Increasingly as transport networks expand, with climate change and as our dependence on technology increases, we see that there is a need to mitigate against the disruption of land, sea and air transport.
This session invites contributions from those involved in developing weather-based solutions for reducing risk to air, sea and/or land transport. In particular, participants are encouraged to discuss strategic risk reduction in transport at organizational or national level, perhaps achieved through engagement with the aviation or marine community, stakeholders and users in road and rail networks.
In addition, the session welcomes presentations on other aspects of transport meteorology, including impact studies and verification of forecasts, meteorological services in the cockpit, and environmental impacts of aviation and other forms of transport.
Gravel roads constitute a significant portion of rural transportation networks. In Finland, 27 000 km of state roads and most of the 350 000 km of private roads are gravel roads. Gravel roads are subject to seasonal and weather-induced variations that greatly affect their drivability. The frost layer during winter increases the load-bearing capacity of the road, allowing extra heavy trucks to pass. However, the thawing of ice in spring causes muddy roads that are impassable by heavy vehicles. Information about gravel road conditions and timing of road freezing and thawing is vital for planning timber transportation and is useful for private road users and emergency services as well. Currently used methods for predicting gravel road conditions in Finnish Meteorological Institute (FMI) are coarse and do not account for local variations. However, FMI is in the process of developing a new model for predicting gravel road conditions. The model is based on the FMI’s road weather model RoadSurf that is used on asphalt roads. RoadSurf is one dimensional heat balance model that forecasts road surface temperature and road conditions. The model’s parameters will be adjusted, particularly those related to physical properties like thermal conductivity, to better represent gravel road conditions. The accumulation of snow, ice and water on the road will be also modified. Additionally, the model will be enhanced to account for water flow in the road. The weather during autumn and winter considerably affects the severity of the spring thaw weakening. Rainy autumn and mild winter cause higher water content and slower freezing, which causes more severe spring thaw weakening. On the other hand, fast freezing at the start of the winter usually leads to meager spring thaw weakening. To take into account the long-term variations, the length of the gravel road simulations needs to be considerably longer than when predicting asphalt road conditions. The model will be verified by using observations from stations that measure gravel road temperature and moisture through electric conductivity at different depths.
How to cite: Karsisto, V.: Forecasting road conditions on gravel roads, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-104, https://doi.org/10.5194/ems2024-104, 2024.
In recent years, DWD has developed a ground-breaking nowcasting methods for thunderstorms (TS) and strong convection based on intelligent combination of lightning data, satellite information and Numerical Weather Prediction (NWP). The respective NowCastSat-Aviation (NCS-A) method covers the complete geostationary ring and is used in cockpits of many airlines around the world in order to reduce risks of injury by thunderstorms and associated turbulences. However, NCS-A covers only forecasts up to 2 hours. Thus, recent developments focus on the extension of the forecast horizon by deep learning and combination with an ensemble analysis of the Lightning Potential Index (LPI), provided by the DWD NWP model ICON. Both approaches attempt to overcome the limitations associated with the pure extrapolation of objects with Atmospheric Motion Vectors (AMVs). Although blending with NWP enables an improvement of the forecasting capability for predictions up to 3 hours, the CSI drops below 0.5 afterwards. This results from the chaotic nature of thunderstorms, which makes it difficult to model TS accurately. Within this scope it is discussed whether Artificial Intelligence will be able to replace numerical weather modeling in the near future. In fact, the University of Mainz has shown in a joint project that neural networks can learn something about the lifecycle and thus the physics of thunderstorms, leading to an improvement of CSI compared to classical nowcasting.
The talk presentation will start with an overview of the current 24/7 thunderstorm nowcasting. This is followed by a presentation and discussion of the current developments at DWD aimed at providing accurate forecasts of thunderstorms up to 6 hours, including a discussion of NWP versus AI. The presentation will close with overview about the status and the further plans concerning volcanic ash and turbulence nowcasting, both danerous for traffic as well.
