资源环境科技发展态势分析平台

Selection of suitable predictors for downscaling local-scale precipitation from the wide range of large-scale predictors available in National Center for Atmospheric Research/National Centers for Environmental Prediction (NCAR/NCEP) reanalysis is a challenging task because of the existence of the complex interactions between local-scale predictands and large-scale predictor fields. An attempt was made to assess how well different large-scale predictors were able to reproduce local-scale monsoon precipitation over seven homogeneous zones of India through statistical downscaling. For calibration of downscaling (DS) models, the principal component (PC)-based multiple linear regression approach was adopted where each raw grid-point predictor field transformed into PCs using empirical orthogonal function (EOF) analysis. The predictors consistently producing better downscaled results across four nonoverlapping calibration and validation periods were identified as superior predictor (SP). It was found that some common predictors like precipitable water; specific and relative humidity at different levels have emerged as SP predictors over several zones. In general, SP predictors have not been much sensitive with small changes in the domain size. However, a decline in performances of DS models was noticed for the majority of SP predictors for a large increase in the size of domains. Especially, the largest South Asia domain has been the most inappropriate domain as very few predictors found to be suitable for downscaling. In general, about 40% out of 36 numbers of combined predictors were identified as potential SP predictors over the majority of the zones. Several numbers of combined SP predictors have also produced slightly superior skills compared to single SP predictors. In many cases, predictors showing poor performance as single predictors have produced improved performances when combined with other predictors.