Getting the Most From Your Extreme Wind Data: A Step by Step Guide

Historical Winter Storm Atlas for Germany (GeWiSA)

Long-term gust speed (GS) measurements were used to develop a winter storm atlas of the 98 most severe winter storms in Germany in the period 1981–2018 (GeWiSa). The 25 m × 25 m storm-related GS fields were reconstructed in a two-step procedure: Firstly, the median gust speed ( G S ˜ ) of all winter storms was modeled by a least-squares boosting (LSBoost) approach. Orographic features and surface roughness were used as predictor variables. Secondly, the quotient of GS related to each winter storm to G S ˜ , which was defined as storm field factor (STF), was calculated and mapped by a thin plate spline interpolation (TPS). It was found that the mean study area-wide GS associated with the 2007 storm Kyrill is highest (29.7 m/s). In Southern Germany, the 1999 storm Lothar, with STF being up to 2.2, was the most extreme winter storm in terms of STF and GS. The results demonstrate that the variability of STF has a considerable impact on the simulated GS fields. Event-related model validation yielded a coefficient of determination (R2) of 0.786 for the test dataset. The developed GS fields can be used as input to storm damage models representing storm hazard. With the knowledge of the storm hazard, factors describing the vulnerability of storm exposed objects and structures can be better estimated, resulting in improved risk management.

Indicative hazard profile for strong winds in South Africa

Abstract While various extreme wind studies have been undertaken for South Africa for the purpose of, amongst others, developing strong wind statistics, disaster models for the built environment and estimations of tornado risk, a general analysis of the strong wind hazard in South Africa according to the requirements of the National Disaster Management Centre is needed. The purpose of the research was to develop a national profile of the wind hazard in the country for eventual input into a national indicative risk and vulnerability profile. An analysis was undertaken with data from the South African Weather Service’s long-term weather stations to quantify the wind hazard on a municipal scale, taking into account that there are more than 220 municipalities in South Africa. South Africa is influenced by various strong wind mechanisms occurring at various spatial and temporal scales. This influence is reflected in the results of the analyses which indicated that the wind hazard across South Africa is highly variable, spatially and seasonally. A general result was that the strong wind hazard is highest from the southwestern Cape towards the central and eastern parts of the Northern Cape Province, and the southeastern parts of the coast as well as the eastern interior of the Eastern Cape Province. On a seasonal basis, the southern parts of the country showed similar magnitudes of relative wind hazard throughout the year. However, further north, a strong seasonal component was evident, with lowest risk of strong winds during autumn and winter, and highest risk in spring and summer when convective activity is strongest.