Daphne LaDue, David Roueche, Frank Lombardo, and Lara Mayeux
When a tornado strikes a permanent or mobile/manufactured home, the people inside are at risk of injury and death from blunt force trauma caused by debris-loaded winds and failure of the structure. Although the mechanisms for these structural failures have been studied in recent decades, linking how tornadic winds interact with residential buildings and how human decisions affect survival has rarely been done due to the inherent complexity and cost of conducting joint social science, forensic engineering, and wind engineering assessments. This study developed and tested an interscience protocol to holistically evaluate risk factors related to the tornado and surroundings, efficacy of available shelter, and influences on decision-making regarding sheltering in individual homes in the southeast U.S. Interviews with tornado survivors at over 90 home sites ranged from 8 to 90 min long. We developed our protocol through the course of the study, ending with one page of phrases to prompt the interviewer, who could the retain eye contact and build relationship with the survivor through the course of the interview. The final protocol also included a method for noting whether topics had been raised in the survivor’s story and had also been asked in a follow-up question. Five tornado deployments were particularly successful, with nine to 18 interviews for each of those tornadoes. These tornadoes occurred at different times of day and days of the week. This talk will focus on how key factors in survivor’s stories varied across the events and when the factors did not have the intended effect; for example, some knew about the chance of tornadoes but were not monitoring the weather. Patterns emerged for most tornadoes, with environmental cues (e.g., seeing, hearing, or feeling the tornado coming) being the most important factor in sheltering decisions. Alerts were particularly important for the two nighttime tornadoes. The talk will close with a few highlights from the interscience analysis, illustrating how survivors’ stories, photo/videos, and access to structural elements were crucial for understanding how the tornado interacted with the residence.
How to cite:
LaDue, D., Roueche, D., Lombardo, F., and Mayeux, L.: Advancing Forensic Engineering Analyses of Tornadoes with Survivor’s First-Hand Accounts, 12th European Conference on Severe Storms, Utrecht, The Netherlands, 17–21 Nov 2025, ECSS2025-46, https://doi.org/10.5194/ecss2025-46, 2025.
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Simon Eng, Julian Brimelow, Jack Hamilton, Areez Habib, and Dylan Painchaud-Niemi
On August 5, 2024, a major wind-driven hail event hit northern portions of the City of Calgary, Alberta—Canada's 5th most populous metropolitan area. Large volumes of hail with maximum diameters of 40 to 50 mm were accompanied by outflow winds gusting from 65 km/h to possibly over 100 km/h. This affected residential, commercial and industrial areas, as well as the international airport. With an estimated insured loss of $3.25 billion CDN, this event is both the costliest hailstorm and the costliest severe convective event in Canadian history.
On the day following this event, the Northern Hail Project (NHP) deployed its Rapid Response Survey (RRS) for Major Urban Hailstorms, executing what is likely the most thorough damage survey of an urban hailstorm in North America. First, a team was deployed to conduct a scouting mission, locating the worst affected areas, as well as defining the boundaries of urban hail impacts. Following this, a rotating group of three teams was deployed to document— through extensive ground and aerial surveys— impacts in the worst affected areas. These teams made 49 drone flights, conducted over 100 eyewitness interviews, and drove over 1000 km. This was supplemented by outreach (using phone and social media) for locations with limited access, and by consultation with roofing and building repair companies.
The survey documented damage to residential and commercial buildings, vehicles and other assets. Information was collected on damage to a variety of residential roofing and siding materials, including hail-resilient products. Single-, double- and triple-pane building windows were broken. Water penetrated the flat roofing systems of commercial and industrial buildings. Vehicle damage (~1/3 of the insured loss) was recorded in residential areas (personal and fleet vehicles) and vehicle storage lots. Notable damage at Calgary’s International Airport included roof damage and water penetration, as well as severe damage to dozens of commercial aircraft.
Damage information will be compared to hail data from multiple sources, including hail samples collected by the NHP, and disdrometer stations within the CSSL’s disdrometer network. Findings will be used to support and refine the identification of hail-resistant construction materials, and to inform risk reduction measures and hail event scenario modelling. Our findings indicate that, given the significant amount of damage from this event and the potential for even worse hailstorms to hit the city, much greater damage could result from future events unless widespread damage reduction measures are implemented across Calgary.
