A parsimonious post-processor for uncertainty evaluation of ensemble precipitation forecasts: an application to quantitative precipitation forecasts for civil protection purposes

In this study, we present a post-processing technique designed to assess conditional predictive uncertainty for quantitative precipitation forecasts (QPFs) that extends to the multivariate predictor case. The theoretical foundation of the mixed binary-continuous precipitation process representation for a single predictor is coupled with the univariate version of the model conditional processor (MCP) that allows considering ensemble forecasts while maintaining a parsimonious approach. The experiment set-up is based on a weather vigilance zone (WVZ) of the national warning system in southern Italy and the relative QPFs issued by the Italian Civil Protection Department. Various aspects of the quality of the probabilistic forecast from the uncertainty processor were evaluated. The results indicate that post-processed forecasts can provide improved performances in terms of accuracy and reliability, tend to correct bias and are generally less under-dispersive than raw forecasts for the investigated area. Furthermore, we explored the possibility of issuing warnings employing the full predictive distribution and moving to the use of probabilistic thresholds here identified through a receiver operating characteristic (ROC) analysis. Considering the probability of exceeding a critical rainfall value allowed successful discrimination between events and non-events for critical precipitation occurrences and proved to be a valuable approach to decision-makers and information providers.