An analysis of uncertainties and skill in forecasts of winter storm losses

Abstract

Abstract. 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.

Objective identification of potentially damaging tropical cyclones over the Western North Pacific

“…This is done by adapting WiTRACK-a scheme proven to be successful for objectively identifying damaging windstorms associated with extra-tropical cyclones (e.g. Leckebusch et al 2008, Nissen et al 2010, Pardowitz et al 2016-for applicability to damaging TCs. The basic approach of this detection method is based on the assumption that infrastructure and building standards are usually adapted to the local wind climatology and damages typically occur for wind speeds beyond a threshold associated with a certain return period, that is a specific quantile of the local wind climatology.…”

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

“…This suggests a potential for warnings but with possible false alarms, as already noted by Lalaurette (2003). The use of EFI and SOT thus requires an appropriate balance between hit and false alarm rates (Petroliagis and Pinson, 2014;Boisserie et al, 2016).…”

“…As noted by Lalaurette (2003), the EFI gives useful warnings of extreme events but also frequent false alarms. Petroliagis and Pinson (2014) therefore suggested the use of an optimal threshold to balance between hit rate (H ) and false alarm rate (F ), a lower or higher thresh- old increasing or decreasing both H and F . Boisserie et al (2016) further suggested to maximise the Heidke skill score (HSS; Heidke, 1926) to define the optimal threshold.…”

“…Lalaurette (2003) further showed that the extremeness of the ensemble as a whole, measured by its deviation from the model climate, allows identifying areas of unusually strong winds up to 120 h lead time in the case of Lothar, although failing in the case of Martin. Petroliagis and Pinson (2014) and Boisserie et al (2016) recently extended this method to longer periods with, respectively, operational and retrospective forecasts. While they found contrasting results from case to case, the authors confirmed the potential of ensemble forecasts for the early warning of severe European storms.…”

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

“…Regional models are required to represent mesoscale features such as sting jets or convection embedded in the cold front, while the accurate representation of wind gusts stays beyond the resolution of operational models and relies on large-eddy simulations or observations at the turbulent scale. Alternatively, dynamical and statistical downscaling can be combined to obtain skillful forecasts at the local level, as demonstrated by Pardowitz et al (2016) for storm losses, who further took both meteorological and damage model uncertainties into account. These different approaches shall be considered to allow advances in the predictability of severe European winter storms.…”