How to cite:
Eng, S., Brimelow, J., Hamilton, J., Habib, A., and Painchaud-Niemi, D.: Forensic Damage Assessment of a $3 Billion Urban Hailstorm – August 5, 2024 Calgary, Alberta, Canada, 12th European Conference on Severe Storms, Utrecht, The Netherlands, 17–21 Nov 2025, ECSS2025-242, https://doi.org/10.5194/ecss2025-242, 2025.
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The Royal Netherlands Meteorological Institute (KNMI) has been responsible for weather forecasting in the Caribbean Netherlands (Bonaire, St. Eustatius and Saba – the BES islands) since 2016. And while weather patterns in the Caribbean often exhibit homogeneous characteristics, this region is also prone to some of the most violent storms on earth in the form of hurricanes. Infamous examples of this are Hurricanes Irma and Maria (2017), which both passed close to Saba and St. Eustatius.
With its extensive weather forecasting expertise as a solid foundation, KNMI is now moving towards impact-based forecasting through the development of the Early Warning Centre. For the BES islands, this means that we will design a hurricane impact model, combining KNMI's forecasting experience with impact modeling expertise nested within academia. This impact model will operate on two spatial levels: as a network model to analyze disruptions in transportation networks between Caribbean islands, and an impact model to study impacts on the islands themselves. For the latter, we follow the traditional risk modeling approach and set up a hazard – exposure – vulnerability type of model chain. However, one of the major challenges we’ll be facing is the spatial scale of particularly the islands of Saba and St. Eustatius: the islands have a surface areas of respectively 13 km2 (5 sq mi) and 21 km2 (8.1 sq mi), meaning they can easily vanish in a catastrophe model. We will therefore rely on local stakeholders, who will help develop and improve exposure and vulnerability input data.
How to cite:
Bloemendaal, N., Koks, E., and Sluijter, R.: Towards hurricane impact forecasting for the Caribbean Netherlands, 12th European Conference on Severe Storms, Utrecht, The Netherlands, 17–21 Nov 2025, ECSS2025-11, https://doi.org/10.5194/ecss2025-11, 2025.
Anna del Moral Méndez, Tammy M. Weckwerth, Christopher D. Wirz, Rita D. Roberts, and James W. Wilson
Lake Victoria Basin (LVB), home to over 40 million people in East Africa, plays a vital role in the region’s economy, particularly through fishing and agriculture. However, this densely populated area lies in a global hotspot for convective activity, making it highly vulnerable to severe weather hazards. With one of the world’s fastest-growing populations and climate change projections suggesting a future with more extreme weather and stronger thunderstorms, the region faces increasing societal exposure to natural hazards.
The region’s complex topography and land-lake interactions contribute to frequent nocturnal severe storms. Each year, a high death toll of ~1000 fishermen occurs due to high waves resulting from severe convective winds. Furthermore, the basin region has experienced a rise in destructive hailstorms and flood-producing rains. Unfortunately, despite the growing risk, only 40% of Africa’s population has access to Early Warning advisory systems, and less than 4% of global climate change research funding is allocated to the continent.
Building effective research-to-operations pathways remains difficult due to limited sustainable observational systems for weather monitoring, forecasting and warning. Nevertheless, initiatives like the World Meteorological Organization’s “High Impact Weather Lake System” project, launched in 2017, have made impressive progress. For instance, the 2019 project-related field campaign used S- and C-band dual-polarization radars to collect high-resolution data on convective storms over the lake, advancing scientific understanding and forecasting capacity.
Capacity-building has continued through workshops led by the Climate Risk and Early Warning Systems and the Severe Weather Forecasting Project (WMO), among others. The two most recent workshops, in Kigali, Rwanda, in 2023, and in Entebbe, Uganda, in 2025, gathered forecasters from the National Meteorological and Hydrological Services of nine East African countries. These WMO training sessions focused on nowcasting techniques, radar and satellite-based severe weather identification, modeling, and collaborative case study analysis. They also facilitated ongoing discussions on forecasting needs and operational challenges across the region.
This work highlights the current state of radar-based severe weather research in Lake Victoria, showcasing severe weather events like waterspouts and downbursts (in the tropics!) in a poorly observed area of the world, as well as operational difficulties. It shares lessons learned from regional training and collaboration while identifying critical gaps and opportunities for international cooperation. These collective efforts aim to improve monitoring, nowcasting, forecasting capabilities, and communication and warning systems, ultimately supporting the goal of building a more resilient LVB through science, training, and cross-border collaboration.
