Continuous welded rails, which are used as standard on the United Kingdom railway network, are optimised to withstand a specified temperature range centred around a given “stress-free temperature” (SFT). This is the temperature at which the rail is neither in tension nor compression. Higher SFTs mean the track can withstand higher temperatures before expanding. However, too high an SFT makes the rail susceptible to brittleness and cracks in low winter temperatures. Exceeding the temperature range within which the rail is designed to operate can cause it to distort, leading to increased instances of buckling. Although rare, derailment caused by buckling can result in catastrophic consequences. Therefore, to prevent such accidents, blanket speed restrictions are currently imposed when the forecast air temperature exceeds a set threshold.

However, these blanket speed restrictions are based on the simple assumption that the rail surface temperature will be a constant value above the air temperature. This assumption is widely adopted even though observations show that rail surface temperature is not linearly correlated with air temperature. If rail surface temperatures can be accurately and reliably modelled, speeds restrictions and preventative measures can be more targeted. This is becoming increasingly important since climate change is predicted to increase the frequency of occurrence of extreme high temperatures in the United Kingdom.

Therefore, the Met Office is currently developing a new rail surface temperature model, designed to accommodate these future user requirements. This model is centred on the Joint UK Land Environment Simulator (JULES); a community model used as the land-surface component of the Met Office Unified Model (UM), but which can also be used – as we do here – as a stand-alone surface-exchange-scheme driven by forecast output from Numerical Weather Prediction (NWP) models. By adapting JULES to model the energy balance of the rail, we are able to produce forecasts of rail surface temperatures. In particular, by driving JULES with output from the Met Office regional ensemble model MOGREPS-UK, we are able to create a set of possible rail forecast outputs. Considering these in aggregate allow us to produce probabilistic forecasts of rail surface temperatures.

Output from the rail surface temperature model has been compared to observation data collected at 40 locations across Northern Ireland. Initial analysis shows the model significantly outperforms traditional forecasting methods based on linear relationships with air temperature. Additionally, producing probabilistic forecasts allows to quantify uncertainty, supporting users in moving towards probabilistic, risk-based forecasting. This has the potential to significantly improve heat-related hazard forecasting across the UK railway network, thus improving the safety and efficiency of the network. 

How to cite: Lake, A., Eyles, J., Susorney, H., and Skea, A.: Forecasting Heat-Related Hazards on the UK Rail Network, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-18, https://doi.org/10.5194/ems2023-18, 2023.

Since the 1980s the Met Office has produced Surface Transport Forecasts (STF) for the UK. These forecasts allow mitigation of the UK surface transport infrastructure’s vulnerability to weather-based impacts across the autumn and winter season, for example road ice (roads are gritted during ice events), and low rail adhesion (the speed of trains is adapted). Although historically sufficient, these forecasts have limitations, such as not accurately forecasting high summer maximum temperatures, which are becoming more common due to the changing climate. These high temperatures lead to melting road surfaces and buckling railway lines. The current STF system additionally struggles with the future needs of Connected Autonomous Vehicles, for example hazards which specifically impact the on-board sensors such as road spray, and flexible machine-machine communication.

In order to address the limitations of the current STF system, the Met Office is building a new system. This is both a refresh of the pipeline, with a goal to make it flexible, robust, and portable, as well as a revisit of the scientific code within. Updates to the scientific core centre around upgrading the physics model to the Joint UK Land Environment Simulator (JULES). This allows us to accurately capture summer maximum temperatures and carefully model the depth of water on the road (vital for a road spray forecast). Other scientific updates include using Machine Learning based approaches for bias correction and the spin up of new forecast locations (necessary for delivering the service via an API), and building probabilistic ensemble-based forecasts.

The physics model JULES is a community model used as the land-surface component of the Met Office’s Unified Model, but which can also be used – as we do here – as a stand-alone surface-exchange-scheme driven by forecast output from Numerical Weather Prediction models. JULES models a comprehensive list of physical land-surface energy processes, as well as modelling water and snow stores. We have extended JULES to better capture processes specific to a road. Externally to JULES we have implemented a shading scheme and heating due to longwave radiation emitted by traffic.

How to cite: Eyles, J., Lake, A., and Susorney, H.: Mitigating risks to UK surface transport infrastructure through physical modelling, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-93, https://doi.org/10.5194/ems2023-93, 2023.

Ice and snow cause hazardous road conditions every winter, especially in northern countries. Accurate road surface temperature and road condition forecasts are essential for road maintenance personnel to keep the roads safe for driving. With accurate forecasts, the roads can be salted before the freezing occurs and thus the roads can be kept free of ice. Finnish Meteorological Institute has used its road weather model called RoadSurf for over two decades to predict road conditions. RoadSurf is one dimensional model and calculates the heat transfer in the road and the heat balance at the road surface. The model requires atmospheric values, like air temperature, wind speed and radiation as input values and calculates the road surface temperature and amounts of ice, snow, frost, and water on the road. Shadowing caused by the surrounding objects can have a large effect on the road surface temperature. If sky view factor and local horizon angles are known at the to forecast location, the model will use those to modify the radiation input. Soon other institutes and companies can also use RoadSurf, as the main parts of the model will be published as an open-source Fortran library. The library will not include all the model features like friction calculation, but it is programmed so that implementing one’s own features is easy. When the model is implemented to new locations, some model parameters might need calibration to better fit the local road structure. The model is light to run and can be run in parallel mode, so the forecasts can be made easily to thousands of road points. The quality of the forecasts made by RoadSurf has been assessed previously in many verification studies, and new studies will be made to ensure that the new open-source library performs well.

