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A Comparison of Statistical Approaches for Seasonal Precipitation Prediction in Pakistan

Ting DingNational Climate Center, China Meteorological Administration, Beijing, China

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Zongjian KeNational Climate Center, China Meteorological Administration, Beijing, China

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Abstract

The present study focuses on two statistical approaches for improving seasonal precipitation prediction skills for Pakistan. Precipitation over Pakistan is concentrated in July–August (JA), when droughts and floods occur recurrently and cause disasters. Empirical orthogonal function (EOF) analysis is used to assess spatial patterns of precipitation, and two precipitation patterns are identified: a consistent pattern and a north–south dipole pattern. Two statistical approaches, the statistical regression method using prewinter predictors and statistical downscaling, are employed to perform rainfall predictions for JA in Pakistan. Linear regression (LR) and optimal subset regression (OSR) are used for each approach, and the regression forecast methods are compared with the raw model outputs. Historical data for large-scale variables from the NCEP–NCAR reanalysis and version 1.0 of the coupled atmosphere–ocean general circulation model from the Beijing Climate Center (CGCM1.0/BCC) outputs in 1986–2011 are used as predictors for the statistical prewinter method and statistical downscaling, respectively. In the majority of the years, the statistical prewinter method and statistical downscaling are able to correct the erroneous signs of the raw dynamical model output for the consistent pattern. The statistical prewinter method is found to provide more skillful predictions than the statistical downscaling on the prediction of the dipolelike pattern. The best prediction skills for the consistent pattern and dipolelike pattern are provided by NCEP-OSR and NCEP-LR, which have significant correlations of 0.39 and 0.40, respectively. For all the forecast methods in this study, prewinter prediction and downscaled prediction show considerable improvements when compared with model output. These statistical methods provide valuable approaches for studying local climates.

Corresponding author address: Ting Ding, National Climate Center, China Meteorological Administration, No. 46, Zhongguancun South Street, Haidian District, Beijing 100081, China. E-mail: dingting@cma.gov.cn

Abstract

The present study focuses on two statistical approaches for improving seasonal precipitation prediction skills for Pakistan. Precipitation over Pakistan is concentrated in July–August (JA), when droughts and floods occur recurrently and cause disasters. Empirical orthogonal function (EOF) analysis is used to assess spatial patterns of precipitation, and two precipitation patterns are identified: a consistent pattern and a north–south dipole pattern. Two statistical approaches, the statistical regression method using prewinter predictors and statistical downscaling, are employed to perform rainfall predictions for JA in Pakistan. Linear regression (LR) and optimal subset regression (OSR) are used for each approach, and the regression forecast methods are compared with the raw model outputs. Historical data for large-scale variables from the NCEP–NCAR reanalysis and version 1.0 of the coupled atmosphere–ocean general circulation model from the Beijing Climate Center (CGCM1.0/BCC) outputs in 1986–2011 are used as predictors for the statistical prewinter method and statistical downscaling, respectively. In the majority of the years, the statistical prewinter method and statistical downscaling are able to correct the erroneous signs of the raw dynamical model output for the consistent pattern. The statistical prewinter method is found to provide more skillful predictions than the statistical downscaling on the prediction of the dipolelike pattern. The best prediction skills for the consistent pattern and dipolelike pattern are provided by NCEP-OSR and NCEP-LR, which have significant correlations of 0.39 and 0.40, respectively. For all the forecast methods in this study, prewinter prediction and downscaled prediction show considerable improvements when compared with model output. These statistical methods provide valuable approaches for studying local climates.

Corresponding author address: Ting Ding, National Climate Center, China Meteorological Administration, No. 46, Zhongguancun South Street, Haidian District, Beijing 100081, China. E-mail: dingting@cma.gov.cn
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