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Susanne Drechsel and Georg J. Mayr

Abstract

Foehn winds often depend on topographical features of a scale that is not sufficiently resolved in numerical models. Consequently, a successful foehn forecast has crucially depended on the experience of bench forecasters. This study provides a method for an objective, probabilistic forecast of foehn occurrence and strength, based on an operational global model (ECMWF). Because model topography differs from real topography, forecasted wind is not a reliable indicator of a foehn. Instead, using the larger-scale fingerprint of foehn from cross-barrier pressure differences and the descent of isentropes is more successful. These foehn predictors were tested over a period of 3 yr for the subgrid-scale Wipp Valley in the central Alps, which is instrumented sufficiently for objectively diagnosing the occurrence and strength of a foehn. The joint probability from pressure differences and isentropic descent is better at diagnosing a foehn from model analyses than from the distributions of the individual parameters. The larger the pressure difference and the isentropic descent, the higher the foehn probability. As wind speed and pressure gradient are directly connected by the Bernoulli equation, the cross-barrier pressure difference in the model proved to be a suitable predictor for the strength of the foehn. Despite being a small-scale weather phenomenon, the skill of a objective foehn forecast out to 3 days degrades little compared to the analysis. Afterward, the predictability decreases progressively.

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Markus Dabernig, Georg J. Mayr, and Jakob W. Messner

Abstract

Energy traders and decision-makers need accurate wind power forecasts. For this purpose, numerical weather predictions (NWPs) are often statistically postprocessed to correct systematic errors. This requires a dataset of past forecasts and observations that is often limited by frequent NWP model enhancements that change the statistical model properties. Reforecasts that recompute past forecasts with a recent model provide considerably longer datasets but usually have weaker setups than operational models. This study tests the reforecasts from the National Oceanic and Atmospheric Administration (NOAA) and the European Centre for Medium-Range Weather Forecasts (ECMWF) for wind power predictions. The NOAA reforecast clearly performs worse than the ECMWF reforecast, the operational ECMWF deterministic and ensemble forecasts, and a limited-area model of the Austrian weather service [Zentralanstalt für Meteorologie und Geodynamik (ZAMG)]. On the contrary, the ECMWF reforecast has, of all tested models, the smallest squared errors and one of the highest financial values in an energy market.

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