How to cite: Karsisto, V.: RoadSurf – Open-source library for predicting road conditions, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-134, https://doi.org/10.5194/ems2023-134, 2023.

Slippery road and highway conditions can cause accidents if necessary maintenance actions are not undertaken. These include the clearing of snow and prevention of ice by salting. Belgium is characterised by a “marginal” winter environment, where the air temperature commonly fluctuates around the freezing point. A few snow events usually occur during winter, while days with frost occur more often. For these types of conditions, great potential in cost savings for winter maintenance lies in the accurate prediction of ice formation. To aid decision making, road weather information systems have long been used. These consist of road weather stations (RWS) that gather meteorological and road observations, combined with models that forecast the road condition and tools to communicate the relevant information to end users for decision support. For forecasting purposes, the use of dedicated road weather models (RWM’s) has become popular approach. At the Royal Meteorological Institute of Belgium (RMI), a road weather forecasting system was developed in collaboration with the Royal Netherlands Meteorological Institute (KNMI). The KNMI RWM is a physical model, which was previously validated and compared with the Finnish RoadSurf model over the Netherlands. This model was taken as the basis to develop a RWM for Belgian highways in 2018, leading to the deployment of the RMI “GMS” system (“Gladheidmeetsysteem” in Dutch). This system has been operational since the winter of 2018-2019, and is being further improved in close collaboration with the regional road maintenance authorities Belgium. GMS forecasts are updated every hour and communicated to users through a GIS-based interface. Users can consult forecasts of road surface temperature (RST), road surface condition and various other meteorological variables for all RWS locations (about 140), in addition to RWS observations. Other map layers include overlays of weather radar images for precipitation and satellite images for cloud cover. Finally, users can consult a static thermal map and webcam images for highways in Flanders, and geolocated weather reports generated by citizens through the RMI weather app for the whole country. The physical RWM has been tuned for Belgian highways, and adapted to make use of various available numerical weather prediction (NWP) models as input. Road surface condition information from RWS is also used for better initialization. Since the winter of 2022-2023, the model is also run with a mini-ensemble input of four NWP models. This information is currently used to present an uncertainty interval around the RST forecasts, but more applications are foreseen. We present the operational system, GIS-interface and preliminary validation results of the ensemble road forecasts for the past winter. We also comment on specific use cases and present avenues for future improvements to the GMS system.

How to cite: Van den Bergh, J., Dehenauw, D., Watelet, S., Schmitz, J., Tijm, S., and Termonia, P.: The RMI Road Weather Forecasting System, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-334, https://doi.org/10.5194/ems2023-334, 2023.

Met4Airports is a research and development project funded by the German Federal Ministry for Digital and Transport (BMDV), aiming at the prediction of relevant planning and control parameters of air traffic management (ATM) by means of artificial intelligence (AI). It focusses on the effects of selected weather phenomena such as thunderstorms, fog, and snowfall, as they pose a significant disturbance for air traffic, causing capacity constraints for airports and en-route and approach sectors. The predicted quantities at the airport are runway and airport capacities, delays of individual flights as well as average delays for varying timespans up to 24 hours lead-time. Furthermore, capacities of the flight sectors close to the airport are predicted. These impact predictions can be used to support and optimize decision-making processes in ATM and enhance the situational awareness of decision makers.

Throughout the development process, various machine learning (ML) models are examined, relying on both meteorological forecast products of Deutscher Wetterdienst and air traffic data of airport operators (Flughafen München GmbH and Fraport) and air traffic control (Deutsche Flugsicherung). A detailed insight into this more technical part of the project as well as results of feature and hyperparameter studies from the machine learning process are given by Koser et al. [1].

The other major part of the project contains the validation and testing of the new impact predictions based on two different approaches using historical data from 2021/22. For the first approach, five days with thunderstorm events at or in the surrounding of the Munich airport are selected. The situations are displayed in a dashboard including all relevant weather and flight information of the respective day together with the new impact forecasts. Air traffic controller examine the selected situations and assess the benefit of the impact forecasts. The second approach compares the impact forecasts with the already available information from the ATM process. Both calculations of long-term statistics as well as single day studies are considered.

While the final evaluation of the air traffic controller of the first approach will be available during the upcoming summer, first analyses of both approaches already indicates that the ML models provide viable impact predictions on the selected thunderstorm days. Advantages over the already available information from the A-CDM system are visible for both statistical and single-day analyses. However, it can also be seen, that in some cases the impact predictions do not provide any profit, which might be due to the deficiencies in the input data like wrong weather predictions or air traffic disturbing processes which are not captured by the system.

