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Storm surges in the German Bight can have great destructive potential. This includes devastating floods, structural damage to infrastructure, and even loss of life. The most important driver of storm surge events in the German Bight is strong winds from north-westerly directions, often related to intense extra-tropical cyclones travelling from the North Atlantic into the North Sea region.Making use of an objective, impact-oriented identification and tracking scheme, we analyse storm events related to storm surges in the German Bight. This particular version of the tracking algorithm includes the so-called Storm Surge Severity Index (SSSI) and is used as a complementary tool in operational forecasting by the German Federal Maritime and Hydrographic Agency (BSH). The SSSI takes wind speed and direction into account and intends to quantify storm surge risk in the German Bight. However, to date, the SSSI has never been systematically evaluated for past storm surge events. To fill this gap and to prove that the SSSI can be used as a proxy for storm surge risk, we analyse the relationship between SSSI values of past storm events and the associated water levels recorded in the German Bight using ERA5 atmospheric reanalysis data. Moreover, we analyse potentially storm surge-relevant storms in a multi-model ensemble of global climate model simulations to assess potential future changes in storm surge risk in the German Bight.

High wind speeds are a major cause for tree fall. However, the impact of wind on trees can depend on other co-occurring or antecedent meteorological conditions. Tree fall can also have an impact on surrounding infrastructure like the railway system. Here, it may damage railway infrastructure and lead to train disruptions. The intensity and frequency of windstorms and other meteorological factors is expected to change in the course of a changing climate. Thus, their impacts on trees and tree damage might change as well. To understand these changes an examination of impact-relevant weather factors and sequences is needed.We obtained a dataset for tree and branch fall events alongside German railway lines from the Deutsche Bahn for the years 2017 to 2021. We use logistic regression to model tree fall probabilities and to identify relevant current and antecedent weather factors, their combinations and their impact on tree fall risk during winter. We use meteorological predictors derived from the ERA5 reanalysis and RADOLAN radar data.High wind speed is identified as the strongest risk increasing factor. However, high daily precipitation and high soil water volume during the tree fall event as well as an antecedent year with high precipitation also increase tree fall risk. A small decreasing effect was found for warm and wet soil conditions in the three preceding months. We found no or only minor effects for daily mean air temperature, storm duration and wind direction.In further steps these factors and their combinations will be assessed in terms of their effect on occurrence probabilities of tree fall events under recent and future climate conditions based on regional climate simulations.

Weather conditions can affect the probability of road accidents. However, the effect can be different, depending on the vehicle type, the type of collision or road characteristics at the location of the accident. The aim of this study is to quantify the combined effects of traffic volume and different meteorological parameters on hourly probabilities of different accident types using generalized additive models. These models are based on police reports, which are available at the level of administrative districts, as well as reanalysis data and radar-based precipitation. Using tensor product bases, we model non-linear relationships and combined effects of different meteorological parameters. We evaluate the increase in relative risk of different accident types in case of precipitation, sun glare and high wind speeds.The largest effect of snow is found in case of single-truck accidents, while rain has a larger effect on single-car accidents. Precipitation particularly increases the relative risk of run-off-road accidents, as well as accidents at curves and descending road sections. A increasing effect of sun glare on accident probability was found in case of multi-car accidents, in particular in case of rear-end crashes. High wind speeds increase the risk of single-truck accidents and, for all vehicle types, the risk of collisions with objects blown on the road. A comparison of the predictive skill of models with and without meteorological variables shows an improvement of scores of up to 24%. This makes the models suitable for applications in real-time traffic management or impact-based warning systems and have the potential to improve risk perception and behavior of warning recipients.

Wind and windstorms cause severe damage to natural and human-made environments. Thus, wind-related risk assessment is vital for the preparation and mitigation of calamities. However, the cascade of events leading to damage depends on many factors that are environment-specific and the available methods to address wind-related damage often require sophisticated analysis and specialization. Fortunately, simple indices and thresholds are as effective as complex mechanistic models for many applications. Nonetheless, the multitude of indices and thresholds available requires a careful selection process according to the target environment. Here, we first provide a basic background on wind and storm formation and characteristics, followed by a comprehensive collection of both indices and thresholds that can be used to predict the occurrence and magnitude of wind and storm damage. We focused on five key environments: forests, urban, transport, agriculture, and wind-based energy production. For each environment we described indices and thresholds relating to physical properties such as topography and land cover but also to economic aspects (e.g. disruptions in transportation or energy production). In the face of increased climatic variability, the promotion of more effective analysis of wind and storm damage could reduce the impact on society and the environment.

