ALBERT

Description: The properties of European wind storms under present climate conditions are estimated on the basis of surface wind forecasts from the European Center of Medium-Range Weather Forecast (ECMWF) Ensemble Prediction System (EPS). While the EPS is designed to provide forecast information of the range of possible weather developments starting from the observed state of weather, we use its archive in a climatological context. It provides a large number of modifications of observed storm events, and includes storms that did not occur in reality. Thus it is possible to create a large sample of storm events, which entirely originate from a physically consistent model, whose ensemble spread represents feasible alternative storm realizations of the covered period. This paper shows that the huge amount of identifiable events in the EPS is applicable to reduce uncertainties in a wide range of fields of research focusing on winter storms. Wind storms are identified and tracked in this study over their lifetime using an algorithm, based on the local exceedance of the 98th percentile of instantaneous 10 m wind speed, calculating a storm severity measure. After removing inhomogeneities in the dataset arising from major modifications of the operational system, the distributions of storm severity, storm size and storm duration are computed. The overall principal properties of the homogenized EPS storm data set are in good agreement with storms from the ERA-Interim dataset, making it suitable for climatological investigations of these extreme events. A demonstrated benefit in the climatological context by the EPS is presented. It gives a clear evidence of a linear increase of maximum storm intensity and wind field size with storm duration. This relation is not recognizable from a sparse ERA-Interim sample for long lasting events, as the number of events in the reanalysis is not sufficient to represent these characteristics.

Description: This paper describes an approach to derive probabilistic predictions of local winter storm damage occurrences from a global medium-range ensemble prediction system (EPS). Predictions of storm damage occurrences are subject to large uncertainty due to meteorological forecast uncertainty (typically addressed by means of ensemble predictions) and uncertainties in modelling weather impacts. The latter uncertainty arises from the fact that local vulnerabilities are not known in sufficient detail to allow for a deterministic prediction of damages, even if the forecasted gust wind speed contains no uncertainty. Thus, to estimate the damage model uncertainty, a statistical model based on logistic regression analysis is employed, relating meteorological analyses to historical damage records. A quantification of the two individual contributions (meteorological and damage model uncertainty) to the total forecast uncertainty is achieved by neglecting individual uncertainty sources and analysing resulting predictions. Results show an increase in forecast skill measured by means of a reduced Brier score if both meteorological and damage model uncertainties are taken into account. It is demonstrated that skilful predictions on district level (dividing the area of Germany into 439 administrative districts) are possible on lead times of several days. Skill is increased through the application of a proper ensemble calibration method, extending the range of lead times for which skilful damage predictions can be made.