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Statistical postprocessing is applied in operational forecasting to correct systematic errors of numerical weather prediction models (NWP) and to automatically produce calibrated local forecasts for end-users. Postprocessing is particularly relevant in complex terrain, where even state-of-the-art high-resolution NWP systems cannot resolve many of the small-scale processes shaping local weather conditions. In addition, statistical postprocessing can also be used to combine forecasts from multiple NWP systems. Here we assess an ensemble model output statistics (EMOS) approach to produce seamless temperature forecasts based on a combination of short-term ensemble forecasts from a convection-permitting limited-area ensemble and a medium-range global ensemble forecasting model. We quantify the benefit of this approach compared to only postprocessing the high-resolution NWP. The multimodel EMOS approach (“mixed EMOS”) is able to improve forecasts by 30% with respect to direct model output from the high-resolution NWP. A detailed evaluation of mixed EMOS reveals that it outperforms either one of the single-model EMOS versions by 8%–12%. Temperature forecasts at valley locations profit in particular from the model combination. All forecast variants perform worst in winter (DJF); however, calibration and model combination improves forecast quality substantially. In addition to increasing skill as compared to single-model postprocessing, it also enables us to seamlessly combine multiple forecast sources with different time horizons (and horizontal resolutions) and thereby consolidates short-term to medium-range forecasting time horizons in one product without any user-relevant discontinuity. [ABSTRACT FROM AUTHOR]

Weather forecasts for arbitrary locations in space are becoming increasingly important. Likewise, the need for an adequate quantification of the forecast uncertainty makes ensemble approaches indispensable. Even though numerical weather prediction models (NWPs) are run at increasingly high resolutions, raw ensemble forecasts still tend to be biased and underdispersed. Hence, statistical postprocessing is expected to improve forecast skill and may help to condense the forecast information. At MeteoSwiss a project on postprocessing of the most relevant weather variables for public weather forecasts, i.e. near surface temperature, precipitation, 10 meters wind speed, and cloud cover, has recently been launched. The project's goal is the introduction of an operational suite that providesproviding spatial fields of postprocessed predictions based on most of the observation data available and on raw ensemble forecasts provided by COSMO-E and the IFS-ENS of ECMWF. While the project does not focus on the development of novel methods, the optimal use of existing statistical postprocessing approaches is at the core of the project. This presentation will first summarize the project and then focus on the example of cloud cover. As for the other variables, we are developing a method routine to obtain postprocessed predictions of cloud cover for arbitrary locations in Switzerland. As ground based cloud cover observations are rather scarce, the training of the statistical model is based on EUMETSAT CM SAF satellite data, which will potentially be augmented by ground based observations. Methodologically, we start from ensemble model output statistics like and models that are suitable for the bounded variable of cloud cover. In a first step spatial consistency is ensured by ensemble copula coupling, but other approaches are to be tested as well. [ABSTRACT FROM AUTHOR]