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Historical Winter Storm Atlas for Germany (Gewisa)

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Historical Winter Storm Atlas for Germany (Gewisa) atmosphere Article Historical Winter Storm Atlas for Germany (GeWiSA) Christopher Jung * and Dirk Schindler Environmental Meteorology, Albert-Ludwigs-University of Freiburg, Werthmannstrasse 10, D-79085 Freiburg, Germany * Correspondence: [email protected]; Tel.: +49-761-203-6822 Received: 31 May 2019; Accepted: 8 July 2019; Published: 11 July 2019 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 (GSf) 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 GSf, 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. Keywords: gust speed; roughness length; European Settlement Map; storm damage; digital elevation model 1. Introduction Strong storms chronically lead to enormous socio-economic damage [1]. In the period 1981–2018, storm events around the world caused total losses of about US$ 2115bn and led to approximately 446,000 fatalities [1]. The spatiotemporal extent of storm events greatly varies depending on the geographical location and the time during the year [2]. In Central Europe, storm events can roughly be classified into two categories: small-scale thunderstorms, which mainly occur from May to September [3–6], and large-scale winter storms mainly occurring from October to March, which are related to intense low-pressure systems [2,7–10]. As a part of Central Europe, Germany was often hit by severe winter storms, causing total losses of about US$ 37bn and 300 fatalities since 1981 [1]. The most destructive feature of winter storms are high-impact gusts, which are short-time fluctuations of the horizontal wind vector [11,12]. High gust speed (GS) seriously affects numerous sectors including forestry [13–15], insurance [16], local authorities [17], wind energy [2], waterways transport [18] and air traffic [19]. In these sectors, there is great interest in spatially explicit modeled GS fields for improving the identification of storm damage risk factors [15]. Among the approaches used to model storm characteristics including GS, mechanistic models [7,8] can be differentiated from statistical (empirical) models [20–23]. Mechanistic models are useful tools for characterizing and investigating physical processes that determine storm formation, storm life cycle and storm-related GS dynamics. However, one of the major challenges in the application of mechanistic models is the knowledge of and the control over the large number of input parameters and the rather extensive initialization as well as parameterization for particular datasets. Atmosphere 2019, 10, 387; doi:10.3390/atmos10070387 www.mdpi.com/journal/atmosphere Atmosphere 2019, 10, 387 2 of 17 The second, widely used approach is statistical (empirical) modeling, which is based on measured GS values. Although statistical approaches provide only general insights into the physical mechanisms of GS dynamics, they can be applied to assess GS field dynamics associated with winter storms. However, due to measurement errors, missing data and low temporal resolution, the quality of many GS time series is poor [24]. Comprehensive preparation is usually a basic prerequisite for the scientific analysis and interpretation of GS data. This mostly includes breakpoint analysis, measurement height correction and gap filling [25]. Moreover, long-term GS measurements are rare [26]. The small number of high-quality GS time series is a serious issue, since GS is one of the fastest varying atmospheric variables [27]. Complex land cover pattern and orographic obstacles at and around GS measuring sites further reduce the spatial representativeness of the few available long-term GS measurements [28,29]. To improve the spatial representativeness, statistical approaches making use of relationships between surface properties and GS were applied to model GS on high spatial resolution grids. For instance, the 98th percentiles of daily maximum GS time series were modeled for Switzerland on a 50 m 50 m × resolution grid [20]. Return periods of extreme GS were mapped in Germany on a 1000 m 1000 m × resolution grid [21]. In another study, 69 GS time series were used to model GS distributions on a 50 m 50 m resolution grid in Southwestern Germany [22]. Using terrain and roughness-related × information as predictor variables (PV), storm event-related GS