Generating and Calibrating Probabilistic Quantitative Precipitation Forecasts from the High-Resolution NWP Model COSMO-DE

Abstract

Abstract Statistical postprocessing is an integral part of an ensemble prediction system. This study compares methods used to derive probabilistic quantitative precipitation forecasts based on the high-resolution version of the German-focused Consortium for Small-Scale Modeling (COSMO-DE) time-lagged ensemble (COSMO-DE-TLE). The investigation covers the period from July 2008 to June 2011 for a region over northern Germany with rain gauge measurements from 445 stations. The investigated methods provide pointwise estimates of the predictive distribution using logistic and quantile regression, and full predictive distributions using parametric mixture models. All mixture models use a point mass at zero to represent the probability of precipitation. The amount of precipitation is modeled by either a gamma, lognormal, or inverse Gaussian distribution. Furthermore, an adaptive tail using a generalized Pareto distribution (GPD) accounts for a better representation of extreme precipitation. The predictive probabilities, quantiles, and distributions are evaluated using the Brier, the quantile verification, and the continuous ranked probability scores. Baseline predictions and covariates are based on first-guess estimates from the COSMO-DE-TLE. Predictive performance is largely improved by statistical postprocessing due to an increase in reliability and resolution. The mixture models show some deficiencies. The inverse Gaussian fails to provide calibrated predictive distributions, whereas the lognormal and gamma mixtures perform well within the bulk of the distribution. Both mixtures provide significantly less skill for the extremal quantiles (0.99–0.999). Their representation is largely improved by incorporating an adaptive GPD tail. Even more stable estimates are obtained if the annual cycle is included in the postprocessing and training is performed on almost 3 yr of data.