OSA2.1

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.

How to cite: Becker, N., Rust, H., and Ulbrich, U.: A model for weather-related traffic variations and accident probabilities on roads, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-205, https://doi.org/10.5194/ems2021-205, 2021.

Road weather information is essential for keeping the roads well maintained and safe during wintertime. Main source of road weather observations are road weather stations, but IoT (Internet of things) sensor technology provides new ways to observe road weather. Finnish Meteorological Institute (FMI) and Fintraffic Road are studying whether such IoT technology could help increase spatial density and/or improve coverage in the observation network and whether these additional observations could also be used to improve road weather forecasts. Around 100 autonomous battery-operated low-cost IoT sensors based on LoRaWAN communication technology were installed into the roadside area of a motorway in southern Finland and at the Sodankylä airport test track during winter 2020. Most of the sensors were of the types UC11-T1 from Ursalink and ELT-2 from ELSYS AB, but there were a few MCF-LW12TERWP sensors from MCF88 as well. All sensors measure air temperature and humidity and the MCF sensors also measure air pressure. Some of the sensors were installed at a weather station and some at road weather stations to enable data comparison with reference stations. During wintertime the IoT sensors’ air temperature measurements correspond rather well to the reference measurements. However, during other times of the year the solar radiation often causes warm bias to the measurements. The bias is reduced when the sensors are installed inside radiation shields. However, the reliability of the IoT devices needs improvement, as several sensors stopped working during the measurement campaign. This was probably caused by a firmware bug, that led to excess power consumption and emptying of batteries in some of the devices.

The FMI road weather model uses surface temperature observations in the model initialization to improve the forecasts. As the model surface temperature is forced to the observed surface temperature, the air temperature measurements don’t have that much effect in the initialization. When there are no surface temperature observations available at the forecast location, the model uses values interpolated from road weather station observations. The interpolation is done with the universal kriging method, where elevation is used as an explanatory variable. In this project we studied whether air temperature observations from IoT sensors could be used as explanatory variable as well. The results thus far show that use of air temperature observations from road weather stations improves the interpolated surface temperature values at least in some situations. However, this is rather location dependent. Initial results suggest that IoT observations would be useful this way as well. According to the results, IoT observations show potential to improve road weather monitoring and forecasting, but more studies are still needed.

How to cite: Karsisto, V., Latva, L., Miettinen, J., Hippi, M., Mäenpää, K., Siili, T., Posio, P., and Sukuvaara, T.: Experiences in using low-cost sensors to observe road weather, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-145, https://doi.org/10.5194/ems2021-145, 2021.

Autonomous driving can be challenging especially in winter conditions when road surface is covered by icy and snow or visibility is low due to precipitation, fog or blowing snow. These harsh weather and road conditions set up very important requirements for the guidance systems of autonomous cars. In the normal conditions autonomous cars can drive without limitations but otherwise the speed must be reduced, and the safety distances increased to ensure safety on the roads. 

Autonomous driving needs very precise and real-time weather and road condition information. Data can be collected from different sources, like (road) weather models, fixed road weather station network, weather radars and vehicle sensors (for example Lidars, radars and dashboard cameras). By combining the all relevant weather and road condition information a weather-based autonomous driving mode system is developed to help and guide autonomous driving. The driving mode system is dividing the driving conditions from perfect conditions to very poor conditions. In between there are several steps with slightly alternate driving modes depending for example snow intensity and friction. In the most challenging weather conditions, automatic driving must be stopped because the sensors guiding the driving are disturbed by for example heavy snowfall or icy road.

Finnish Meteorological Institute is testing autonomous driving in the Arctic vehicular test track in Sodankylä, Northern Finland. The test track is equipped with road weather observation system network including road weather stations, IoT sensors measuring air temperature and humidity along with various communication systems. Also, tailored road weather services are produced to the test track, like precise road weather model calculations and very accurate radar precipitation observations and nowcasting. The developed weather-based autonomous driving system is tested on Sodankylä test track among other arctic autonomous driving testing.

This study presents the Sodankylä Arctic vehicular test track environment and weather-based autonomous driving mode system that is developed at the Finnish Meteorological Institute.

How to cite: Hippi, M., Sukuvaara, T., Mäenpää, K., Perälä, T., and Stepanova, D.: Autonomous driving in adverse winter weather conditions, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-310, https://doi.org/10.5194/ems2021-310, 2021.