How to cite:
del Moral Méndez, A., Weckwerth, T. M., Wirz, C. D., Roberts, R. D., and Wilson, J. W.: From Observation to Action: Enhancing Forecasting Capabilities over Lake Victoria Through Radar Analysis and Regional Collaboration, 12th European Conference on Severe Storms, Utrecht, The Netherlands, 17–21 Nov 2025, ECSS2025-144, https://doi.org/10.5194/ecss2025-144, 2025.
Constanza Inés Villagran Asiares, M. Gabriela Nicora, Paola Salio, Hernan Bechis, Vito Galligani, Eldo E. Avila, and Amalia Meza
Atmospheric electrical activity (AEA) reflects the microphysics and internal dynamics of storms, and abrupt variations in AEA, known as Lightning Jumps (LJ), can anticipate the occurrence of severe weather events up to 45 minutes in advance. In 2023, the South American Meteorological Hazards and Impacts Database (SAMHI) (https://samhi.cima.fcen.uba.ar/) was created to collect reports of hail, tornadoes, floods, and strong wind gusts, aiming to enhance the understanding, remote detection, and predictability of these phenomena in the context of climate change in South America.
Given that collecting reports, particularly in rural areas, remains a significant challenge, this study aims to enrich the SAMHI database and identify vulnerable regions that have been previously underestimated. To this end, reports of hail, tornadoes, and strong wind gusts from the 2018–2023 period were used, along with Lightning Jump data from 2009–2023. LJ data were derived by processing AEA data from the World Wide Lightning Location Network using the Georayos algorithm (https://georayos.citedef.gob.ar/), which applies the Density-Based Spatial Clustering of Applications with Noise method to group lightning and detect significant increases in lightning rates.
The connection between severe weather events and LJ was analyzed across Argentina, Uruguay, and southern Brazil, a region recognized as one of the most active in the world for severe storms, frequently experiencing hail, tornadoes, floods, and intense wind gusts, with significant economic impacts on sectors such as agriculture, infrastructure, and energy. The analysis consisted of assessing the presence of LJ before and after each reported event, followed by the application of the K-Means clustering algorithm to identify the regions with the highest frequency of experiencing severe events.
The results show a significant correspondence between LJ occurrences and reported severe events, with an average lead time of 29.3 minutes. Over 60% of the LJ cases preceded severe events, with correspondence rates of 70% for hail, 60% for tornadoes, and 50% for strong wind gusts. In addition, Uruguay, northern and central Argentina and southern Brazil were identified as the regions most likely to experience adverse weather conditions. The use of LJ data also made it possible to characterize the different regions by type of most frequent severe events and to identify vulnerable areas previously underestimated, probably due to factors such as low population density, limited access to communication routes, among others.
These findings highlight the importance of integrating LJ data to enhance early detection of severe weather events across South America.
How to cite:
Villagran Asiares, C. I., Nicora, M. G., Salio, P., Bechis, H., Galligani, V., Avila, E. E., and Meza, A.: Enhancement of the South American Meteorological Hazards and Impacts Database (SAMHI) through Atmospheric Electrical Activity as a Proxy for Severe Weather Event Detection, 12th European Conference on Severe Storms, Utrecht, The Netherlands, 17–21 Nov 2025, ECSS2025-99, https://doi.org/10.5194/ecss2025-99, 2025.
Maria del Socorro Fonseca-Cerda, Hans de Moel, Jeroen Aerts, Wouter Botzen, and Toon Haer
Extreme hailstorms, linked to convective systems, can cause significant societal impact and account for ~30% of insured losses from 2007 to 2022 in the Netherlands. Stochastic models can quantify hailstorm impacts, but assessing the hazard and its consequences remains challenging due to a lack of consistent observations and detailed post-disaster losses. Our study addresses this research gap and investigates hailstorm occurrence and impacts by combining high-resolution radar-based data, meteorological observations, and reanalysis data with a unique asset-level insurance loss dataset for the Netherlands.
Random forest models are used to identify proxy variables for hailstorm hazard and associated losses. Meteorological and convective variables are used to identify hail or damage days, testing different Maximum Expected Hail Sizes (MEHS) thresholds and train-test designs. Training the model solely on hail days and the entire hazard time series results in poor performance with correct predictions but many false positives. However, using a 70-30% (train-test) random selection from the entire hazard series enhances performance, especially when the training sample contains more positive observations. Random forest models, despite not providing direct hazard intensity information, effectively highlight influential proxies like CAPE and dewpoint temperature, which can be refined to enhance hailstorm prediction, frequency, and damage thresholds. Random forest models appear to be a promising option for further improving hazard and loss models. Our findings offer valuable insights to enhance hailstorm hazard and loss assessments.
