scholarly journals A method to preserve trends in quantile mapping bias correction of climate modeled temperature

2017 ◽  
Author(s):  
Manolis G. Grillakis ◽  
Aristeidis G. Koutroulis ◽  
Ioannis N. Daliakopoulos ◽  
Ioannis K. Tsanis
Keyword(s):  
Bias Correction ◽  
Quantile Mapping ◽  
Temperature Bias ◽  
Mapping Bias ◽  
Temperature Signal ◽  
Change Impact ◽  
The Mean ◽  
Model Temperature ◽  
Daily Time

Abstract. Bias correction of climate variables is a standard practice in Climate Change Impact (CCI) studies. Various methodologies have been developed within the framework of quantile mapping. However, it is well known that quantile mapping may significantly modify the long term statistics due to the time dependency that the temperature bias. Here, a method to overcome this issue without compromising the day to day correction statistics is presented. The methodology separates the model temperature signal into a normalized and a residual component relatively to the molded reference period climatology, in order to adjust the biases only for the former and preserve intact the signal of the later. The results show that the adoption of this method allows for the preservation of the originally modeled long-term signal in the mean, the standard deviation and higher and lower percentiles of temperature. The methodology is tested on daily time series obtained from five Euro CORDEX RCM models, to illustrate the improvements of this method.

scholarly journals A method to preserve trends in quantile mapping bias correction of climate modeled temperature

2017 ◽  
Vol 8 (3) ◽  
pp. 889-900 ◽  
Author(s):  
Manolis G. Grillakis ◽  
Aristeidis G. Koutroulis ◽  
Ioannis N. Daliakopoulos ◽  
Ioannis K. Tsanis
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Quantile Mapping ◽  
Temperature Bias ◽  
Mapping Bias ◽  
Temperature Signal ◽  
Change Impact

Abstract. Bias correction of climate variables is a standard practice in climate change impact (CCI) studies. Various methodologies have been developed within the framework of quantile mapping. However, it is well known that quantile mapping may significantly modify the long-term statistics due to the time dependency of the temperature bias. Here, a method to overcome this issue without compromising the day-to-day correction statistics is presented. The methodology separates the modeled temperature signal into a normalized and a residual component relative to the modeled reference period climatology, in order to adjust the biases only for the former and preserve the signal of the later. The results show that this method allows for the preservation of the originally modeled long-term signal in the mean, the standard deviation and higher and lower percentiles of temperature. To illustrate the improvements, the methodology is tested on daily time series obtained from five Euro CORDEX regional climate models (RCMs).

scholarly journals Addressing the assumption of stationarityin statistical bias correction of temperature

2016 ◽  
Author(s):  
Manolis G. Grillakis ◽  
Aristeidis G. Koutroulis ◽  
Ioannis N. Daliakopoulos ◽  
Ioannis K. Tsanis
Keyword(s):  
Time Series ◽  
Bias Correction ◽  
Statistical Characteristics ◽  
Statistical Bias ◽  
Climate Variable ◽  
Processing Procedure ◽  
Change Impact ◽  
The Mean ◽  
Temperature Time

Abstract. Bias correction of climate variables has become a standard practice in Climate Change Impact (CCI) studies. While various methodologies have been developed, their majority assumes that the statistical characteristics of the biases between the modeled data and the observations remain unchanged in time. However, it is well known that this assumption of stationarity cannot stand in the context of a climate. Here, a method to overcome the assumption of stationarity and its drawbacks is presented. The method is presented as a pre-post processing procedure that can potentially be applied with different bias correction methods. The methodology separates the stationary and the non-stationary components of a time series, in order to adjust the biases only for the former and preserve intact the signal of the later. The results show that the adoption of this method prevents the distortion and allows for the preservation of the originally modeled long-term signal in the mean, the standard deviation, but also the higher and lower percentiles of the climate variable. Daily temperature time series obtained from five Euro CORDEX RCM models are used to illustrate the improvements of this method.

scholarly journals Influence of Bias Correction Methods on Simulated Köppen−Geiger Climate Zones in Europe

