CapsuleState-of-the-Art statistical postprocessing techniques for ensemble forecasts are reviewed, together with the challenges posed by a demand for timely, high-resolution and reliable probabilistic information. Possible research avenues are also discussed.

This study applies statistical postprocessing to ensemble forecasts of near‐surface temperature, 24 h precipitation totals, and near‐surface wind speed from the global model of the European Centre for Medium‐Range Weather Forecasts (ECMWF). The main objective is to evaluate the evolution of the difference in skill between the raw ensemble and the postprocessed forecasts. Reliability and sharpness, and hence skill, of the former is expected to improve over time. Thus, the gain by postprocessing is expected to decrease. Based on ECMWF forecasts from January 2002 to March 2014 and corresponding observations from globally distributed stations, we generate postprocessed forecasts by ensemble model output statistics (EMOS) for each station and variable. Given the higher average skill of the postprocessed forecasts, we analyze the evolution of the difference in skill between raw ensemble and EMOS. This skill gap remains almost constant over time indicating that postprocessing will keep adding skill in the foreseeable future.

Hydrologic ensemble forecasts driven by atmospheric ensemble prediction systems need statistical postprocessing in order to account for systematic errors in terms of both location and spread. Runoff is an inherently multivariate process with typical events lasting from hours in case of floods to weeks or even months in case of droughts. This calls for multivariate postprocessing techniques that yield wellcalibrated forecasts in univariate terms and ensure a realistic temporal dependence structure at the same time. To this end, the univariate ensemble model output statistics (EMOS) postprocessing method is combined with two different copula approaches that ensure multivariate calibration throughout the entire forecast horizon. The domain of this study covers three subcatchments of the river Rhine that represent different sizes and hydrological regimes: the Upper Rhine up to the gauge Maxau, the river Moselle up to the gauge Trier, and the river Lahn up to the gauge Kalkofen. In this study, the two approaches to model the temporal dependence structure are ensemble copula coupling (ECC), which preserves the dependence structure of the raw ensemble, and a Gaussian copula approach (GCA), which estimates the temporal correlations from training observations. The results indicate that both methods are suitable for modeling the temporal dependencies of probabilistic hydrologic forecasts.

This work presents a comprehensive intercomparison of different alternatives for the calibration of seasonal forecasts, ranging from simple bias adjustment (BA)-e.g. quantile mapping-to more sophisticated ensemble recalibration (RC) methods-e.g. non-homogeneous Gaussian regression,-which build on the temporal correspondence between the climate model and the corresponding observations to generate reliable predictions. To be as critical as possible, we validate the raw model and the calibrated forecasts in terms of a number of metrics which take into account different aspects of forecast quality (association, accuracy, discrimination and reliability). We focus on one-month lead forecasts of precipitation and temperature from four state-of-the-art seasonal forecasting systems, three of them included in the Copernicus Climate Change Service (C3S) dataset (ECMWF-SEAS5, UK Met Office-GloSea5 and Météo France-System5) for boreal winter and summer over two illustrative regions with different skill characteristics (Europe and Southeast Asia). Our results indicate that both BA and RC methods effectively correct the large raw model biases, which is of paramount importance for users, particularly when directly using the climate model outputs to feed their impact models, or when computing climate indices depending on absolute values/thresholds. However, except for particular regions and/or seasons (typically with high skill), there is only marginal added value beyond this bias removal. For those cases, RC methods can outperform BA ones, mostly due to an improvement in reliability. Finally, we also show that whereas an increase in the number of members only modestly affects the results obtained from calibration, longer hindcast periods lead to improved forecast quality, particularly for RC methods.