References:
Barleben, A.; Haussler, S.; Müller, R.; Jerg, M. A Novel Approach for Satellite-Based Turbulence Nowcasting for Aviation. Remote Sens. 2020, 12, 2255. https://doi.org/10.3390/rs12142255
Müller, R.; Barleben, A.; Haussler, S.; Jerg, M. A Novel Approach for the Global Detection and Nowcasting of Deep Convection and Thunderstorms. Remote Sens. 2022, 14, 3372. https://doi.org/10.3390/rs14143372 4936-
Brodehl, S.; Müller, R.; Schömer, E.; Spichtinger, P.; Wand, M. End-to-End Prediction of Lightning Events from Geostationary Satellite Images. Remote Sens. 2022, 14, 3760. https://doi.org/10.3390/rs14153760
Müller, R.; Barleben, A. Data Driven Prediction of Severe Convection at DWD. An Overview of Recent Developments. Preprints 2024, 2024031179. https://doi.org/10.20944/preprints202403.1179.v1. In the meanwhile accepted for publication in Atmosphere.
How to cite: Müller, R., Barleben, A., and Jerg, M.: Nowcasting applications of remote sensing to reduce weather risks to traffic. , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-358, https://doi.org/10.5194/ems2024-358, 2024.
Aviation turbulence is a hazardous weather phenomenon that affects aviation safety and operations. It can be in the form of convective induced turbulence (CIT), clear air turbulence (CAT), mountain waves or in relation to low level jets. Currently, observations of aviation turbulence are based on either reports from pilots or automated observations from the equipment on board of aircraft. Pilot reports were scarce and could be subjective. Besides, due to the wide range of aircrafts deployed by different airlines, not all aircraft were equipped with automatic equipment to measure turbulence. As aviation traffic recovers, the volume of navigation information broadcasted by commercial aircraft has also increased significantly. The Observatory has installed an Automatic Dependent Surveillance - Broadcast (ADS-B) reception system at Tai Mo Shan Weather Radar Station to track ADS-B equipped aircraft within a range of approximately 500 km. This study attempts to identify and estimate turbulence from these ADS-B signals with an aim to expand turbulence observation over the ADS-B coverage. The analysis would be based on the vertical acceleration and attitude information extracted from ADS-B aircraft data. As aircraft maneuvers would induce noise to the turbulence signal, an algorithm was developed to first identify the en-route phase of the aircraft. As an initial step, the study focused on the en-route phase of aircraft only. Several methodologies, including frequency analysis, peaks analysis, etc., were developed to derive turbulence information from ADS-B data. The results were then compared with the observations from flights that encountered turbulence in 2023. The comparison analysis using different methodologies and their limitations would be discussed. Such turbulence detection algorithm has the potential application on the provision of real-time turbulence information within the ADS-B coverage to aviation users.
How to cite: Leung, C. Y. and Leung, H. T.: Estimating en-route turbulence using ADS-B aircraft data, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-635, https://doi.org/10.5194/ems2024-635, 2024.
The Cross-Border Convective Forecast (CBCF) is a collaborative forecast that provides the Network Manager (NM) at EUROCONTROL and participating air navigation services a forecast of convective weather across European Airspace. Eurocontrol, as a primary user, uses this information to coordinate with impacted Air Traffic Control Centres (ACCs) across Europe and implement mitigation measures to minimise flight delays and improve aircraft safety. Currently, Eumetnet established CBCF as a module within the Aviation Support Program.
The user-oriented forecast started in 2018 and is still developing. Since 2020, aviation weather forecasters of all services who participate simultaneously produce the forecast on a collaborative web tool. From 2022, 24 organisations are participating - from Portugal and Ireland up to Poland and Greece. The number of forecasters included in production of forecast during a year is more than 200, which is probably unique worldwide. To ease dissemination of the product, the lead forecaster coordinates the final product and issues and communicates with the user.
CBCF is a probabilistic forecast of polygons in the domain in 3-hour periods for tomorrow and the current day. Local meteorological service providers create polygons for their area of responsibility, harmonised across state borders. The risk matrix is a function of the likelihood and extent/severity of convective weather with additional CB-top height information.
