2013 Fiscal Year Final Research Report
Statistical Downscaling and Uncertainty Identification for Multi-scale AGCM Output
| Project/Area Number |
23760459
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Hydraulic engineering
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| Research Institution | Kyoto University |
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Principal Investigator |
KIM Sunmin 京都大学, 工学研究科, 准教授 (10546013)
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| Project Period (FY) |
2011 – 2013
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| Keywords | 統計的DS / 気候変動 / 降水データ |
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| Research Abstract |
The purpose of this research is to develop a simple yet efficient statistical downscaling (SDS) method to downscale 60-km AGCM output into 20-km resolution for the precipitation data. We has successfully developed a new technique of statistical downscaling considering the spatial correlation structure of precipitation. Here, the downscaling target is 60-km resolution of daily precipitation for 20-km resolution data, which is based on a downscaling window having (3x60-km)x(3x60-km) of area. The proposed regression model provides very effective and efficient results with a certain level of estimation error.
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