Road transportation is considered the most vulnerable mode of transport in terms of weather impacts (Molarius et al., 2014). Extreme weather events like winter storms, heavy rain or glazed frost can have a significant impact on road quality and hence, public safety. In addition, seasonal and daily changes in traffic volume or commuter flows influence the occurrence of accidents. German road safety organisations like maintenance services receive weather information through a road condition and weather information system (SWIS) that provides  information like precipitation and road surface temperature. Hence, these information give an overview over the weather conditions on the roads - not what the impacts of those conditions can have on traffic. With predictive modelling, it is possible to assess the impacts of weather on road safety as hourly probabilities of weather-related road accidents (Becker et al. 2020). It is assumed that such information on impacts of weather events on road infrastructure can be of value for road maintenance services that are responsible for ensuring road safety.  However, it is still unclear how such statistical information on accident probability would be used in practice. Early warning systems are encouraged to be ‘people-centred’ (UNISDR 2015), allowing users to act in sufficient time and in an appropriate manner. In order to become people-centred, those implementing a warning system must know who their audience is and understand their information requirements for an optimal response (Zhang et al. 2019). This conference contribution will shed some light on the requirements that practitioners of road safety organisations in Germany have for impact-based forecast on weather-related road accidents. The study is part of an interdisciplinary research project in collaboration with Germany's National Meteorological Service DWD and follows a qualitative social science research approach. With the aim of stakeholder engagement in the process of developing predictive weather-related accident models, focus group discussions with managers of highway and road maintenance services as well as representatives of road and transport authorities were carried out and first results will be presented. Findings contribute to the understanding of professional recipients’ responses to weather warnings and weather information for their daily work tasks and hence, provide a deeper insight into weather service requirements from a set of stakeholders that are instrumental to public safety. Road safety organisations use weather information for the preparation of events, e.g. in planning of personnel and adjusting equipment at hand. In the focus groups, interest in further information for decision-making was expressed, while individual experience of the road’s conditions in practice was also highlighted.

How to cite: Schmidt, J., Tietze, N., and Kox, T.: Requirements for the use of impact-based forecasts by road safety organisations in Germany, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-275, https://doi.org/10.5194/ems2021-275, 2021.

In-flight icing constitutes a major hazard to aviation, and so it is vital to be able to forecast the risk of icing accurately. The Met Office is one of two World Area Forecast Centres (WAFC) to routinely produce and validate icing forecasts. The existing verification methodology of Bowyer and Gill (2018) evaluates each WAFC forecast against satellite-derived icing potential. However, the methodology currently evaluates the full Global forecast data, whereas satellite-derived icing potential is only available during daytime. At night, the presence of cloud is reported, and so only correct rejections and false alarms are possible during nocturnal hours.

We first present an extension to the existing verification methodology by restricting the analysis to only include daytime data and demonstrate that this significantly reduces the degree of over-forecasting previously reported. In addition, we examine the performance of the new WAFC icing severity forecasts and compare against the routine product, during Winter 2020/2021.

There are two major challenges when comparing forecast icing severity and forecast icing potential. The first is that we are comparing the potential for an icing event to occur with its predicted intensity. The second challenge is that the new severity forecasts are on a 0.25º grid, compared with 1.25º for icing potential.

We present results both under the assumption that moderate icing potential is the same as moderate icing severity, and as an independent comparison with a new satellite-derived icing severity product. We also test the sensitivity to the choice of verification grid by re-gridding to both 0.25º and 1.25º.

The results show that WAFC icing severity is over-predicted when compared with satellite-derived severity, especially over the tropics. However, icing events are likely to be too infrequent in the observations, and so the magnitude of the over-prediction is over-estimated. All forecasts show regional skill at predicting icing severity and the results are not sensitive to the choice of verification grid. However, the performance of the higher resolution icing severity forecast is likely to influenced by the double penalty problem.

How to cite: Steele, C., Gill, P., Buchanan, P., Bennett, K., Morcrette, C., Francis, P., and Cheung, J.: Verification of WAFC in-flight icing forecasts using satellite observations, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-113, https://doi.org/10.5194/ems2021-113, 2021.

In the framework of the SINOPTICA project (EU H2020 SESAR, 2020 – 2022), different meteorological forecasting techniques are being tested to better nowcast severe weather events affecting Air Traffic Management (ATM) operations. Short-range severe weather forecasts with very high spatial resolution will be obtained starting from radar images, through an application of nowcasting techniques combined with Numerical Weather Prediction (NWP) model with data assimilation. The final goal is to integrate compact nowcast information into an Arrival Manager to support Air Traffic Controllers (ATCO) when sequencing and guiding approaching aircraft even in adverse weather situations. The guidance-support system will enable the visualization of dynamic weather information on the radar display of the controller, and the 4D-trajectory calculation for diversion coordination around severe weather areas. This meteorological information must be compact and concise to not interfere with other relevant information on the radar display of the controller.

