PostDateIcon 2月 18th, 2017

Sunmin Kim, Yasuto Tachikawa, Eiichi Nakakita
Received 2016年09月21日, Accepted 2017年01月22日, Published 2017年02月18日

Sunmin Kim1), Yasuto Tachikawa1), Eiichi Nakakita2)

1) Graduate School of Engineering, Kyoto University
2) Disaster Prevention Research Institute, Kyoto University

A statistical downscaling method based on regressing precipitation data is introduced and applied to 60-km resolution Atmospheric General Circulation Model (AGCM60km) output for daily precipitation. The method utilizes a regression domain with a ×ばつ3 60-km grid, and the downscaling target is ×ばつ3 20-km grids in the center of the regression domain. By shifting the regression domain one grid by one grid in 60-km resolution, the same form of regression model, but different regression coefficients for each 20-km grid, can be applied to all the downscaling target areas. Based on application tests for the Asian Monsoon region, the statistical downscaling algorithm shows extremely effective results with a certain pattern of regression error. The monthly based downscaled results from AGCM60km output shows a rather good match to the monthly mean precipitation amount of AGCM20km. The downscaled results also show a plausible mimic to the AGCM20km output in the frequency of daily precipitation amounts; however, the results showed noticeable limitations in simulating low rainfall amounts (e.g., less than 5 mm d–1), especially on land.

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Copyright (c) 2017 The Author(s) CC-BY 4.0