Revisiting the synoptic-scale predictability of severe European winter storms using ECMWF ensemble reforecasts

Abstract

Abstract. New insights into the synoptic-scale predictability of 25 severe European winter storms of the 1995-2015 period are obtained using the homogeneous ensemble reforecast dataset from the European Centre for Medium-Range Weather Forecasts. The predictability of the storms is assessed with different metrics including (a) the track and intensity to investigate the storms' dynamics and (b) the Storm Severity Index to estimate the impact of the associated wind gusts. The storms are well predicted by the whole ensemble up to 2-4 days ahead. At longer lead times, the number of members predicting the observed storms decreases and the ensemble average is not clearly defined for the track and intensity. The Extreme Forecast Index and Shift of Tails are therefore computed from the deviation of the ensemble from the model climate. Based on these indices, the model has some skill in forecasting the area covered by extreme wind gusts up to 10 days, which indicates a clear potential for early warnings. However, large variability is found between the individual storms. The poor predictability of outliers appears related to their physical characteristics such as explosive intensification or small size. Longer datasets with more cases would be needed to further substantiate these points.

Overview and first results of the Wind and Storms Experiment (WASTEX): a field campaign to observe the formation of gusts using a Doppler lidar

“…2). This tendency of extratropical cyclones to cluster is due to favourable conditions in the large-scale dynamics (Pinto et al, 2014). It facilitates the planning of observations, as the track and intensity of storms can be predicted 2-4 days in advance only (Pantillon et al, 2017).…”

Brief Communication: Key papers of 20 years in Natural Hazards and Earth System Sciences

“…The priority was further considered by the following Sendai Framework for Disaster Risk Reduction (UNISDR, 2015). Using the definition by the United Nations Development Programme in 2004, the Disaster Risk Index (DRI) (Peduzzi et al, 2009) was the first attempt to produce a global, quantitative approach to assessing risk due to multiple hazards. By exploring the relationship between human losses and socio-economic and environmental variables for a variety of hazards (i.e., cyclones, droughts, earthquakes and floods), Peduzzi et al (2009) provided the first statistical evidence of the links between vulnerability to natural hazards and levels of development at the global scale (i.e.…”

“…The method has been applied to Reanalysis datasets and both global and regional climate model data, permitting the quantification of the windstorm risk in Europe and elsewhere for recent and future decades (e.g., Pinto et al (2012); Leckebusch et al (2008)). Recently, Pantillon et al (2017) provided evidence that the impact of European windstorms is predictable with a certain level of confidence with a lead time of 2-4 days using 20 years of European Center for Medium-Range Forecasts (ECMWF) ensemble forecast data. This demonstrates the ability to assess storm damage, issue extreme weather warnings in a timely manner, and respond appropriately to avoid major damage and disruption.…”

“…For example, the selected article on 'Assessment of economic flood damage' (Merz et al, 2010) is one of the highly cited interdisciplinary articles (637 citations, based on Scopus search dated October 5, 2021), which covers multiple topics such as hydrological hazards, meteorological hazards, and risk assessment, mitigation and adaptation strategies, socioeconomic and management aspects. The development of multi-hazards disaster risk index by Peduzzi et al (2009), a paper with more than 278 citations, was one of the initial contributions on was one of the initial contributions on quantitative assessments of risks globally, within the topic of risk assessment, mitigation and adaptation strategies, socioeconomic and management aspects.…”

Towards operational impact forecasting of building damage from winter windstorms in Switzerland

“…According to Merz et al (2020), there are storm impact forecasting systems for insurance losses in the private domain, for example, proprietary models run by insurance companies (Pinto et al, 2019). In the public domain, there have been studies that show the skill of impact forecasting for storm impacts on a theoretical level (Pantillon et al, 2017; Pardowitz et al, 2016), but they do not focus on the communication of these forecasted impacts as warnings to specific users or the general public.…”