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Traditional ensemble streamflow prediction (ESP) systems are known to provide a valuable baseline to predict streamflows at the subseasonal to seasonal timescale. They exploit a combination of initial conditions and past meteorological observations, and can often provide useful forecasts of the expected streamflow in the upcoming month. In recent years, numerical weather prediction (NWP) models for subseasonal to seasonal timescales have made large progress and can provide added value to such a traditional ESP approach. Before using such meteorological predictions two major problems need to be solved: the correction of biases, and downscaling to increase the spatial resolution. Various methods exist to overcome these problems, but the potential of using NWP information and the relative merit of the different statistical and modelling steps remain open. To address this question, we compare a traditional ESP system with a subseasonal hydrometeorological ensemble prediction system in three alpine catchments with varying hydroclimatic conditions and areas between 80 and 1700 km 2. Uncorrected and corrected (pre-processed) temperature and precipitation reforecasts from the ECMWF subseasonal NWP model are used to run the hydrological simulations and the performance of the resulting streamflow predictions is assessed with commonly used verification scores characterizing different aspects of the forecasts (ensemble mean and spread). Our results indicate that the NWP-based approach can provide superior prediction to the ESP approach, especially at shorter lead times. In snow-dominated catchments the pre-processing of the meteorological input further improves the performance of the predictions. This is most pronounced in late winter and spring when snow melting occurs. Moreover, our results highlight the importance of snow-related processes for subseasonal streamflow predictions in mountainous regions. [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]