How to cite:
Fonseca-Cerda, M. S., de Moel, H., Aerts, J., Botzen, W., and Haer, T.: Towards improved hailstorm and loss prediction using random forest in the Netherlands , 12th European Conference on Severe Storms, Utrecht, The Netherlands, 17–21 Nov 2025, ECSS2025-13, https://doi.org/10.5194/ecss2025-13, 2025.
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Felix Erdmann, Lesley De Cruz, Maarten Reyniers, Ricardo Reinoso-Rondinel, Dieter R. Poelman, and Michiel Van Ginderachter
Heavy rainfall followed by flooding is one of the most damaging weather phenomena in Belgium and Western Europe. Therefore, accurate forecasting of such extreme precipitation events is crucial for effective disaster preparedness and mitigation.
The Royal Meteorological Institute of Belgium (RMI) operates pySTEPS-BE, a seamless ensemble nowcast system built with the open-source and community-driven pySTEPS library [2]. PySTEPS-BE uses radar-based QPE and ALARO-AROME NWP input to produce 6-hour ensemble forecasts via scale-dependent blending and noise modeling, following the STEPS methodology [2,3].
PyRainWarn translates these probabilistic nowcasts into warnings at high resolution, considering uncertainties, which is not possible using deterministic nowcasts. Probabilities of exceeding rainfall thresholds are defined as the proportion of ensemble members exceeding a reference threshold. The threshold values are calculated for four return periods and four accumulation durations based on spatial generalized extreme value models [4]. They are compared to each of the pySTEPS-BE members to obtain the exceedance probabilities per pixel of the rainfall nowcast, and further processed on the administrative scale of municipalities. Finally, each municipality is colored based on 4 warning levels that are defined with the severity, i.e. the return period, and the probability, i.e. the likelihood of the event.
We present the theory and methods behind these novel extreme rainfall warnings, as well as a demonstration of the interactive map output and some actual examples. The product is currently being developed for expert users such as hydrology and emergency services, but we plan to condense and simplify the output and make it available to the general public.
References
[1] Pulkkinen, S., Nerini, D., Pérez Hortal, A. A., Velasco-Forero, C., Seed, A., Germann, U., and Foresti, L (2019), Pysteps: an open-source Python library for probabilistic precipitation nowcasting (v1.0), Geosci. Model Dev., 12, 4185–4219, https://doi.org/10.5194/gmd-12-4185-2019.
[2] Bowler, N.E., Pierce, C.E. and Seed, A.W. (2006), STEPS: A probabilistic precipitation forecasting scheme which merges an extrapolation nowcast with downscaled NWP. Q.J.R. Meteorol. Soc., 132: 2127-2155. https://doi.org/10.1256/qj.04.100.
[3] Imhoff, Ruben O., et al. (2023): Scale‐dependent blending of ensemble rainfall nowcasts and numerical weather prediction in the open‐source pysteps library, Quarterly Journal of the Royal Meteorological Society 149.753 (2023): 1335-1364.
[4] Van de Vyver, H. (2012): Spatial regression models for extreme precipitation in Belgium, Water Resour. Res., 48, W09549, https://doi.org/10.1029/2011wr011707.
How to cite:
Erdmann, F., De Cruz, L., Reyniers, M., Reinoso-Rondinel, R., Poelman, D. R., and Van Ginderachter, M.: PyRainWarn: pySTEPS-BE ensemble nowcasts for extreme rainfall warnings in Belgium, 12th European Conference on Severe Storms, Utrecht, The Netherlands, 17–21 Nov 2025, ECSS2025-49, https://doi.org/10.5194/ecss2025-49, 2025.
Please use the buttons below to download the supplementary material or to visit the external website where the presentation is linked. Regarding the external link, please note that Copernicus Meetings cannot accept any liability for the content and the website you will visit.
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Petra Mikus Jurkovic, Katarina Katusic, and Matea Stibuhar
Croatian Meteorological and Hydrological Service (DHMZ) is responsible for weather warnings in Croatia and issued it on official webpage meteo.hr, as well as on meteoalarm.org. Information about weather warnings are communicated with Civil Protection Directorate (CPD) using standard operation procedure on everyday basis. Additionally, DHMZ uses social networks and media for distribution of the weather warnings to the public. But, in 2023 a huge step was done in alerting the public. Ministry of Internal Affairs of the Republic of Croatia formed the procedure for CB/SMS warning system – Early Warning and Crises Management System (SRUUK). Although system was in the final phase in 2023, severe derecho storm in July that hit continental part of Croatia and cause huge damage, unfortunately, also four fatalities, was the big additional motivation for the earliest possible operational application, and it started in August 2023.