Severe winter storms are one of the most damaging natural hazards for European residential buildings. Previous studies mainly focused on the loss ratio (loss value/total insured sum) as a monetary value for damages (e.g. Prahl et al. 2012; Pardowitz et al. 2016). In this study the focus is on the claim ratio (number of insured claims/number of contracts), which is derived from a storm loss dataset provided by the German Insurance Association. In a first step, loss ratios and claim ratios in German administrative districts are compared to investigate differences and similarities between the two variables. While there is no significant change in the ratio between claim ratio and loss ratio with increasing wind speeds, a tendency for lower loss ratios in urban areas can be confirmed. In a second step, a generalized linear model for daily claim ratios is developed using daily maximum wind gust (ERA5) and different non-meteorological indicators for vulnerability and exposure as predictor variables. The non-meteorological predictors are derived from the Census 2011. They include information about the district-average construction years, the number of apartments per buildings and others to get a better understanding of these factors concerning the number of buildings affected by windstorms. The modeling procedure is divided into two steps. First, a logistic regression model is used to model the probabilty of storm damage occurence. Second, generalized linear models with different link functions are compared regarding their ability to predict claim ratios in case a storm damage occured. In a cross-validation setting a criteria for model selection is implemented and the models of both steps are verified. Both steps show an improvement over the climatological forecast.

Das Wetter kann einen erheblichen Einfluss auf das Reiseverhalten der Menschen haben. Um diesen Zusammenhang zu quantifizieren und nutzbar zu machen, wird untersucht, ob meteorologische Variablen die Vorhersage des Verkehrsaufkommens in Deutschland verbessern können. Dazu werden stündlichen Verkehrszählungen an 1.400 Stationen an Bundesstraßen und Autobahnen verwendet, wobei zwischen Motorrädern, Pkw, Transportern und Lkw unterschieden wird. Es wird untersucht, inwieweit sich der mittlere quadratische Fehler von Poisson-Regressionsmodellen für stündliche Verkehrszählungen durch die Berücksichtigung von Niederschlag, Temperatur, Bewölkung und Windgeschwindigkeit verringert. In einem iterativen Verfahren werden den Modellen Prädiktorvariablen hinzugefügt, wenn diese den mittleren quadratischen Fehler reduzieren. Wir zeigen, dass insbesondere Motorradzahlen stark wetterabhängig sind. Auf Bundesstraßen reduziert sich der mittlere quadratische Fehler von Modellen für stündliche Motorradzahlen um bis zu 60%, wenn meteorologische Prädiktorvariablen zum Modell hinzugefügt werden. Eine detaillierte Analyse des Modells für Motorradzahlen zeigt nicht-lineare Beziehungen zwischen den meteorologischen Variablen und den Motorradzählungen. Für Pkw zeigt sich eine Wetterabhängigkeit insbesondere in touristischen Regionen wie Badeorten und Naturparks. Für Lieferwagen und Lkw zeigt sich nur eine geringe Wetterabhängigkeit. Die Ergebnisse eröffnen Möglichkeiten für unterschiedliche potenzielle Anwendungen, wie z. B. die Verbesserung der Routenplanung in Navigationssystemen, die Implementierung in Verkehrsmanagementsystemen, die Vorausplanung von Besucherzahlen in touristischen Gebieten oder zur Abschätzung der Exposition in Verkehrsunfallstudien.

