scholarly journals Bias Correction of RCP-based Future Extreme Precipitation using a Quantile Mapping Method ; for 20-Weather Stations of South Korea

2012 ◽  
Vol 54 (6) ◽  
pp. 133-142 ◽  
Author(s):  
Jihoon Park ◽  
Moon Seong Kang ◽  
Inhong Song
Keyword(s):  
South Korea ◽  
Bias Correction ◽  
Extreme Precipitation ◽  
Mapping Method ◽  
Quantile Mapping ◽  
Weather Stations

scholarly journals Probability Distributions for a Quantile Mapping Technique for a Bias Correction of Precipitation Data: A Case Study to Precipitation Data Under Climate Change

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1475 ◽  
Author(s):  
Jun-Haeng Heo ◽  
Hyunjun Ahn ◽  
Ju-Young Shin ◽  
Thomas Rodding Kjeldsen ◽  
Changsam Jeong
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Shape Parameter ◽  
Mapping Method ◽  
Distribution Model ◽  
Parameter Distribution ◽  
Precipitation Data ◽  
Distribution Models ◽  
Quantile Mapping

The quantile mapping method is a bias correction method that leads to a good performance in terms of precipitation. Selecting an appropriate probability distribution model is essential for the successful implementation of quantile mapping. Probability distribution models with two shape parameters have proved that they are fit for precipitation modeling because of their flexibility. Hence, the application of a two-shape parameter distribution will improve the performance of the quantile mapping method in the bias correction of precipitation data. In this study, the applicability and appropriateness of two-shape parameter distribution models are examined in quantile mapping, for a bias correction of simulated precipitation data from a climate model under a climate change scenario. Additionally, the impacts of distribution selection on the frequency analysis of future extreme precipitation from climate are investigated. Generalized Lindley, Burr XII, and Kappa distributions are used, and their fits and appropriateness are compared to those of conventional distributions in a case study. Applications of two-shape parameter distributions do lead to better performances in reproducing the statistical characteristics of observed precipitation, compared to those of conventional distributions. The Kappa distribution is considered the best distribution model, as it can reproduce reliable spatial dependences of the quantile corresponding to a 100-year return period, unlike the gamma distribution.

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 Nonparametric quantile mapping using the response surface method – bias correction of daily precipitation

2017 ◽  
Vol 9 (3) ◽  
pp. 525-539
Author(s):  
Taeho Bong ◽  
Young-Hwan Son ◽  
Seung-Hwan Yoo ◽  
Sye-Woon Hwang
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
Bias Correction ◽  
Climate Models ◽  
Regional Climate ◽  
Mapping Method ◽  
Quantile Mapping ◽  
The Past ◽  
Surface Method ◽  
Simulated Precipitation

Abstract Currently, regional climate models are widely used to provide projections of how climate may change locally. However, they sometimes have a spatial resolution that is too coarse to provide an appropriate resolution for the local scale. In this paper, a new nonparametric quantile mapping method based on the response surface method was proposed to perform an efficient and robust bias correction. The proposed method was applied to correct the bias of the simulated precipitation for the period of 1976–2005, and the performance and uncertainty were subsequently assessed. As a result, the proposed method was effectively able to reduce the biases of the entire distribution range, and to predict new extreme precipitation. The future precipitation based on representative concentration pathways of RCP 4.5 and 8.5 were bias corrected using the proposed method, and the impacts of the climate scenarios were compared. It was found that the average annual precipitations increased compared to the past for both scenarios, and they tended to increase over time in the three studied areas. The uncertainty of future precipitation was slightly higher than in the past observation period.

scholarly journals Reanalysis Product-Based Nonstationary Frequency Analysis for Estimating Extreme Design Rainfall

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 191
Author(s):  
Dong-IK Kim ◽  
Dawei Han ◽  
Taesam Lee
Keyword(s):  
South Korea ◽  
20Th Century ◽  
Frequency Analysis ◽  
Systematic Errors ◽  
Quantile Mapping ◽  
Reanalysis Product ◽  
Rainfall Analysis ◽  
Future Evolution ◽  
Design Rainfall ◽  
Nonstationary Frequency

