Statistical refinement of the North American Multi-Model Ensemble precipitation forecasts over Karoon basin, Iran

Abstract

An effective postprocessing approach has been examined to improve the skill of North American Multi-Model Ensemble (NMME) precipitation forecasts in the Karoon basin, Iran. The Copula–Bayesian approach was used along with the Normal Kernel Density marginal distribution and the Kernel Copula function. This process creates more than one postprocessing precipitation value as result candidates (first pass). A similar process is used for a second pass to obtain preprocessed values based on the candidate inputs, which helps identify the most suitable postprocessed value. The application of the technique for order preference by similarity to ideal solution method based on conditional probability distribution functions of the first and second passes leads to achieving final improved forecast data among the existing candidates. To validate the results, the data from 1982 to 2010 and 2011 to 2018 were used for the calibration period and the forecast period. The results show that while the GFDL and CFS2 models tend to overestimate precipitation, most other NMME models underestimate it. Postprocessing improves the accuracy of precipitation forecasts for most models by 20–40%. Overall, the proposed Copula–Bayesian postprocessing approach could provide more reliable forecasts with higher spatial and temporal consistency, better detection of extreme values of precipitation, and a significant reduction in uncertainties in comparison with raw data for various lengths of time.