Climate ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 18 ◽  
Author(s):  
Beáta Szabó-Takács ◽  
Aleš Farda ◽  
Petr Skalák ◽  
Jan Meitner
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Regional Climate ◽  
Generalized Pareto Distribution ◽  
Regional Climate Models ◽  
Climate Classification ◽  
Quantile Mapping ◽  
Temperature Bias ◽  
Simulated Precipitation ◽  
Precipitation And Temperature

Our goal was to investigate the influence of bias correction methods on climate simulations over the European domain. We calculated the Köppen−Geiger climate classification using five individual regional climate models (RCM) of the ENSEMBLES project in the European domain during the period 1961−1990. The simulated precipitation and temperature data were corrected using the European daily high-resolution gridded dataset (E-OBS) observed data by five methods: (i) the empirical quantile mapping of precipitation and temperature, (ii) the quantile mapping of precipitation and temperature based on gamma and Generalized Pareto Distribution of precipitation, (iii) local intensity scaling, (iv) the power transformation of precipitation and (v) the variance scaling of temperature bias corrections. The individual bias correction methods had a significant effect on the climate classification, but the degree of this effect varied among the RCMs. Our results on the performance of bias correction differ from previous results described in the literature where these corrections were implemented over river catchments. We conclude that the effect of bias correction may depend on the region of model domain. These results suggest that distribution free bias correction approaches are the most suitable for large domain sizes such as the pan-European domain.

scholarly journals Exploring the Long-Term Reanalysis of Precipitation and the Contribution of Bias Correction to the Reduction of Uncertainty over South Korea: A Composite Gamma-Pareto Distribution Approach to the Bias Correction

2018 ◽  
Author(s):  
Dong-Ik Kim ◽  
Hyun-Han Kwon ◽  
Dawei Han
Keyword(s):  
South Korea ◽  
Gamma Distribution ◽  
Bias Correction ◽  
Pareto Distribution ◽  
Daily Precipitation ◽  
Regional Scale ◽  
Systematic Bias ◽  
Quantile Mapping ◽  
Contour Map

Abstract. The long-term record of precipitation data plays an important role in climate impact studies. The local observation is often considered to be the truth in regional-scale analyses, but the long-term meteorological record for a given catchment is very limited. Recently, ERA-20c, a century-long reanalysis of the data has been published by the European Centre for Medium-Range Weather Forecasts (ECMWF), which includes daily precipitation over the whole 20th century with high spatial resolution of 0.125° × 0.125°. Preliminary studies have already indicated that the ERA-20c can reproduce the mean reasonably well, but rainfall intensity was underestimated and wet-day frequency was overestimated. The primary focus of this study was to expand our sample size significantly for extreme rainfall analysis. Thus, we first adopted a relatively simple approach to adjust the frequency of wet-days by imposing an optimal lower threshold. We found that the systematic errors are fairly well captured by the conventional quantile mapping method with a gamma distribution, but the extremes in daily precipitation are still somewhat underestimated. In such a context, we introduced a quantile mapping approach based on a composite distribution of a generalized Pareto distribution for the upper tail (e.g. 95th and 99th percentile), and a gamma distribution for the interior part of the distribution. The proposed composite distributions provide a significant reduction of the biases compared with that of the conventional method for the extremes. We suggest a new interpolation method based on the parameter contour map for bias correction in ungauged catchments. The strength of this approach is that one can easily produce the bias-corrected daily precipitation in ungauged or poorly gauged catchments. A comparison of the corrected datasets using contour maps shows that the proposed modelling scheme can reliably reduce the systematic bias at a grid point that is not used in the process of parameter estimation. In particular, the contour map with the 99th percentile shows a more accurate representation of the observed daily rainfall than other combinations. The findings in this study suggest that the proposed approach can provide a useful alternative to readers who consider the bias correction of a regional-scale modelled data with a limited network of rain gauges. Although the study has been carried out in South Korea, the methodology has its potential to be applied in other parts of the world.

scholarly journals Application of Quantile Mapping Bias Correction for Mid-Future Precipitation Projections over Vietnam

SOLA ◽  
2019 ◽  
Vol 15 (0) ◽  
pp. 1-6 ◽  
Author(s):  
Long Trinh-Tuan ◽  
Jun Matsumoto ◽  
Fredolin T. Tangang ◽  
Liew Juneng ◽  
Faye Cruz ◽  
...  
Keyword(s):  
Bias Correction ◽  
Quantile Mapping ◽  
Mapping Bias ◽  
Precipitation Projections