DHMZ has a crucial role in decision to alert the public via CB/SMS message when the severe weather phenomena tend to develop into a major accident or disaster, while CPD coordinates the actions of all collaborators included in SRUUK. Since 2023 for few possible severe thunderstorm episodes the SRUUK was activated. In two cases forecasted extremely severe weather phenomena did not occur, although the environment was very conductive for severe organized convective thunderstorm. This reflects key issues in alerting the public via CB/SMS messages for severe thunderstorm. These are: definition of criteria in what kind of convective favorable environment the message should be sent, what information the message contain, when the message will be sent – for very likely forecast event (even more than 12 hours in advance) or for the severe thunderstorm that is already developed. It is necessary to have in mind that under certain conditions, such a warning can cause additional panic, further endangering human lives. Because of all of the above, extensive preparation is necessary, especially in cases of thunderstorms which are one of the most challenging to forecast, in order to maintain or build a high level of trust within the public.
How to cite:
Mikus Jurkovic, P., Katusic, K., and Stibuhar, M.: The public warning alerts in Croatia using the CB/SMS communication channel – special focus on severe thunderstorm warning, 12th European Conference on Severe Storms, Utrecht, The Netherlands, 17–21 Nov 2025, ECSS2025-74, https://doi.org/10.5194/ecss2025-74, 2025.
Elena Collino, Giosuè Maugeri, Riccardo Bonanno, and Salvatore Guastella
In 2024 the global share of solar accounted for 6.9% while in Italy this share exceeded 13% and continues to grow rapidly [1]. Given the constant increase in electricity demand, solar energy is playing a more and more significant role in meeting energy needs.
However, aside from the intrinsic weather-dependent nature of solar generation, the continuity of power supply can also be threatened by extreme weather events, which are becoming more frequent and severe due to climate change [2]. For example, windstorms and hailstorms can cause significant damage in a short period of time, leading to revenue losses from production interruptions. Sometimes the severity of damage is such that a complete reconstruction of the solar field becomes necessary, entailing substantial financial costs. Even when insurance coverage is in place, the lengthy process of compensation and reconstruction leads to further revenue losses for the producer.
An increase in atmospheric conditions prone to severe storms is expected [2], making mitigation strategies essential, especially in countries where solar power plays a crucial role in meeting energy demand peaks, such as in Italy
In this work, we analyse the needs and strategies that can be implemented to mitigate the impact of severe weather on solar production, highlighting the importance of an interdisciplinary approach.
To enhance the resilience of solar power systems against extreme weather events three key goals have been identified:
Advancements in PV component technology,
Optimization of system control strategies,
Development of effective early warning systems.
As for the first strategy, under the framework of the International Energy Agency Photovoltaic Power Systems Programme (IEA-PVPS) the Task 13 - a group of international PV experts - is addressing this challenge by conducting studies on how hail impacts depend on direction, density, and speed [3]. In this regard, historical weather and damage data becomes very relevant.
On the other hand, an effective early warning system, combined with robust PV tracker control units, plays a critical role in protecting PV installations from irreversible damage and mitigating the subsequent revenue losses.
Therefore, it is important to make a coordinated effort to monitor extreme weather events, to make historical weather and damage data available, and to foster collaboration among meteorologists, solar panel manufacturers, control system designers, and operators.
Ember (2025); Energy Institute - Statistical Review of World Energy (2024).
M. E. Pons e D. Faranda, «Assessing the future occurrence of severe thunderstorm environments in Europe: frequency, hotspots and impacts», 2021. https://api.semanticscholar.org/CorpusID:236764490
How to cite:
Collino, E., Maugeri, G., Bonanno, R., and Guastella, S.: Severe Weather: Challenges and Adaptive Solutions for Solar Power Resilience, 12th European Conference on Severe Storms, Utrecht, The Netherlands, 17–21 Nov 2025, ECSS2025-115, https://doi.org/10.5194/ecss2025-115, 2025.
