Censored and shifted gamma distribution based EMOS model for probabilistic quantitative precipitation forecasting

Recently, all major weather prediction centers provide forecast ensembles of different weather quantities, which are obtained from multiple runs of numerical weather prediction models with various initial conditions and model parametrizations. However, ensemble forecasts often show an underdispersive character and may also be biased, so that some post-processing is needed to account for these deficiencies. Probably the most popular modern post-processing techniques are the ensemble model output statistics (EMOS) and the Bayesian model averaging (BMA), which provide estimates of the density of the predictable weather quantity. In the present work, an EMOS method for calibrating ensemble forecasts of precipitation accumulation is proposed, where the predictive distribution follows a censored and shifted gamma (CSG) law with parameters depending on the ensemble members. The CSG EMOS model is tested on ensemble forecasts of 24-h precipitation accumulation of the eight-member University of Washington mesoscale ensemble and on the 11-member ensemble produced by the operational Limited Area Model Ensemble Prediction System of the Hungarian Meteorological Service. The predictive performance of the new EMOS approach is compared with the fit of the raw ensemble, the generalized extreme value (GEV) distribution-based EMOS model, and the gamma BMA method. According to the results, the proposed CSG EMOS model slightly outperforms the GEV EMOS approach in terms of calibration of probabilistic and accuracy of point forecasts and shows significantly better predictive skill than the raw ensemble and the BMA model. Copyright (c) 2016 John Wiley & Sons, Ltd.