Editorial Type:
Article
Article Type:
Research Article

Seasonal Forecasting of North China Summer Rainfall Using a Statistical Downscaling Model

Yan Guo State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, and State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Jianping Li College of Global Change and Earth System Science, Beijing Normal University, and State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Yun Li CSIRO Computational Informatics, CSIRO Climate Adaptation Flagship, Wembley, Western Australia, Australia

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Print Publication:
01 Jul 2014
DOI:
https://doi.org/10.1175/JAMC-D-13-0207.1
Page(s):
1739–1749
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Abstract

A statistical downscaling model was developed with reanalysis data and applied to forecast northern China summer rainfall (NCSR) using the outputs of the real-time seasonal Climate Forecast System, version 2 (CFSv2). Large-scale climate signals in sea level pressure, 850-hPa meridional wind, and 500-hPa geopotential height as well as several well-known climate indices were considered as potential predictors. Through correlation analysis and stepwise screening, two “optimal” predictors (i.e., sea level pressure over the southwestern Indian Ocean and 850-hPa meridional wind over eastern China) were selected to fit the regression equation. Model reliability was validated with independent data during a test period (1991–2012), in which the simulated NCSR well represented the observed variability with a correlation coefficient of 0.59 and a root-mean-square error of 18.6%. The statistical downscaling model was applied to forecast NCSR for a 22-yr period (1991–2012) using forecast predictors from the CFSv2 with lead times from 1 to 6 months. The results showed much better forecast skills than that directly from the CFSv2 for all lead months, except the 3-month-lead example. The biggest improvement occurred in the 1-month-lead forecast, in which the hit rate increased to 77.3% from 45.5% in the CFSv2 forecast. In the forecast of rainfall at 15 stations, the statistical downscaling model also showed superior capability when compared with the CFSv2, with forecast skill being improved at 73% of stations. In particular, 13 of 15 stations obtained a hit rate exceeding 55%.

Corresponding author address: Dr. Jianping Li, State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China. E-mail: ljp@lasg.iap.ac.cn

Abstract

A statistical downscaling model was developed with reanalysis data and applied to forecast northern China summer rainfall (NCSR) using the outputs of the real-time seasonal Climate Forecast System, version 2 (CFSv2). Large-scale climate signals in sea level pressure, 850-hPa meridional wind, and 500-hPa geopotential height as well as several well-known climate indices were considered as potential predictors. Through correlation analysis and stepwise screening, two “optimal” predictors (i.e., sea level pressure over the southwestern Indian Ocean and 850-hPa meridional wind over eastern China) were selected to fit the regression equation. Model reliability was validated with independent data during a test period (1991–2012), in which the simulated NCSR well represented the observed variability with a correlation coefficient of 0.59 and a root-mean-square error of 18.6%. The statistical downscaling model was applied to forecast NCSR for a 22-yr period (1991–2012) using forecast predictors from the CFSv2 with lead times from 1 to 6 months. The results showed much better forecast skills than that directly from the CFSv2 for all lead months, except the 3-month-lead example. The biggest improvement occurred in the 1-month-lead forecast, in which the hit rate increased to 77.3% from 45.5% in the CFSv2 forecast. In the forecast of rainfall at 15 stations, the statistical downscaling model also showed superior capability when compared with the CFSv2, with forecast skill being improved at 73% of stations. In particular, 13 of 15 stations obtained a hit rate exceeding 55%.

Corresponding author address: Dr. Jianping Li, State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China. E-mail: ljp@lasg.iap.ac.cn
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