Historical Winter Storm Atlas for Germany (GeWiSA)

Abstract

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.

Assessment of the Response of a Scots Pine Tree to Effective Wind Loading

“…In the past 70 years, more than 150 large-scale, high-impact winter storms have occurred over the North Atlantic-European region [1][2][3], causing major damage to European forests approximately every two years. Through their large extent and the extreme characteristics of their near-surface wind fields, winter storms currently pose the greatest threat to Europe's forests.…”

About the return period of a catastrophe

“…It is now like a survival function of a random variable (decreasing with the value of functions between 0 and 1), which describes the exceedance probability in contrast to a CDF for non-exceedance probability (Upton and Cook, 2008). Jung and Schindler (2019) have already applied such aggregating functions to German winter storm events and call them explicitly a survival function. However, not every normalized aggregating decreasing function is based on an actual random variable.…”

“…We also extend Schlather's (2002) first theorem with a focus on spatial dependence. The more recent approaches to area functions (Raschke, 2013) and survival functions (Jung and Schindler, 2019) of local event intensities within a region are implemented therein. In Sect.…”

Precipitation Atlas for Germany (GePrA)

“…The orographic features (Table 2) were represented by 22 predictor variables (PV) derived from the digital elevation model EU-DEM v.1 at a 25 m × 25 m spatial resolution [26]. The relative elevation (η) was developed by subtracting the mean elevation of an outer circle of each grid cell from the grid cell-specific ε value [27]. Four η variants with outer-circle radii of 1000 m (η 1000 ), 3000 m (η 3000 ), 5000 m (η 5000 ), and 7500 m (η 7500 ) were built.…”

“…Furthermore, the orographic sheltering (σ) was quantified. It was derived by calculating the angles between grid cell-specific elevation and the visible horizon up to a distance of 1000 m [27]. , were modeled in a two-step procedure: (1) A least-squares boosting (LSBoost) algorithm was trained for RR included in DS1 using PV; and (2) the remaining DS1 residuals were further reduced by applying a thin plate spline interpolation (TPS).…”