[1] Koser et al. Development and optimization of Machine Learning methods to predict weather-induced operating restrictions in air traffic management, OSA1.9 Machine Learning in Weather and Climate, submitted for EMS 2023

How to cite: Knigge, C., Koser, D., Beckmann, B.-R., Zinkhan, D., Beumer-Aftahi, H., Müller, B., Melzer, A., Breitruck, I., Seitz, S., and Estrella, H.: Testing and validation of forecasts for weather-induced operating restrictions in air traffic management based on Machine Learning models, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-280, https://doi.org/10.5194/ems2023-280, 2023.

In-flight icing, i.e. the accretion of ice on airplane’s surfaces during flight, is caused by supercooled water droplets that freeze instantly when they impact the airframe and it represents a critical meteorological risk to aviation as it affects aircraft performance, stability and controllability. Therefore, the remote detection of weather conditions leading to in-flight icing is a goal of great interest to the scientific community.  

In 2017, the Meteorological Laboratory of CIRA has developed a first satellite-based tool for in-flight icing detection in collaboration with Italian Air Force Meteorological Service. This tool is based on several high-resolution satellite products of Meteosat Second Generation (MSG) and a set of experimental curves and envelopes describing the interrelationship of icing-related cloud variables that represent the icing reference certification rules, namely Appendix C to FAA 14 CFR Part 25 / EASA CS-25. However, Appendix C data do not consider Supercooled Large Droplets (SLD), which have been the cause of tragic accidents over the last decades and that have been introduced in new certification procedures and guidelines through the Appendix O, effective as of 2015. In the framework of the H2020 EU project SENS4ICE (SENSors and certifiable hybrid architectures for safer aviation in ICing Environment) started in 2019, CIRA is working on a further maturation of the previously developed icing detection algorithm, in order to consider also Appendix O Icing Conditions. The developed tool is targeted to identify areas potentially affected by in flight icing hazard, giving an estimate of the altitude and of the severity of the phenomenon (light, moderate, severe) with indication of possible SLD conditions.

In the present work an overall description of the implemented tool is provided along with an analysis of its performance. Due to the lack of suitable in-situ observations of icing conditions, a complete validation of the developed product is challenging. A comparison with significant weather charts has been performed and other validation activities based on the comparison with soundings data are ongoing, showing quite good results. Furthermore, this tool is currently being used in the framework of the SENS4ICE flight test campaign (scheduled in April 2023), which represents a good opportunity to evaluate its performance in environmental icing conditions. During the flight tests, information on monitoring of icing conditions are provided in the pre-flight phase and updated in near-real time. The outcomes of the flight test campaign will be exploited to identify the strengths and weaknesses of the algorithm.

Acknowledgment: This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement N° 824253 (SENS4ICE project).

How to cite: Zollo, A. L. and Bucchignani, E.: An aviation support tool for satellite remote detection of in-flight icing., EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-297, https://doi.org/10.5194/ems2023-297, 2023.

Within the framework of the OBeLiSk project funded by the  Federal Aeronautical Research Programme (LuFo VI-1), we investigate the integration of stratospheric drones into the air space with special focus on weather challenges and data supply. While on one hand the stratospheric drones, so-called high altitude pseudo satellites (HAPS), form a special use case, the challenge to supply suitable weather data for new air space users is a much wider and more general field of interest.

Having a glider-like design with a wide wingspan makes the HAPS very weather-susceptible. They fly very slowly, thus achieving only small climb rates. Hence, ascend and descent take long time and play an important role in the operational concept of the HAPS. For this reason, special focus must be put on the distance between earth’s surface and the first kilometer above ground, where both, other air traffic but also weather conditions feature lots of constraints. Laterally well resolved weather data in this range is not only key for the HAPS, but ultimately addresses a much wider spectrum of users. Since, however, data sets and their supply ways to the customer in this field are still sparsely available, new dataset products as well as suitable web application programming interfaces (web-APIs) are needed.

Moreover, particularly strategic flight planning for the next days requires probabilistic weather information since deterministic weather forecast lacks reliability in this regime. But, probabilistic ensemeble-data consists of vast amounts of data that can be challenging to handle. We present a new prototype probabilistic weather data set with detailed height level resolution based on the ICON-D2 NWP model of the Deutsche Wetterdienst. We combine this with the prototype of a new web-API that allows the evaluation of general probabilistic weather data in different height levels in a universal and user-driven fashion. With this contribution we address new air space users but also customers interested in near-surface probabilistic weather information.

How to cite: Anger, F., Niebuhr, H., Metzinger, I., Lang, J., Wetter, T., Beckmann, B.-R., Zinkhan, D., and Jerg, M.: Weather data supply for high altitude pseudo satellites (HAPS): A use case of a new prototype universal Web-API for probabilistic height-resolved weather datasets, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-308, https://doi.org/10.5194/ems2023-308, 2023.