Severe storm events in recent years have caused extensive flooding in various regions of Japan, creating significant challenges for rapid emergency response and damage assessment. This study presents a real-time flood mapping approach using images and videos from social media, implemented in a browser-based web GIS tool. The system enables users to input reference point and inundation depths to estimate inundation extents based on high-resolution elevation data. We applied this tool during two recent flood events: the July 2023 flood in Akita City and the September 2024 flood in Wajima City. Notably, the latter occurred in areas still recovering from the January 2024 Noto Peninsula Earthquake, amplifying the disaster's impact on vulnerable infrastructure and temporary housing. In both cases, flood conditions were estimated within hours after impact using social media data and the results were publicly available the same day. In Wajima, field surveys were conducted for post-event validation. Although some discrepancies were observed in areas where the terrain had changed due to earthquake-induced subsidence, overall agreement between estimated and observed flood depths was reasonably good. These results indicate that real-time flood mapping using crowd-sourced data is effective for flood disaster response and can supplement existing hazard information system. The study also highlights limitations in spatial accuracy and emphasized the need for automated region segmentation and multisource data fusion to improve scalability and reliability.
How to cite:
Hirano, K., P.c., S., and Iizuka, S.: Development and Field Application of a Real-time Flood Mapping Tool Using Social Media Imagery During Severe Storm Events, 12th European Conference on Severe Storms, Utrecht, The Netherlands, 17–21 Nov 2025, ECSS2025-150, https://doi.org/10.5194/ecss2025-150, 2025.
Francesco De Martin, Mario Marcello Miglietta, Thomas Gastaldo, Michele Martinazzo, Federico Pavan, Matteo Siena, and Silvana Di Sabatino
The Bayesian yacht sank in Porticello, Sicily, at 0206 UTC on 19 August 2024 during a thunderstorm. Of the 22 people on board, 7 lost their lives. An in-depth analysis of available observations highlighted that the ship was likely struck by a quasi-linear convective system. Satellite images showed a Mesoscale Convective System over the Tyrrhenian Sea between 2300 UTC on 18 August 2024 and 0300 UTC on 19 August 2024, with convective cells that lasted less than 1h. The storm motion of the cell that hit Porticello was not consistent with that expected for a right mover supercell, suggesting that supercells were not present during the event. A few videos taken along the coast captured very intense northwesterly wind gusts, with no evidence of rotating winds or waterspouts. Before sinking, the yacht drifted southeastward, pushed by the northwesterly wind. Data from weather stations revealed classic downburst features, such as an increase in pressure and a drop in potential temperature corresponding to the strongest gusts. No signs of mesocyclones (e.g. sudden pressure drop) were detected.
The predictability of the event was also investigated. Operational simulations performed one day ahead with the ICON-2I model, running at 2.2 km horizontal resolution over a domain centred on Italy, pointed out that a convective wind gust hazard could have been expected over the southern Tyrrhenian Sea that night. Furthermore, the satellite analysis showed that the storm developed 3h before the accident and kept a coherent trajectory during its lifetime, suggesting that there may have been enough time to warn people. Lastly, we remark that radar data were unavailable in the area affected by the storm, which is a significant limitation for nowcasting, early warning systems, post-event analysis and research.
How to cite:
De Martin, F., Miglietta, M. M., Gastaldo, T., Martinazzo, M., Pavan, F., Siena, M., and Di Sabatino, S.: The Bayesian sinking in Porticello: a predictable convective windstorm?, 12th European Conference on Severe Storms, Utrecht, The Netherlands, 17–21 Nov 2025, ECSS2025-195, https://doi.org/10.5194/ecss2025-195, 2025.
Thilo Kühne, Bogdan Antonescu, Pieter Groenemeijer, Alois Holzer, Tomáš Púčik, and Gabriel Strommer
Fatal lightning strike incidents over the past decades have been examined in studies of individual European countries. Nevertheless, there remains a notable absence of research focusing on historical periods. To provide a comprehensive analysis on this subject with a main focus on societal impact, this study investigates fatal lightning incidents in Germany during the 25-year interval from 1900 to 1924 (within the borders of 1990) and compares these findings with data from the period 2000 to 2024. The dataset utilized in this study is derived from the European Severe Weather Database (ESWD), which is managed by the European Severe Storm Laboratory (ESSL), and contains publicly available data derived from contemporary newspapers, church chronicles, and local authorities (police and fire departments), which are collected by ESSL and its partners.
In total, more than 2,500 individual lightning fatalities were recorded in the ESWD for both investigation periods. We report on the spatial distribution of fatalities, gender and age groups, work and leisure activities, and further circumstances which lead to a fatal lightning incident. Furthermore, this study will examine the extent to which individuals were able to perceive the risks associated with their circumstances and the protective actions they undertook in response to the perceived threat. We will also examine the specific characteristics of events occurring in urban and rural environments, and to what extent particular behaviors, such as altruism, played a role.
