Application of Dynamical and Statistical Downscaling to East Asian Summer Precipitation for Finely Resolved Datasets
Various downscaling approaches have been developed to overcome the limitation of the coarse spatial resolution of general circulation models (GCMs). Such techniques can be grouped into two approaches of dynamical and statistical downscaling. In this study, we investigated the performances of differe...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2017/2956373 |
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| author | Yoo-Bin Yhang Soo-Jin Sohn Il-Won Jung |
| author_facet | Yoo-Bin Yhang Soo-Jin Sohn Il-Won Jung |
| author_sort | Yoo-Bin Yhang |
| collection | DOAJ |
| description | Various downscaling approaches have been developed to overcome the limitation of the coarse spatial resolution of general circulation models (GCMs). Such techniques can be grouped into two approaches of dynamical and statistical downscaling. In this study, we investigated the performances of different downscaling methods, focusing on East Asian summer monsoon precipitation to obtain more finely resolved and value added datasets. The dynamical downscaling was conducted by the Regional Model Program (RMP) of the Global/Regional Integrated Model system (GRIMs), while the statistical downscaling was performed through coupled pattern-based simple linear regression. The dynamical downscaling resulted in a better representation of the spatial distribution and long-term trend than the GCM produced; however, it tended to overestimate precipitation over East Asia. In contrast, the application of the statistical downscaling resulted in a bias in the amount of precipitation, due to low variance that is inherent in regression-based downscaling. A combination of dynamical and statistical downscaling produced the best results in time and space. This study provides a guideline for determining the most effective and robust downscaling method in the hydrometeorological applications, which are quite sensitive to the accuracy of downscaled precipitation. |
| format | Article |
| id | doaj-art-3bfe99c48be242dab239bbd8835ea3e1 |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-3bfe99c48be242dab239bbd8835ea3e12025-02-03T01:07:53ZengWileyAdvances in Meteorology1687-93091687-93172017-01-01201710.1155/2017/29563732956373Application of Dynamical and Statistical Downscaling to East Asian Summer Precipitation for Finely Resolved DatasetsYoo-Bin Yhang0Soo-Jin Sohn1Il-Won Jung2Climate Prediction Department, APEC Climate Center (APCC), Busan, Republic of KoreaClimate Prediction Department, APEC Climate Center (APCC), Busan, Republic of KoreaResearch Institute for Infrastructure Performance, Korea Infrastructure Safety & Technology Corporation, Jinju, Republic of KoreaVarious downscaling approaches have been developed to overcome the limitation of the coarse spatial resolution of general circulation models (GCMs). Such techniques can be grouped into two approaches of dynamical and statistical downscaling. In this study, we investigated the performances of different downscaling methods, focusing on East Asian summer monsoon precipitation to obtain more finely resolved and value added datasets. The dynamical downscaling was conducted by the Regional Model Program (RMP) of the Global/Regional Integrated Model system (GRIMs), while the statistical downscaling was performed through coupled pattern-based simple linear regression. The dynamical downscaling resulted in a better representation of the spatial distribution and long-term trend than the GCM produced; however, it tended to overestimate precipitation over East Asia. In contrast, the application of the statistical downscaling resulted in a bias in the amount of precipitation, due to low variance that is inherent in regression-based downscaling. A combination of dynamical and statistical downscaling produced the best results in time and space. This study provides a guideline for determining the most effective and robust downscaling method in the hydrometeorological applications, which are quite sensitive to the accuracy of downscaled precipitation.http://dx.doi.org/10.1155/2017/2956373 |
| spellingShingle | Yoo-Bin Yhang Soo-Jin Sohn Il-Won Jung Application of Dynamical and Statistical Downscaling to East Asian Summer Precipitation for Finely Resolved Datasets Advances in Meteorology |
| title | Application of Dynamical and Statistical Downscaling to East Asian Summer Precipitation for Finely Resolved Datasets |
| title_full | Application of Dynamical and Statistical Downscaling to East Asian Summer Precipitation for Finely Resolved Datasets |
| title_fullStr | Application of Dynamical and Statistical Downscaling to East Asian Summer Precipitation for Finely Resolved Datasets |
| title_full_unstemmed | Application of Dynamical and Statistical Downscaling to East Asian Summer Precipitation for Finely Resolved Datasets |
| title_short | Application of Dynamical and Statistical Downscaling to East Asian Summer Precipitation for Finely Resolved Datasets |
| title_sort | application of dynamical and statistical downscaling to east asian summer precipitation for finely resolved datasets |
| url | http://dx.doi.org/10.1155/2017/2956373 |
| work_keys_str_mv | AT yoobinyhang applicationofdynamicalandstatisticaldownscalingtoeastasiansummerprecipitationforfinelyresolveddatasets AT soojinsohn applicationofdynamicalandstatisticaldownscalingtoeastasiansummerprecipitationforfinelyresolveddatasets AT ilwonjung applicationofdynamicalandstatisticaldownscalingtoeastasiansummerprecipitationforfinelyresolveddatasets |