A high-resolution compound vulnerability function for severe convective storm losses

توفان‌های تندری نوعی از توفان است که عموماً با ابرهای همرفتی و معمولاً با سیلاب‌های لحظه‌ای و گاهی تگرگ و باد شدید همراه هستند. ابرهای مربوط به توفان‌های همرفتی در بیشتر مناطق مشاهده می‌شوند، اما درصد کوچکی از این توفان‌های همرفتی تولید شرایط هوای سخت و سیل‌های ناگهانی را می‌کنند و خسارات زیادی به بار می‌آورند. یکی از این توفان‌های تندری مرگ‌بار، توفان تندری 28 تیرماه 1394 است که دارای خسارات مالی و جانی فراوانی بود. در این پژوهش به بررسی شرایط سینوپتیکی و ترمودینامیکی این توفان تندری پرداخته شده است. هدف از انجام این پژوهش پیش‌بینی احتمال وقوع توفان تندری، تعیین شدت توفان احتمالی، تعیین مکان توفان همرفتی و ارتباط آن با سامانه‌های سینوپتیکی بوده است. در این راستا از داده‌های NCEP/NCAR، تصاویر ماهواره‌ای NOVA/AVHRR، داده‌های جو بالا و نرم‌افزارهای GRADS، ENVI، RAOB و ArcGIS برای رسیدن به اهداف فوق استفاده شد. نتایج این پژوهش نشان داد که شرایط سینوپتیکی مساعد برای وقوع توفان تندری ازجمله کم‌فشار تراز دریا، ناوه تراز میانی، همگرایی رطوبت و وجود رطوبت در لایه‌های پایینی جو وجود دارد. همچنین هسته اصلی توفان که بین کرج و قزوین قرار دارد با مرکز بیشینه امگای منفی تراز 500 هکتوپاسکال منطبق است. نتایج شاخص‌های ناپایداری برای ساعت 00UTC نشان داد که شاخص‌های KO، KI، JI و VT شدت ناپایداری را قوی و توفان همرفتی شدید را پیش‌بینی کرده‌اند. 6 شاخص نیز ناپایداری(توفان همرفتی) متوسط و فقط دو شاخص توفان همرفتی ضعیف را پیش‌بینی کرده‌اند. نرم‌افزار RAOB حداکثر سرعت قائم در این ساعت را 30 متر بر ثانیه برآورد کرده است که نشان‌دهنده صعود شدید و درنتیجه وقوع توفان تندری شدید است.

Hail is by far the greatest contributor worldwide to insured losses from severe convective storms on an annual basis. Individual outbreaks can cause losses well above EUR 1 bn. In Italy, severe convective storm losses have been dominating the market in the last 5-7 years, with a record of EUR 1.4 bn in 2019 prior to year 2023. On 18-25 July 2023 an unprecedented outbreak brought large hail and strong winds to Lombardy, Veneto, Friuli-Venezia Giulia, Piedmont and Emilia-Romagna, with affected cities including Parma, Turin, Milan and Venice. There were many reports of large hailstones, causing significant damage to property and motor vehicle. The European hail record was breached too. Twice. On 19 July, a hailstone measuring 16 cm in diameter was recorded in Carmignano di Brenta, and broke the previous largest hail record in Europe, which was held by a 15 cm stone found in Romania in 2016. Just five days later, a new record was set, when a 19 cm hailstone was found in the town of Azzano Decimo. This is very close to the all-time largest hail recorded of 20.3 cm, found in 2010 in South Dakota, US. Total loss estimates, of which hail was by far the largest contributor, exceeds EUR 3 bn, of which 70-80% in the property sector (residential and commercial buildings), and the remaining 20-30% in the motor vehicle sector. These were the largest hail events in Italy in recorded history, and the costliest cat event in the third quarter of 2023 for the global insurance market.Following in the footsteps of the severe convective storm outbreak that impacted France in June 2022, these storms came after a record-hot air mass that languished over Southern Europe much of the week prior. Persistent meteorological conditions conducive to rotating supercell thunderstorms were observed for several consecutive days. These compounded with local conditions favorable for the development of severe hail over the Po Valley. In this study we present a reconstruction of these events based on event reports from European Severe Weather Database. We analyze the synoptic configurations and pre-convective environments that characterized them, with focus on those properties and features that are peculiar to severe hail-forming thunderstorms. We look at different formulations of CAPE and vertical wind shear, as well as composite parameters such as the Significant Hail Parameter and the Supercell Composite Parameter. We make use of Gallagher Re’s Severe Convective Storm Index to contextualize these events historically, and to discuss climate change trends over Northern Italy. Finally, we discuss the implications that such events and their expected frequency under climate change have on the (re)insurance market.