During the period from 1900 to 1924, an annual average of more than 100 fatalities due to lightning strikes was recorded. In the comparison interval from 2000 to 2024, the total number of fatalities was 50, corresponding to an average of 2 deaths per year. In both periods, the majority of victims were male (65.5% and 68.8%, respectively). The German states with the highest cumulative numbers of lightning-related fatalities across both periods under investigation are North Rhine-Westphalia (397 deaths), Bavaria (355 deaths), and Baden-Württemberg (319 deaths). Comparative analysis of the two periods reveals significant contrasts in total fatality counts over the 25-year intervals, notable differences in the mean age of victims, and disparities in the proportions of fatalities associated with work versus leisure activities.
How to cite:
Kühne, T., Antonescu, B., Groenemeijer, P., Holzer, A., Púčik, T., and Strommer, G.: Comparative Analysis on Lightning Strike Fatalities in Germany: 1900–1924 and 2000–2024, 12th European Conference on Severe Storms, Utrecht, The Netherlands, 17–21 Nov 2025, ECSS2025-241, https://doi.org/10.5194/ecss2025-241, 2025.
Jenni Rauhala, Terhi K. Laurila, Antti Mäkelä, Jani Särkkä, and Ulpu Leijala
Meteotsunamis are tsunami-like waves caused by mesoscale atmospheric disturbances that induce a moving air pressure anomaly over water. The speed and direction of the disturbance and the shape and topography of the coastal waters influence the resonance that can increase the wave height. If the speed of the air pressure disturbance is close to the long wave phase speed in shallow water, the resonance increases the wave height. Meteotsunamis can be generated by a phenomenon such as a cold front, a thunderstorm or a squall line.
Finland is favorable location for meteotsunami formation because of shallow depths over Finnish coastal waters. A few studies of meteotsunami occurrence have been published in Finland. Pellikka et al. (2020) investigated summer period meteotsunamis on the Finnish side of the Gulf of Finland and Pellikka et al. (2022) made a classification of meteotsunami cases according to season by studying high-frequency sea level variations from 2004–2015 on the Finnish coast.
In this study, we analyze occurrence of meteotsunamis over the Finnish sea areas in 17 squall line situations. The radar-observed evolution of the squall lines is analyzed and their movement speeds and tracks are compared to the water depths. Observations from 13 tide gauges operated by the Finnish Meteorological Institute on the Finnish coast are used to analyze meteotsunami events. The motivation of this study is to increase understanding of the squall line speeds, track lengths and directions that may cause meteotsunamis at the Finnish sea areas. These findings may help us to forecast meteotsunami events in the future.
How to cite:
Rauhala, J., Laurila, T. K., Mäkelä, A., Särkkä, J., and Leijala, U.: Squall-line caused meteotsunamis over Finnish sea areas, 12th European Conference on Severe Storms, Utrecht, The Netherlands, 17–21 Nov 2025, ECSS2025-295, https://doi.org/10.5194/ecss2025-295, 2025.
Julia Keller, Jan Bondy, Vanessa Fundel, Ina Blumenstein-Weingartz, Olga Kiseleva, Maja Rüth, Stefan Wolff, Thomas Deutschländer, Stefanie Hollborn, Kathrin Feige, Felix Fundel, Andreas Lambert, Armin Rauthe-Schöch, Ute Badde, Manfred Bremicker, Norbert Demuth, Natalie Stahl-van Rooijen, and Joachim Stoermer
In recent years, several regions in Germany experienced devastating floods caused by heavy precipitation events, often associated with severe convective storms. To improve the prediction of such events and corresponding warning strategies, Deutscher Wetterdienst (DWD) is intensifying its collaboration with Germany’s flood forecasting authorities. DWD has significantly enhanced and diversified its forecasting strategies through a range of new model systems. With a focus on seamless and probabilistic prediction in combination with more frequent initializations, DWD’s novel Seamless Integrated Forecasting System (SINFONY) marks a major step forward in predicting severe summertime convective events and heavy precipitation within a lead time of minutes to approximately 12 hours. Additional advancements include the development of 500 m high-resolution NWP with ICON as well as the establishment of an AI center and the integration of AI-based forecasting methods– all together paving the way for next-generation weather forecast systems. Beyond the improvement of forecasting techniques, DWD is placing strong emphasis on collaboration and communication with regional flood forecasting centers, including coordinated outreach to local disaster management authorities. To that end, the joint “Co-Design Project” was launched in 2023 together with regional German Flood Forecasting Centers, aiming to strengthen the hydrometeorological value chain. It is part of DWD’s new binational research initiative “Italia–Deutschland science-4-services network in weather and climate (IDEA-S4S)”. The project focuses on identifying user needs, creating a shared knowledge base for forecast evaluation, developing tailored forecast and warning tools, and supporting decision-making in the face of diverse and uncertain weather predictions. Its four main activities include:
a user-oriented evaluation of DWD’s precipitation forecasts
the establishment of standardized hydrological verification and thereupon analysis of (new) DWD forecasts within operational flood forecasting models
the tailoring of DWD’s new warning system to meet requirements of flood forecasting centers
a serious game and E-Learning initiative to improve the communication along the entire warning chain of rainfall and flood forecasts for better decision-making.