Three severe weather events impacting different Italian airports have been selected for a preliminary radar analysis. Some products are considered for obtaining the best radar approach to characterize the severity of the events for ATM interests. Combining the Vertical Integrated Liquid and the Echo Top Maximum products, hazard thresholds are defined for different domains around the airports. The Weather Research and Forecasting (WRF) model has been used to simulate the formation and development of the aforementioned convective events. In order to produce a more accurate very short-term weather forecast (nowcasting), remote sensing data (e.g. radar, GNSS) and conventional observations are assimilated by using a cycling three-dimensional variational technique. This contribution presents some preliminary results on the progress of the project.

How to cite: Esbri, L., Llasat, M. C., Rigo, T., Milelli, M., Mazzarella, V., Lagasio, M., Parodi, A., Temme, M.-M., Glushenko, O., Kerschbaum, M., Biondi, R., Surian, N., Realini, E., Gatti, A., Tagliaferro, G., and Parodi, A.: Initial results of the project SINOPTICA (Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM), EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-189, https://doi.org/10.5194/ems2021-189, 2021.

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.

How to cite: Lorenz, R., Becker, N., and Ulbrich, U.: Impacts of extreme wind speeds and other factors on vegetation disturbances in the German railway network, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-299, https://doi.org/10.5194/ems2021-299, 2021.

The growing availability of high resolved meteorological measurements coming from automobiles puts forward the possibility of developing real time weather forecast systems, which appears to be an essential key of autonomous driving enhancement. In this frame, the Fleet Weather Maps (Flotten-Wetter-Karte - FloWKar) project, a joint work of the German Meteorological Service (DWD) and the German car manufacturer AUDI AG, aims to explore how environmental data from sensors of vehicles on Germany’s roads, respecting data protection regulations, can be used in real time to improve weather forecast, nowcasting and warnings within DWD’s products. Regarding weather forecasting, an exceptionally fast data assimilation cycle with an update rate of the order of minutes is necessary. However, this cannot be achieved using standard assimilation systems. Hence, an ultra-rapid data assimilation (URDA) method has been developed. The URDA updates only a reduced version of the state variables in an existing model forecast, using different kind of observation data available, only after a standard assimilation cycle and a full model forecast. Moreover, the quality of the meteorological data collected by moving vehicles is vital and therefore a series of quality control and bias correction algorithms has been built for the correction of the raw observations, employing among others artificial intelligence techniques. The first preliminary results of both project partners are promising: the corrected measured variables of the mass-produced vehicle-based sensors match well with the ‘ground truth’ and real time maps are produced after the assimilation of the high resolved project data. The improved and detailed model outputs for road weather forecasting are a first necessary step towards the safety on roads especially in the winter conditions and the future autonomous driving.

How to cite: Paschalidi, Z., Acevedo, W., Hellweg, M., Kratzsch, T., Potthast, R., and Nachtigall, J.: Real time weather for autonomous driving and precise road weather forecasts based on floating car data and seamless integration of Ultra-Rapid Data Assimilation and Nowcasting, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-385, https://doi.org/10.5194/ems2021-385, 2021.

According to the 14^th Annual Road Safety Performance Index Report by the European Transport Safety Council, annually more than 100,000 accidents occur on European roads, of which 22,660 people lost their lives in 2019. The factors contributing to road traffic accidents are commonly grouped into three categories: environment, vehicle or driver. The European accident research and safety report 2013 by Volvo states in about 30% of accidents contributing factors could be attributed to weather and environment leading for example to unexpected changes in road friction, such as black ice. In this work, we are developing a solution to forecast road conditions in Norway by applying the Model of the Environment and Temperature of Roads – METRo, which is a surface energy balance model to predict the road surface temperature. In addition, METRo includes modules for water accumulation at the surface (liquid and frozen) and vertical heat dissipation (Crevier and Delage, 2001). The road condition is forecasted for a given pair of latitude, longitude and desired forecast time. Data from the closest road weather station and postprocessed weather forecast are used to initialize METRo and provide boundary conditions to the road weather forecast. The weather forecasts are obtained from the THREDDS service and the road weather station data from the FROST service, both provided by MET Norway. We develop algorithms to obtain the data from these services, process them to match the METRo model input requirements and send them to METRo’s pre-processing algorithms, which combine observations and forecast data to initialize the model. In a case study, we will compare short-term METRo forecasts with observations obtained by road weather stations and with observations retrieved by car-mounted environmental sensors (e.g., road surface temperature). This work is part of the project AutonoWeather - Enabling autonomous driving in winter conditions through optimized road weather interpretation and forecast financed by the Research Council of Norway in 2020. 

How to cite: Mayer, S., Andrade, F., Lorenz, T., de Lima, L., Hovenburg, A., and Dahlin, C.: Applying the METRo model for road-condition forecasting in Norway  , EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-294, https://doi.org/10.5194/ems2021-294, 2021.