Conditional Ensemble Model Output Statistics for Postprocessing of Ensemble Precipitation Forecasting

Forecasts produced by EPSs provide the potential state of the future atmosphere and quantify uncertainty. However, the raw ensemble forecasts from a single EPS are typically characterized by underdispersive predictions, especially for precipitation that follows a right-skewed gamma distribution. In this study, censored and shifted gamma distribution ensemble model output statistics (CSG-EMOS) is performed as one of the state-of-the-art methods for probabilistic precipitation postprocessing across China. Ensemble forecasts from multiple EPSs, including the European Centre for Medium-Range Weather Forecasts, the National Centers for Environmental Prediction, and the Met Office, are collected as raw ensembles. A conditional CSG EMOS (Cond-CSG-EMOS) model is further proposed to calibrate the ensemble forecasts for heavy-precipitation events, where the standard CSG-EMOS is insufficient. The precipitation samples from the training period are divided into two categories, light- and heavy-precipitation events, according to a given precipitation threshold and prior ensemble forecast. Then individual models are, respectively, optimized for adequate parameter estimation. The results demonstrate that the Cond-CSG-EMOS is superior to the raw EPSs and the standard CSG-EMOS, especially for the calibration of heavy-precipitation events. The spatial distribution of forecast skills shows that the Cond-CSG-EMOS outperforms the others over most of the study region, particularly in North and Central China. A sensitivity testing on the precipitation threshold shows that a higher threshold leads to better outcomes for the regions that have more heavy-precipitation events, i.e., South China. Our results indicate that the proposed Cond-CSG-EMOS model is a promising approach for the statistical postprocessing of ensemble precipitation forecasts. Heavy-precipitation events are of highly socioeconomic relevance. But it remains a great challenge to obtain high-quality probabilistic quantitative precipitation forecasting (PQPF) from the operational ensemble prediction systems (EPSs). Statistical postprocessing is commonly used to calibrate the systematic errors of the raw EPSs forecasts. However, the non-Gaussian nature of precipitation and the imbalance between the size of light- and heavy-precipitation samples add to the challenge. This study proposes a conditional postprocessing method to improve PQPF of heavy precipitation by performing calibration separately for light and heavy precipitation, in contrast to some previous studies. Our results indicate that the conditional model mitigates the underestimation of heavy precipitation, as well as with a better calibration for the light- and moderate-precipitation.