipitation from near to far-future periods during the dry season (December, January, February (DJF) and March, April, May (MAM)) for RCP4.5 and 8.5; with pronounced drier conditions for those climatic regions draining towards the Pacific Ocean. In contrast, mostly wetter conditions are expected during the dry season under RCP2.6, particularly for the Caribbean region. In most of the country, the greatest decrease in precipitation is projected at the beginning of the rainy season (June, July, August (JJA)) for the far-future period under RCP8.5, except for the Caribbean region where mostly wetter conditions are anticipated. Regardless of future period, slight increases in precipitation with higher radiative forcing are expected for SON excluding the Caribbean region, where precipitation is likely to increase with increasing radiative forcing and future period. This study demonstrates that bias correction should be considered before direct application of GCM-RCM precipitation projections over complex territories such as Costa Rica.

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“…(), among others. One of the most popular BC methods is quantile mapping (QM) (Gudmundsson et al ., ; Cannon et al ., ; Miao et al ., ; Reiter et al ., ). QM adjusts the cumulative distribution function (CDF) of the RCMs simulations to the CDF of the observations.…”

Section: Introductionmentioning

confidence: 97%

Extreme precipitation events under climate change in the Iberian Peninsula

Pereira

Marta‐Almeida

Carvalho

et al. 2019

Intl Journal of Climatology

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Precipitation is one of the most important atmospheric variables to assess, particularly in the context of climate change. This study evaluates future changes in precipitation over the Iberian Peninsula (IP) under the RCP8.5 scenario. Changes are assessed for two future climate periods namely (2046–2065) and (2081–2100), relative to a recent reference climate (1986–2005). Here we introduce the concept of precipitation episodes (PEs) and estimate their statistical properties for the present climate and, their changes for future climate scenarios. PEs are defined by considering a full range of durations as well as intensities. This constitutes a novel approach to estimate changes with relevance, for example, for water resources applications. The climate simulations are performed with the Weather Research and Forecast (WRF) model. These are compared with an ensemble of other similar simulations from the Coordinated Downscaling Experiment initiative. This was done to evaluate the performance of the WRF model and also to estimate uncertainty of the derived future projections. Since models may present systematic errors, results from all simulations were previously bias corrected relative to observations using the same quantile mapping method. Under climate change, a great part of the region is expected to experience reduced annual precipitation of approximately 20–40% and reaching 80% in summer by the end of the XXI century. For the PEs, a large reduction in the average number of days and duration of all types of PEs is expected across all seasons and regions. The average intensity of episodes is projected to increase in winter and spring and decrease in summer. These results imply that climate change will likely influence precipitation and precipitation extremes in the 21st century, mostly in southern areas. These, together with projected warming may amplify desertification already taking place in the southern regions of the IP and cause stresses to water resources.

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

Cited by 55 publications

References 47 publications

An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross‐validation experiment

An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross‐validation experiment

Performance Evaluation of Bias Correction Methods for Climate Change Monthly Precipitation Projections over Costa Rica

Extreme precipitation events under climate change in the Iberian Peninsula

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