Looking at the European domain, Croatia and its neighbouring area have frequent thunderstorm activity. In a presentation, we will share experiences from a forecaster's perspective in Croatia. The product is easy to produce, fast, and flexible for editing, and the chat is easy to use. Despite the country's unique shape, collaboration and coordination with neighbours are mostly very positive.
How to cite: Jurković, J. and Šoljan, V.: European Collaborative Convection Forecast for Air Traffic Management - Experiences from Croatian MET Provider, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-969, https://doi.org/10.5194/ems2024-969, 2024.
Aviation operations are impossible without reliable weather information. Safe air traffic management requires precise meteorological predictions across various timeframes, ranging from immediate nowcasting (0-3h) to mid-range forecasting (4-30h). Given the inherent uncertainties in predictions, it is crucial to ensure that users can interpret uncertain weather data confidently. This enables effective planning and decision-making within aircraft operations, including setting approach rates and runway configurations.
Currently, the aviation meteogram product provided by MeteoSwiss offers deterministic predictions and threshold exceedance probabilities at hourly intervals up to 24 hours in advance. This data is derived both from direct numerical weather model output and statistically post-processed forecasts. However, this approach underutilizes the forecast potential due to (1) possible biases in deterministic predictions, (2) limited granularity in lead time within the nowcasting range, and (3) a lack of uncertainty information passed on to decision makers.
To overcome these challenges, we are transitioning from delivering deterministic to probabilistic forecasts by employing machine-learned local predictions instead of relying solely on physical model output. Additionally, we are increasing output granularity in the first three hours. This shift unlocks the full potential of information-based decision-making, leading to smoother and more economically and ecologically sustainable aviation operations. Given that probabilistic data and its implications are largely unfamiliar to our product users, a robust program of frequent training and education is essential.
In this contribution, we introduce new machine learning based ensemble predictions for thunderstorms [1], visibility conditions [2], and wind at Swiss airports, focusing on nowcasting and mid-range forecasting. Our emphasis lies in their comprehensive visualization in a web-based meteogram tailored to effectively convey probabilistic information.
[1]: abstract Attinger et al., 2024, submitted to EMS 2024.
[2]: abstract Wehrli et al., 2024, submitted to EMS 2024.
How to cite: Landmann, J. M., Attinger, R., Aznar Siguan, G., Barras, H., Irrgang, M., Reiniger, T., Wehrli, K., Exterde, S., Jordi, T., and Stocker, C.: The next generation meteogram: enhancing decision making for aviation stakeholders, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-499, https://doi.org/10.5194/ems2024-499, 2024.
Japanese railway companies monitor strong winds along their lines with anemometers installed at intervals of several kilometers to several tens of kilometers on average, in order to prevent railway disasters on railways caused by strong winds. When strong winds are observed by anemometers monitoring each section, train operations are controlled. Specifically, if the instantaneous wind speed observed by an anemometer exceeds a certain threshold (usually 25 m/s, on conventional lines in Japan), the speed of train traveling on that section is limited (slowed down) or suspended. However, if strong winds continue intermittently, train operation is repeatedly suspended and resumed, resulting in reduced transportation service. Therefore, we are studying to develop a method for forecasting the short-term instantaneous wind speeds that can be directly used for operation control under strong winds. The Japan Meteorological Agency (JMA) and private weather companies provide wind speed forecast information, but their forecasts are averaged wind speeds and generally have a time resolution of more than one hour. On the other hand, operation control under strong winds requires forecasts of instantaneous wind speeds with a time resolution of about ten minutes.
We have developed a method for forecasting 10-minute maximum instantaneous wind speeds using instantaneous wind speed data observed at anemometers and time series analysis. Our method can forecast 10-minute maximum instantaneous wind speeds up to two hours ahead by using different parameters for the time series analysis depending on the synoptic scales that cause strong winds: typhoon, low pressure, front, winter monsoon, and others. We have also developed a method to automatically classify these five types of synoptic scales from surface weather maps using deep learning.