Severe winter storms are one of the most damaging natural hazards for European residential buildings. Previous studies mainly focused on the loss ratio (loss value / total insured sum) as a monetary value for damages. In this study the focus is on the claim ratio (number of claims / number of contracts), which is derived from a storm loss dataset provided by the German Insurance Association. Due to its magnitude, the claim ratio might be a more intuitive parameter for the use in impact-based warnings than the loss ratio.In a first step, loss ratios and claim ratios in German administrative districts are compared to investigate differences and similarities between the two variables. While there is no significant change in the ratio between claim ratio and loss ratio with increasing wind speeds, a tendency for lower loss ratios in urban areas can be confirmed.In a second step, a generalized linear model for daily claim ratios is developed using daily maximum wind gust (ERA5) and different non-meteorological indicators for vulnerability and exposure as predictor variables. The non-meteorological predictors are derived from the Census 2011. They include information about the district-average construction years, the number of apartments per buildings and others to get a better understanding of these factors concerning the number of buildings affected by windstorms. The modelling procedure is divided into two steps. First, a logistic regression model is used to model the probabilty claim ratios larger than zero. Second, generalized linear models with different link functions are compared regarding their ability to predict claim ratios larger than zero. In a cross-validation setting a criteria for model selection is implemented and the models of both steps are verified. Both steps show an improvement over the climatological forecast and in both cases the addition of data for vulnerability and exposure leads to in decrease of the mean squared error.

Winter windstorms are among the most dangerous and costly natural hazards in Central Europe. Their ability to cause tree and branch fall leads to disruptions and damages along railway systems. Along the German railway network the Deutsche Bahn is preventively removing about 30.000 trees per year. Still, each year a multiplicity of disturbances occur which may lead to delays, economic damages or even train collisions.A data set with vegetation disturbance events between 2017 and 2020 along the German railway system is provided by the Deutsche Bahn. The aim of this study is to use exploratory statistics as well as machine learning methods like regression techniques or decision trees to explore the relationship between vegetation damages and meteorological parameters like wind gusts, precipitation or temperature. Additionally, tree related factors and surrounding conditions like ground frost and soil moisture will be taken into account. Finally, we want to derive critical thresholds and combinations of weather parameters leading to significant damage risk. We find a positive relationship between vegetation disturbance and wind speeds. Especially strong winter storms leave a very clear signal in the disturbance time series. For example, the highest numbers of vegetation disturbances occurred during the winter storms Sabine (10.02.2020, 515 events) and Friderike (18.01.2018, 360 events). During winter storm days the majority of events occurs in those areas affected by high wind speeds. Tree fall disturbances peak during the winter storm season between January and March, while branch fall disturbances peak between June and August. However, a high number of events occurs also during times of low wind speeds. Additionally, high wind speeds do not necessarily lead to vegetation disturbances. It is clear that other meteorological and tree related factors need to be taken into account. Compound events as well as previous weather and soil conditions are expected to affect wind throw risks.

Weather conditions affect both road traffic volume and the probability of road accidents. The aim of this study is improve the understanding of both effects as well as their interactions. In a first step, we develop generalized linear models for hourly road traffic counts at 1400 traffic stations on German federal roads and highways. It is distinguished between different vehicle types, including motorbikes, cars, delivery vans and trucks. Different meteorological variables are derived from reanalysis and radar data. The impacts of these variables on the predictive skill of the models is analyzed. In particular models for motorbike counts show large improvements, if meteorological predictors are added to the model. At weekends in the afternoon the mean squared errors of modeled motorbike counts are reduced by up to 60%. Temperatures around 25°C, no precipitation, low cloud cover and low wind speeds lead to the highest motorbike counts. In a second step, the information derived from the traffic models is used to improve models for hourly probabilities of road accidents. These models are based on police reports, which are available at the level of administrative districts, and can now explicitly take traffic volume into account. It is shown that in particular winter conditions like precipitation and freezing temperatures lead to a significant increase in accident probability. Especially the probabilities of roadway departures show an increase under such conditions. The models presented in this study are suitable for the integration in risk-based warning systems and have the potential to improve risk perception and behavior of warning recipients.

In Germany about 1000 severe road accidents are recorded by the police per day. On average, 8 % of these accidents are related to weather conditions, for example due to rain, snow or ice. In this study we compare several versions of a logistic regression models to predict hourly probabilities of such accidents in German administrative districts. We use radar, reanalysis and ensemble forecast data from the regional operational model of the German Meteorological Service DWD as well as police reports to train the model with different combinations of input datasets. By including weather information in the models, the percentage of correctly predicted accidents (hit rate) is increased from 30 % to 70 %, while keeping the percentage of wrongly predicted accidents (false-alarm rate) constant at 20 %. Accident probability increases nonlinearly with increasing precipitation. Given an hourly precipitation sum of 1 mm, accident probabilities are approximately 5 times larger at negative temperatures compared to positive temperatures. When using ensemble weather forecasts to predict accident probabilities for a leadtime of up to 21 h ahead, the decline in model performance is negligible. We suggest to provide impact-based warnings for road users, road maintenance, traffic management and rescue forces.

