Probabilistic Quantitative Precipitation Forecasting Using Ensemble Model Output Statistics

TL;DR

This paper introduces a new statistical post-processing method for ensemble weather forecasts that generates calibrated probabilistic precipitation predictions using a generalized extreme value distribution, improving forecast accuracy over existing methods.

Contribution

The paper presents a novel EMOS approach modeling precipitation with a GEV distribution, including spatial variability and neighborhood information, enhancing probabilistic forecast calibration.

Findings

The EMOS method produces well-calibrated, sharp precipitation forecasts.

Incorporating neighborhood information improves predictive performance.

The approach outperforms existing logistic regression and Bayesian model averaging methods.

Abstract

Statistical post-processing of dynamical forecast ensembles is an essential component of weather forecasting. In this article, we present a post-processing method that generates full predictive probability distributions for precipitation accumulations based on ensemble model output statistics (EMOS). We model precipitation amounts by a generalized extreme value distribution that is left-censored at zero. This distribution permits modelling precipitation on the original scale without prior transformation of the data. A closed form expression for its continuous rank probability score can be derived and permits computationally efficient model fitting. We discuss an extension of our approach that incorporates further statistics characterizing the spatial variability of precipitation amounts in the vicinity of the location of interest. The proposed EMOS method is applied to daily 18-h forecasts of 6-h accumulated precipitation over Germany in 2011 using the COSMO-DE ensemble prediction system operated by the German Meteorological Service. It yields calibrated and sharp predictive distributions and compares favourably with extended logistic regression and Bayesian model averaging which are state of the art approaches for precipitation post-processing. The incorporation of neighbourhood information further improves predictive performance and turns out to be a useful strategy to account for displacement errors of the dynamical forecasts in a probabilistic forecasting framework.