This contribution provides an overview of the “Co-Design Project”. We will share first results and progress, and look forward to exchanging experiences with other initiatives at the operational intersection of meteorology and hydrology.
How to cite:
Keller, J., Bondy, J., Fundel, V., Blumenstein-Weingartz, I., Kiseleva, O., Rüth, M., Wolff, S., Deutschländer, T., Hollborn, S., Feige, K., Fundel, F., Lambert, A., Rauthe-Schöch, A., Badde, U., Bremicker, M., Demuth, N., Stahl-van Rooijen, N., and Stoermer, J.: Enhancing the collaboration and communication between weather and flood forecasting in Germany following a Co-Design approach, 12th European Conference on Severe Storms, Utrecht, The Netherlands, 17–21 Nov 2025, ECSS2025-303, https://doi.org/10.5194/ecss2025-303, 2025.
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Impact Forecasting (IF) is Aon’s Catastrophe Model development team. For several years, IF have been developing Automated Event Response (AER) services. These services aim to take either forecasts of upcoming events or observations immediately following events and use these to develop hazard footprints for input into operational catastrophe models. This allows for the rapid quantification of predicted losses from the event, either at market level or on individual clients’ exposures.
An AER service for windstorm in Europe has now been operational for several years and a service for hail is under development. Whilst there are not forecasts of hail that can be used for loss modelling, we are able to use observations following events.
In this work we demonstrate the use of two data sources for modelling hail losses. Firstly, the use of ESWD hail reports, and secondly the use of OPERA radar data alongside the ESWD reports. 2D radar reflectivity is combined with geopotential heights to derive the severe hail index and therefore the maximum estimated size of hail. The addition of the radar data allows for more accurate estimation of the area affected by hail and of the variation of hail size within the storm.
A brief overview of the operational IF European Hail model is also provided.
How to cite:
Brocklehurst, A., Mrekaj, I., and Braun, L.: Use of ESWD and OPERA radar data for the rapid estimation of insured losses following hail storms. , 12th European Conference on Severe Storms, Utrecht, The Netherlands, 17–21 Nov 2025, ECSS2025-304, https://doi.org/10.5194/ecss2025-304, 2025.
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Thunderstorms are known to have an significant impact on air traffic, but it highly depends on their organization. Clusters of thunderstorms for example, can enhance this impact, but well organised widespread convection can hardly reduce the air traffic, due to its high vertical extension, long life time and severity. Forecasting the type of storm is thus a real challenge in order to alert the Network Managers within the Air Traffic Control centres.
Mesoscale Convective Systems (MCS) such as derecho events are part of this organised convection. During the 25th of June 2025, a derecho-like event has triggered from the southwestern part of France and spread to the German borders, generating violent wind gusts between 110 and 135 km/h, damages and a fatality. The synoptic meteorological situation presented a highly wind sheared and unstable environment and a large scale lift ahead of a cold front, which tends to favor supercells or bow echoes organizations.
In order to assess the human-made operational forecast, we have developped an automatic tool based on differents observations data (radars and lightning). It provides a bulletin that can be directly compared to the human forecast, one can thus evaluate objectively its quality. Over the last 2024 convective season, the algorithm showed generally good results, but it needs to implement other observation data to improve convection detection (radars from other countries to cover mountains regions or satellite data to cover sea areas). Other types of skill scores can also be implemented.
How to cite:
Vallet, T. and Flouttard, A.: Derecho-like event over France: Observation and Evaluation of the Forecast using an Automatic Verification Tool, 12th European Conference on Severe Storms, Utrecht, The Netherlands, 17–21 Nov 2025, ECSS2025-325, https://doi.org/10.5194/ecss2025-325, 2025.
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