We have confirmed that the synoptic scale can be automatically classified from surface weather maps with an accuracy of about 70%. As a result of forecasting 10-minute maximum instantaneous wind speeds for 50 cases of strong winds, it was confirmed that 10-minute maximum instantaneous wind speeds can be forecast up to one hour ahead with an RMSE of about 5 m/s or less, regardless of the synoptic scale. Furthermore, we have obtained the prospect of reducing the number of operation control under strong winds and the total suspension time by using our forecasts, compared to the current operation control under strong winds that uses only observed wind speeds.
How to cite: Kurihara, A. and Araki, K.: Developing Method to Forecast the Short-term Instantaneous Wind Speed for Railways Operation Control under Strong Winds, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-152, https://doi.org/10.5194/ems2024-152, 2024.
Aviation turbulence, one of the major weather hazards in the aviation industry, is normally classified by generation sources, as follows: convectively induced turbulence (CIT), clear-air turbulence (CAT) that is not directly associated with convective systems, but is mainly caused by various atmospheric instabilities near the jet stream, and mountain wave turbulence (MWT) related to vertically propagating mountain waves generated by low-level flows across mountainous areas. To reduce damage caused by unexpected aviation turbulence encounters, we developed the Korean aviation Turbulence Guidance (KTG) system by combining various large-scale forcing-based CAT and MWT diagnostics calculated from the Korea Meteorological Administration (KMA)’s operational global numerical weather prediction (NWP) model outputs. This has been used successfully for operational turbulence forecast. The KTG system generally shows good performance skills for null- and moderate-or-greater (MOG)-level turbulence, but when focusing only on the performance results for MOG-level turbulence, it shows low performance skill of about 30% (i.e., 0.3 of probability of detection for yes; PODY). In this study, to analyze the cause of the low PODY of the KTG forecast for MOG-level turbulence, we looked at all of the unpredicted MOG-level turbulence events from in situ turbulence observation data for one year. And, the spatial and temporal characteristics of the unpredicted MOG-level turbulence events are analyzed to understand which places are the most unpredicted areas in a given NWP model. We also tried to understand how individual CAT and MWT diagnostics currently used in the KTG system are correlated well with the unpredicted and predicted turbulence events. This will be eventually useful for improving the current version of the KTG system to take into account any possible forecasting capability of CIT and other unknown sources related to CAT and MWT.
Acknowledgment: This work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI2022-00410, and was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (RS-2023-00250021).
How to cite: Lee, D.-B. and Kim, J.-H.: Spatial and Temporal Characteristics of Unpredicted Upper-Level Turbulence Events using the Global Aviation Turbulence Forecast System and In-Situ Aircraft Data, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-603, https://doi.org/10.5194/ems2024-603, 2024.
Due to fuel efficiency most air traffic takes place at the top of the troposphere, always avoiding deep convective clouds because of associated hazards such as severe turbulence, icing, hail, and lightning. If deep convection cloud tops are not very high, airplanes can sometimes fly above them, deviating less from their planned flight paths and causing less delays. Because of this, deep convection cloud top diagnosis and forecast is very important in aviation meteorology. One of the methods to estimate altitude of existing convective cloud tops is to compare infrared satellite brightness temperature with a calculated parcel curve temperature (parcel theory). The premise here is that we already have deep convective clouds so we are not interested in the full vertical temperature profile (sounding). To calculate the cloud top pressure, which is directly related to altitude in the standard atmosphere, we only need the parcel curve and measured satellite brightness temperature. Parcel curve is usually calculated iteratively from surface temperature and dewpoint, but this calculation can be computationally intensive for many points. In the first part of this work we will test different approximations of moist adiabat calculation and compare them to classical iterative method to see which one gives us acceptable error for estimating cloud top heights from satellite data. The second phase will implement the best approximation in operational forecaster environment.
We will also try to test some expected limitations of this approach, as calculation of parcel curve from only surface temperature and dewpoint is realistic only for diurnal deep moist convection, while in the case of elevated convection the most unstable layer temperature and dewpoint should be used.
How to cite: Šoljan, V., Jurković, J., and Babić, N.: Fast approximation for calculating deep convection cloud top heights from satellite brightness temperature, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-861, https://doi.org/10.5194/ems2024-861, 2024.