Nonstationarity is one major issue in hydrological models, especially in design rainfall analysis. Design rainfalls are typically estimated by annual maximum rainfalls (AMRs) of observations below 50 years in many parts of the world, including South Korea. However, due to the lack of data, the time-dependent nature may not be sufficiently identified by this classic approach. Here, this study aims to explore design rainfall with nonstationary condition using century-long reanalysis products that help one to go back to the early 20th century. Despite its useful representation of the past climate, the reanalysis products via observational data assimilation schemes and models have never been tested in representing the nonstationary behavior in extreme rainfall events. We used daily precipitations of two century-long reanalysis datasets as the ERA-20c by the European Centre for Medium-Range Weather Forecasts (ECMWF) and the 20th century reanalysis (20CR) by the National Oceanic and Atmospheric Administration (NOAA). The AMRs from 1900 to 2010 were derived from the grids over South Korea. The systematic errors were downgraded through quantile delta mapping (QDM), as well as conventional stationary quantile mapping (SQM). The evaluation result of the bias-corrected AMRs indicated the significant reduction of the errors. Furthermore, the AMRs present obvious increasing trends from 1900 to 2010. With the bias-corrected values, we carried out nonstationary frequency analysis based on the time-varying location parameters of generalized extreme value (GEV) distribution. Design rainfalls with certain return periods were estimated based on the expected number of exceedance (ENE) interpretation. Although there is a significant range of uncertainty, the design quantiles by the median parameters showed the significant relative difference, from −30.8% to 42.8% for QDM, compared with the quantiles by the multi-decadal observations. Even though the AMRs from the reanalysis products are challenged by various errors such as quantile mapping (QM) and systematic errors, the results from the current study imply that the proposed scheme with employing the reanalysis product might be beneficial to predict the future evolution of extreme precipitation and to estimate the design rainfall accordingly.

scholarly journals Evaluation of NASA POWER Reanalysis Products to Estimate Daily Weather Variables in a Hot Summer Mediterranean Climate

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1207
Author(s):  
Gonçalo C. Rodrigues ◽  
Ricardo P. Braga
Keyword(s):  
Wind Speed ◽  
Bias Correction ◽  
Goodness Of Fit ◽  
Mediterranean Climate ◽  
Weather Data ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Weather Variables ◽  
Weather Stations

This study aims to evaluate NASA POWER reanalysis products for daily surface maximum (Tmax) and minimum (Tmin) temperatures, solar radiation (Rs), relative humidity (RH) and wind speed (Ws) when compared with observed data from 14 distributed weather stations across Alentejo Region, Southern Portugal, with a hot summer Mediterranean climate. Results showed that there is good agreement between NASA POWER reanalysis and observed data for all parameters, except for wind speed, with coefficient of determination (R2) higher than 0.82, with normalized root mean square error (NRMSE) varying, from 8 to 20%, and a normalized mean bias error (NMBE) ranging from –9 to 26%, for those variables. Based on these results, and in order to improve the accuracy of the NASA POWER dataset, two bias corrections were performed to all weather variables: one for the Alentejo Region as a whole; another, for each location individually. Results improved significantly, especially when a local bias correction is performed, with Tmax and Tmin presenting an improvement of the mean NRMSE of 6.6 °C (from 8.0 °C) and 16.1 °C (from 20.5 °C), respectively, while a mean NMBE decreased from 10.65 to 0.2%. Rs results also show a very high goodness of fit with a mean NRMSE of 11.2% and mean NMBE equal to 0.1%. Additionally, bias corrected RH data performed acceptably with an NRMSE lower than 12.1% and an NMBE below 2.1%. However, even when a bias correction is performed, Ws lacks the performance showed by the remaining weather variables, with an NRMSE never lower than 19.6%. Results show that NASA POWER can be useful for the generation of weather data sets where ground weather stations data is of missing or unavailable.

scholarly journals Projecting Health Impacts of Future Temperature: A Comparison of Quantile-Mapping Bias-Correction Methods

2021 ◽  
Vol 18 (4) ◽  
pp. 1992
Author(s):  
Weijia Qian ◽  
Howard H. Chang
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Warm Season ◽  
Climate Science ◽  
Regional Climate Models ◽  
Health Impact ◽  
Quantile Mapping ◽  
Ed Visits

Health impact assessments of future environmental exposures are routinely conducted to quantify population burdens associated with the changing climate. It is well-recognized that simulations from climate models need to be bias-corrected against observations to estimate future exposures. Quantile mapping (QM) is a technique that has gained popularity in climate science because of its focus on bias-correcting the entire exposure distribution. Even though improved bias-correction at the extreme tails of exposure may be particularly important for estimating health burdens, the application of QM in health impact projection has been limited. In this paper we describe and apply five QM methods to estimate excess emergency department (ED) visits due to projected changes in warm-season minimum temperature in Atlanta, USA. We utilized temperature projections from an ensemble of regional climate models in the North American-Coordinated Regional Climate Downscaling Experiment (NA-CORDEX). Across QM methods, we estimated consistent increase in ED visits across climate model ensemble under RCP 8.5 during the period 2050 to 2099. We found that QM methods can significantly reduce between-model variation in health impact projections (50–70% decreases in between-model standard deviation). Particularly, the quantile delta mapping approach had the largest reduction and is recommended also because of its ability to preserve model-projected absolute temporal changes in quantiles.