Supplementary material to "A method to preserve trends in quantile mapping bias correction of climate modeled temperature"

2017 ◽  
Author(s):  
Manolis G. Grillakis ◽  
Aristeidis G. Koutroulis ◽  
Ioannis N. Daliakopoulos ◽  
Ioannis K. Tsanis
Keyword(s):  
Bias Correction ◽  
Quantile Mapping ◽  
Mapping Bias ◽  
Supplementary Material

scholarly journals Quantile Mapping Bias Correction on Rossby Centre Regional Climate Models for Precipitation Analysis over Kenya, East Africa

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 801 ◽  
Author(s):  
Brian Ayugi ◽  
Guirong Tan ◽  
Niu Ruoyun ◽  
Hassen Babaousmail ◽  
Moses Ojara ◽  
...  
Keyword(s):  
Bias Correction ◽  
Impact Analysis ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Absolute Error ◽  
Intermediate Step ◽  
Climate Data ◽  
Quantile Mapping ◽  
Mapping Bias

This study uses the quantile mapping bias correction (QMBC) method to correct the bias in five regional climate models (RCMs) from the latest output of the Rossby Center Climate Regional Model (RCA4) over Kenya. The outputs were validated using various scalar metrics such as root-mean-square difference (RMSD), mean absolute error (MAE), and mean bias. The study found that the QMBC algorithm demonstrates varying performance among the models in the study domain. The results show that most of the models exhibit reasonable improvement after corrections at seasonal and annual timescales. Specifically, the European Community Earth-System (EC-EARTH) and Commonwealth Scientific and Industrial Research Organization (CSIRO) models depict remarkable improvement as compared to other models. On the contrary, the Institute Pierre Simon Laplace Model CM5A-MR (IPSL-CM5A-MR) model shows little improvement across the rainfall seasons (i.e., March–May (MAM) and October–December (OND)). The projections forced with bias-corrected historical simulations tallied observed values demonstrate satisfactory simulations as compared to the uncorrected RCMs output models. This study has demonstrated that using QMBC on outputs from RCA4 is an important intermediate step to improve climate data before performing any regional impact analysis. The corrected models may be used in projections of drought and flood extreme events over the study area.

scholarly journals Bias correction can modify climate model-simulated precipitation changes without adverse affect on the ensemble mean

2013 ◽  
Vol 10 (9) ◽  
pp. 11585-11611
Author(s):  
E. P. Maurer ◽  
D. W. Pierce
Keyword(s):  
Bias Correction ◽  
General Circulation ◽  
Climate Model ◽  
General Circulation Models ◽  
Quantile Mapping ◽  
Mapping Bias ◽  
Simulated Precipitation ◽  
Before And After ◽  
Precipitation Changes ◽  
Precipitation Trends

Abstract. When applied to remove climate model biases in precipitation, quantile mapping can in some settings modify the simulated trends. This has important implications when the precipitation will be used to drive an impacts model that is sensitive to changes in precipitation. We use daily precipitation output from 12 general circulation models (GCMs) over the conterminous United States interpolated to a common 1° grid, and gridded observations aggregated to the same scale, to compare precipitation differences before and after quantile mapping bias correction. The change in seasonal mean (winter, DJF, and summer, JJA) precipitation between different 30-yr historical periods is compared to examine (1) the consensus among GCMs as to whether the bias correction tends to amplify or diminish their simulated precipitation trends, and (2) whether the modification of the change in precipitation tends to improve or degrade the correspondence to observed changes in precipitation for the same periods. In some cases, for a particular GCM, the trend modification can be as large as the original simulated change, though the areas where this occurs varies among GCMs so the ensemble median shows smaller trend modification. In specific locations and seasons the trend modification by quantile mapping improves correspondence with observed trends, and in others it degrades it. In the majority of the domain the ensemble median is for little effect on the correspondence of simulated precipitation trends with observed. This highlights the need to use an ensemble of GCMs rather than relying on a small number of models to estimate impacts.

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