Volume 9, Issue 2 (9-2022)
Journal of Spatial Analysis Environmental Hazards 2022, 9(2): 21-34 |
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Bapirzadeh K, SeyedKaboli H, Najafi L. A comparative study of quantitative mapping methods for bias correction of ERA5 reanalysis precipitation data. Journal of Spatial Analysis Environmental Hazards 2022; 9 (2) :21-34
URL: http://jsaeh.khu.ac.ir/article-1-3296-en.html
URL: http://jsaeh.khu.ac.ir/article-1-3296-en.html
1- Jundi-Shapur University of Technology-Dezful
2- Jundi-Shapur University of Technology-Dezful ,hkaboli@jsu.ac.ir
2- Jundi-Shapur University of Technology-Dezful ,
Abstract: (2423 Views)
A comparative study of quantitative mapping methods for bias correction of ERA5 reanalysis precipitation data
Kaveh Bapirzadeh1, Hesam Seyed kaboli*2, Leila Najafi3
1 MSc student, Department of Civil Engineering, Jundi-Shapur University of Technology, Dezful, Iran.
*2 Associate Professor, Department of Civil Engineering, Jundi-Shapur University of Technology, Dezful, Iran. Corresponding Author: Email: hkaboli@jsu.ac.ir
3 Instructor, Department of Civil Engineering, Jundi-Shapur University of Technology, Dezful, Iran.
Abstract
This study evaluates the ability of different quantitative mapping (QM) methods as a bias correction technique for ERA5 reanalysis precipitation data. Climate type and geographical location can affect the performance of the bias correction method due to differences in precipitation characteristics. For this purpose, ERA5 reanalysis precipitation data for the years 1989-2019 for 10 selected synoptic stations in climates with different topographic characteristics were received daily from the European Centre for Medium-Range Weather Forecasts (ECMWF) website. Bias correction of these data was performed using 5 quantitative mapping methods based on observational data in R software environment. Two-part evaluation and Taylor diagram were used to compare the performance of different methods. The results showed that the performance of the quantification mapping method depends on the performance functions, set of parameters and climatic conditions. In general, non-parametric methods of multiple mapping have better performance than parametric methods, so that the best performance is related to QUANT and RQUANT methods, among which DIST method has the weakest performance.
Keywords: Quantitative mapping, Bias correction, ERA5, ECMWF
Kaveh Bapirzadeh1, Hesam Seyed kaboli*2, Leila Najafi3
1 MSc student, Department of Civil Engineering, Jundi-Shapur University of Technology, Dezful, Iran.
*2 Associate Professor, Department of Civil Engineering, Jundi-Shapur University of Technology, Dezful, Iran. Corresponding Author: Email: hkaboli@jsu.ac.ir
3 Instructor, Department of Civil Engineering, Jundi-Shapur University of Technology, Dezful, Iran.
Abstract
This study evaluates the ability of different quantitative mapping (QM) methods as a bias correction technique for ERA5 reanalysis precipitation data. Climate type and geographical location can affect the performance of the bias correction method due to differences in precipitation characteristics. For this purpose, ERA5 reanalysis precipitation data for the years 1989-2019 for 10 selected synoptic stations in climates with different topographic characteristics were received daily from the European Centre for Medium-Range Weather Forecasts (ECMWF) website. Bias correction of these data was performed using 5 quantitative mapping methods based on observational data in R software environment. Two-part evaluation and Taylor diagram were used to compare the performance of different methods. The results showed that the performance of the quantification mapping method depends on the performance functions, set of parameters and climatic conditions. In general, non-parametric methods of multiple mapping have better performance than parametric methods, so that the best performance is related to QUANT and RQUANT methods, among which DIST method has the weakest performance.
Keywords: Quantitative mapping, Bias correction, ERA5, ECMWF
Type of Study: Research |
Subject:
Special
Received: 2022/02/13 | Accepted: 2022/07/31 | Published: 2022/09/20
Received: 2022/02/13 | Accepted: 2022/07/31 | Published: 2022/09/20
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