scholarly journals Constructing High-Resolution, Bias-Corrected Climate Products: A Comparison of Methods

2021 ◽  
Vol 60 (4) ◽  
pp. 455-475
Author(s):  
Maike F. Holthuijzen ◽  
Brian Beckage ◽  
Patrick J. Clemins ◽  
Dave Higdon ◽  
Jonathan M. Winter
Keyword(s):  
High Resolution ◽  
Bias Correction ◽  
Climate Model ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Model Output ◽  
Quantile Mapping ◽  
Station Data ◽  
Climate Model Output ◽  
Mapping Techniques

AbstractHigh-resolution, bias-corrected climate data are necessary for climate impact studies at local scales. Gridded historical data are convenient for bias correction but may contain biases resulting from interpolation. Long-term, quality-controlled station data are generally superior climatological measurements, but because the distribution of climate stations is irregular, station data are challenging to incorporate into downscaling and bias-correction approaches. Here, we compared six novel methods for constructing full-coverage, high-resolution, bias-corrected climate products using daily maximum temperature simulations from a regional climate model (RCM). Only station data were used for bias correction. We quantified performance of the six methods with the root-mean-square-error (RMSE) and Perkins skill score (PSS) and used two ANOVA models to analyze how performance varied among methods. We validated the six methods using two calibration periods of observed data (1980–89 and 1980–2014) and two testing sets of RCM data (1990–2014 and 1980–2014). RMSE for all methods varied throughout the year and was larger in cold months, whereas PSS was more consistent. Quantile-mapping bias-correction techniques substantially improved PSS, while simple linear transfer functions performed best in improving RMSE. For the 1980–89 calibration period, simple quantile-mapping techniques outperformed empirical quantile mapping (EQM) in improving PSS. When calibration and testing time periods were equivalent, EQM resulted in the largest improvements in PSS. No one method performed best in both RMSE and PSS. Our results indicate that simple quantile-mapping techniques are less prone to overfitting than EQM and are suitable for processing future climate model output, whereas EQM is ideal for bias correcting historical climate model output.

scholarly journals The use of personal weather station observations to improve precipitation estimation and interpolation

2021 ◽  
Vol 25 (2) ◽  
pp. 583-601
Author(s):  
András Bárdossy ◽  
Jochen Seidel ◽  
Abbas El Hachem
Keyword(s):  
Spatial Interpolation ◽  
Empirical Distribution ◽  
Precipitation Amount ◽  
Distribution Functions ◽  
Quantile Mapping ◽  
Novel Approach ◽  
Precipitation Estimation ◽  
Weather Stations ◽  
High Precipitation ◽  
Empirical Distribution Functions

Abstract. The number of personal weather stations (PWSs) with data available through the internet is increasing gradually in many parts of the world. The purpose of this study is to investigate the applicability of these data for the spatial interpolation of precipitation using a novel approach based on indicator correlations and rank statistics. Due to unknown errors and biases of the observations, rainfall amounts from the PWS network are not considered directly. Instead, it is assumed that the temporal order of the ranking of these data is correct. The crucial step is to find the stations which fulfil this condition. This is done in two steps – first, by selecting the locations using the time series of indicators of high precipitation amounts. Then, the remaining stations are then checked for whether they fit into the spatial pattern of the other stations. Thus, it is assumed that the quantiles of the empirical distribution functions are accurate. These quantiles are then transformed to precipitation amounts by a quantile mapping using the distribution functions which were interpolated from the information from the German National Weather Service (Deutscher Wetterdienst – DWD) data only. The suggested procedure was tested for the state of Baden-Württemberg in Germany. A detailed cross validation of the interpolation was carried out for aggregated precipitation amount of 1, 3, 6, 12 and 24 h. For each of these temporal aggregations, nearly 200 intense events were evaluated, and the improvement of the interpolation was quantified. The results show that the filtering of observations from PWSs is necessary as the interpolation error after the filtering and data transformation decreases significantly. The biggest improvement is achieved for the shortest temporal aggregations.

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