An effective post-processing of the North American multi-model ensemble (NMME) precipitation forecasts over the continental US

Abstract

The North American multi-model ensemble (NMME) forecast system provides valuable information for climate variables at different lead times over the globe. A Bayesian ensemble post-processing approach based on Copula functions (COP-EPP) is employed to bias-correct the NMME precipitation forecast from 11 models (totaling 128 ensemble members). The study is conducted over the Continental United States (CONUS) for the hindcast period of 1982–2010 at lead-0, and the forecast period of 2012–2015 at four different lead times of lead-0 to lead-3 and the results are verified using deterministic and probabilistic measures. COP-EPP is compared with a widely-used bias-correction technique, the Quantile Mapping (QM). Although the NMME forecasts show to be more accurate across the eastern United States, large bias is found over the great plains of the central US. However, QM and COP-EPP present significant improvements over the NMME forecasts, with COP-EPP proving to be more reliable and accurate across the CONUS. In addition, COP-EPP substantially improves the temporal and spatial variability of the intra-seasonal NMME forecasts, even at lead-3.