An impact of weather on road accidents has been identified in several studies with a focus mainly on monthly or daily accident counts. We study hourly probabilities of road accidents caused by adverse weather conditions in Germany on the spatial scale of administrative districts. Meteorological predictor variables from radar-based precipitation estimates, high-resolution reanalysis and weather forecasts are used in logistic regression models. Models taking into account temperature and hourly precipitation sums reach the best predictive skill according to different metrics. By introducing meteorological variables, the models hit rate is increased from 0.3 to 0.7, while keeping the false alarm rate constant at 0.2. Accident probability has a non-linear relationship with precipitation. Given an hourly precipitation sum of 1 mm, accident probabilities are about 5 times larger at negative temperatures compared to positive temperatures. Based on ensemble weather forecasts skilful predictions of accident probabilities of up to 21 hours are possible; the loss of skill compared to a model using radar and reanalysis data is negligible. The findings are relevant in the context of impact based warnings for both road users, road maintenance and traffic management authorities, as well as rescue forces.

The sensitivity of the October 1996 medicane in the western Mediterranean basin to sea surface temperatures (SSTs) is investigated via 24-member ensembles of regional climate model simulations. Eleven ensembles are created by uniformly changing SSTs in a range of −4 K to +6 K from the observed field, with a 1 K step. By using a modified phase space diagram and a simple compositing method, it is shown that the SST state has a minor influence on the tracks of the cyclones, but a strong influence on their intensities. Increased SSTs lead to greater probabilities of tropical transitions, to stronger low- and upper-level warm cores, and to lower pressure minima. The tropical transition occurs sooner and lasts longer, which enables a greater number of transitioning cyclones to survive landfall over Sardinia and to re-intensify in the Tyrrhenian Sea. The results demonstrate that SSTs influence the intensity of fluxes from the sea, which leads to greater convective activity before the storms reach their maturity. These results suggest that the processes at steady-state for medicanes are very similar to tropical cyclones.

Paleo-proxy observations have been recently used to constrain the climate models through data assimilation (DA). However, both DA and climate models are computationally very expensive. Moreover, in paleo-DA, the assimilation period is usually too long for a dynamical model to follow the previous analysis state and the chaotic behavior of the model becomes dominant. The majority of the recent paleoclimate studies using DA have performed low or intermediate resolution global simulations along with an off-line DA approach. In an off-line DA, the re-initialisation cycle is completely removed after the assimilation step. In this paper, we design a computationally affordable DA to assimilate yearly pseudo and real observations into an ensemble of COSMO-CLM high resolution regional climate model (RCM) simulations over Europe, where the ensemble members slightly differ in boundary and initial conditions. Within a perfect model experiment, the performance of the applied DA scheme is evaluated with respect to its sensitivity to the noise levels of pseudo-observations. It was observed that the injected bias in the pseudo-observations does linearly impact the DA skill. Such experiments can serve as a tool for selection of proxy records, which can potentially reduce the background error when they are assimilated in the model. Additionally, the sensibility of the COSMO-CLM to the boundary conditions is addressed. The geographical regions, where the model exhibits high internal variability are identified. Two sets of experiments are conducted by averaging the observations over summer and winter. The dependency of the DA skill to different seasons is investigated. Furthermore, the effect of the spurious correlations within the observation space is studied and the optimal correlation length, within which the observations are assumed to be correlated, is detected. Finally, the real yearly-averaged observations are assimilated into the RCM and the performance is evaluated against a gridded observation dataset. We conclude that the DA approach is a promising tool for creating high resolution yearly analysis quantities. The affordable DA method can be applied to efficiently improve the climate field reconstruction efforts by combining high resolution paleo-climate simulations and the available proxy observations.