was modeled on a 50 m 50 m grid in × Southwest Germany [23]. The above-mentioned studies investigated either the statistical properties of GS distributions or individual storm events. Since all storms have a unique track, the results of these studies are either not related to a particular storm event or individual showcases. To combine both approaches, it is necessary to consider the tracks of many storms in the statistical modeling of GS. This allows improved statements to be made about the spatiotemporal GS variability and the associated storm damage. The combined analysis of many storm events allows not only statements about central tendencies of GS during storms, but also about the deviation of individual storm events from the central tendencies. Considering these aspects, the goals of this study are (1) reconstructing the storm fields associated with the most destructive winter storms in Germany in the period 1981–2018 and (2) high-spatial resolution modeling of GS associated with these storms. The mapping of the GS fields yields the winter storm atlas for Germany (GeWiSA). 2. Material and Methods 2.1. Overview The development of GeWiSA comprises the following main steps (Figure1): (1) obtaining a GS time series of 307 measurement stations operated by the German Meteorological Service (DWD) in the period 1981–2018, (2) breakpoint analysis and correction of GS time series, (3) extraction of GS associated with the 98 most destructive winter storms, (4) calculation of median GS (GSf), (5) calculation of the storm field factor (STF), (6) estimation of roughness length (z0), (7) assessment of relative elevation (η) and orographic sheltering (σ), (8) modeling of GS based on a LSBoost approach and PV, (9) thin plate spline interpolation (TPS) of STF, (10) multiplication of GSf by STF yielding GS. Atmosphere 20192019,, 10,, 387x FOR PEER REVIEW 3 of 1716 86 FigureFigure 1. Overview 1. Overview of the of methodology the methodology applied applied to develop to develop Germany’s Germany’s winter winter storm storm atlas (GeWiSA),atlas 87 with(GeWiSA) STF being, with the STF storm being field the factor,storm GSfieldis factor, the gust 퐺푆 speed is the and gustGSf speedis the and median 퐺푆̃ is of theGS .median of 퐺푆. 2.2. Study Area and Evaluated Winter Storms 88 2.2. Study Area and Evaluated Winter Storms Germany has a size of about 357,000 km2. The German landscape consists of four large natural 89 Germany has a size of about 357,000 km². The German landscape consists of four large natural areas: the North German Plain, the Central German Plain, the Alpine Foothills and the Alps in Southern 90 areas: the North German Plain, the Central German Plain, the Alpine Foothills and the Alps in Germany [30]. Germany’s surface is covered by agricultural areas (59%), forests (30%) and artificial 91 Southern Germany [30]. Germany’s surface is covered by agricultural areas (59%), forests (30%) and surfaces such as urban areas, airports and road and rail networks (8%) [30,31]. 92 artificial surfaces such as urban areas, airports and road and rail networks (8%) [30,31]. In total, 98 severe winter storms were included in GeWiSA (Table1). The storms were selected 93 In total, 98 severe winter storms were included in GeWiSA (Table 1). The storms were selected based on the overall losses (inflation-adjusted 2018 $) from Munich Re’s NatCatSERVICE [1]. The first 94 based on the overall losses (inflation-adjusted 2018 $) from Munich Re’s NatCatSERVICE [1]. The winter storm contained in Munich Re’s NatCatSERVICE occurred in 1981. A maximum number of five 95 first winter storm contained in Munich Re’s NatCatSERVICE occurred in 1981. A maximum number severe winter storms per year was selected with overall losses being at least US$ 3.0m. According to 96 of five severe winter storms per year was selected with overall losses being at least US$ 3.0m. the overall losses, the most severe storms were Kyrill (US$ 5100 m), Lothar (US$ 2200m) and Friederike 97 According to the overall losses, the most severe storms were Kyrill (US$ 5,100m), Lothar (US$ 2,200m) (US$ 1900 m) [1]. A year with several severe winter storms was 1990. In this year, storms Daria, Vivian 98 and Friederike (US$ 1,900m) [1]. A year with several severe winter storms was 1990. In this year, and Wiebke occurred, causing US$ 1800 m each. 99 storms Daria, Vivian and Wiebke occurred, causing US$ 1,800m each.
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