Performance comparison of three predictor selection methods for statistical downscaling of daily precipitation
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Predictor selection is a critical factor affecting the statistical downscaling of daily precipitation. This study provides a general comparison between uncertainties in downscaled results from three commonly used predictor selection methods (correlation analysis, partial correlation analysis, and stepwise regression analysis). Uncertainty is analyzed by comparing statistical indices, including the mean, variance, and the distribution of monthly mean daily precipitation, wet spell length, and the number of wet days. The downscaled results are produced by the artificial neural network (ANN) statistical downscaling model and 50 years (1961–2010) of observed daily precipitation together with reanalysis predictors. Although results show little difference between downscaling methods, stepwise regression analysis is generally the best method for selecting predictors for the ANN statistical downscaling model of daily precipitation, followed by partial correlation analysis and then correlation analysis.
KeywordsPredictor selection methods Statistical downscaling Uncertainty assessment Ann
This work was supported by the National Natural Science Foundation of China (Grant No. 41190084) and the “Strategic Priority Research Program (B)” of the Chinese Academy of Sciences (Grant No. XDB03030204).
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