i Tian; Christopher J. Martinez; Wendy D. Graham; Syewoon Hwang.. Statistical downscaling multi-model forecasts for seasonal precipitation and surface temperature over southeastern USA
Journal of Climate, 2014-09-15
Abstract: This study compared two types of approaches to downscale seasonal precipitation (P) and 2 meter air temperature (T2M) forecasts from the North American National Multi-Model Ensemble (NMME) over the states of Alabama, Georgia, and Florida in the southeastern United States (SEUS). Each NMME model forecast was evaluated. Two MME schemes were tested by assigning equal weight to all forecast members (SuperEns) or by assigning equal weights to each models’ ensemble mean (MeanEns). One type of downscaling approach used was a model output statistics (MOS) method, which was based on direct spatial disaggregation and bias correction of the NMME P and T2M forecasts using the quantile mapping technique (SDBC). The other type of approach used was a perfect prognosis (PP) approach using nonparametric locally weighted polynomial regression (LWPR) models, which used the NMME forecasts of Niño3.4 sea surface temperatures (SSTs) to predict local-scale P and T2M. Both SDBC and LWPR downscaled P showed skill in winter but no skill or limited skill in summer at all leads for all NMME models. The SDBC downscaled T2M were skillful only for the CFSv2 model even at far leads, whereas the LWPR downscaled T2M showed limited skill or no skill for all NMME models. In many cases, the LWPR method showed significantly higher skill than the SDBC. After bias correction, the SuperEns mostly showed higher skill than the MeanEns and most of the single models, but its skill did not outperform the best single model.