Abstract
AbstractShelter temperature and wind forecasts from numerical weather prediction models are subject to large systematic errors. Kalman filtering and model output statistics (MOS) are commonly used postprocessing methods, but how effective are they in comparison with steadily increasing resolution of the forecast model? Observations from over 1100 stations in central Europe are used to compare the different postprocessing methods and the influence of model resolution in complex and simple terrain, respectively. A 1-yr period with hourly, or at least 3-hourly, data is used to achieve statistically meaningful results. Furthermore, the importance of real-time observations as MOS predictors and the effects of daily training of the MOS equations are studied.
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