Statistical Downscaling of AGCM60km Precipitation based on Spatial Correlation of AGCM20km Output

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

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 3×3 60-km grid, and the downscaling target is 3×3 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

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