The effects of severe convective storms (SCS) are experienced by much of the world, but whether communities can recover from the impacts depends upon their resilience – the capacity to prepare and mitigate. Hail is the costliest SCS hazard, accounting for 70% of losses. Swiss Re found that SCS accounted for “record high” global loss in 2023 ($64 billion), mostly originating from the U.S., but growing fastest in Europe. In the U.S. in 2023 (2024) there were 28 (27) “Billion Dollar Disasters”. 19 (17) of were caused by SCS. The (re)insurance industry relies on hazard models to statistically estimate risk. These models simulate risk based on long-term data records of weather hazard occurrences. Over much of the world, there are uncertainties with SCS risk assessment, primarily due to a lack of human spotter reports and/or insufficient weather radar climate data records.SCS are maintained by strong updrafts that create unique patterns at their cloud tops: overshooting tops and above anvil cirrus plumes. Though overshooting tops do not guarantee that extremely severe weather will occur, most severe storms are found to generate them. The distinct benefits of geostationary (GEO) satellites are their long-term observational data records, hemispheric spatial coverage, and high spatio-temporal frequency sampling. Reliable methods for overshooting top detection have been demonstrated, and long-term detection datasets have been previously used for SCS risk assessment. GEO observations can provide unique, long-term insights into SCS intensity, frequency, spatio-temporal distribution, and trends that support development of SCS hazard models, complementing alternative methods derived from reanalyses, weather radar, and human reporting.In this NASA Applied Sciences Disasters Program project, we employ an automated convective storm and overshooting top detection method, based on GEO infrared imagery and reanalysis model inputs. This method supports the development of SCS hazard models through creation of multi-decadal data records over Europe and North America. In partnership with Willis Towers Watson (WTW), Gallagher Re, the Karlsruhe Institute of Technology (KIT), and the European Severe Storm Laboratory, we seek to better inform the public and industry about SCS exposure and risk. This presentation will highlight analyses of a recently developed 21-year duration convective storm and overshooting top data record over Europe derived from 15-min MSG SEVIRI infrared imagery on a 4 km grid. It will also highlight comparisons between the SEVIRI data record and the European Severe Weather Database to evaluate regional severe storm detection performance and satellite-based storm intensity estimation.

Severe convective wind storms are responsible for billions of dollars in damage to global infrastructure each year. This suggests there are systematic deficiencies in the way that structures are designed to withstand this type of event and that there is a need to better understand the hazard convective wind storms pose to infrastructure. In order to quantify this risk, it is necessary to have a reliable and spatially complete climatology of their occurrence. Given no such climatology exists for Australia, this research sought to develop such a climatology based on recent observational records, as well as examining how climate change may impact the severe convective wind storm climate into the future.Severe weather records (including wind gusts) in the Australian Bureau of Meteorology’s Severe Storm Archive are spatially and temporally incomplete and are therefore inadequate for developing a reliable climatology. In contrast, approximately 600 Automatic Weather Stations (AWS) around the country now produce 1-minute data records for several atmospheric variables (including wind speed and direction). These records offer a source of data that can be reliably analysed. However, analysis of these data presents challenges, primarily including, a) identifying the weather mode (e.g., convective, frontal, strong pressure gradients, pressure systems) responsible for each extreme gust, and b) overcoming the incomplete spatial coverage of the network. To overcome these challenges, this work utilises Self-Organizing Maps (SOM) as an automated method for classifying the mode of each observed gust, and then applies hierarchical Bayesian statistics to extend the analysis to regions where no AWS records exist.With the increasing use and acceptance of SOM algorithms for classifying atmospheric data, this machine learning technique was used to objectively identify severe wind storms from 1-minutre AWS data. The SOM algorithm was applied to a small subset of AWS stations so that the SOMs could be trained, and their performance verified. Given the large number of free parameters built into the SOM algorithm, it was first essential to conduct a proper sensitivity analysis to determine the set up for the SOM. Upon selecting the best combination of free parameters to run the SOMs, different combinations of atmospheric variables were explored, including: wind speed, change in wind direction, temperature, mean sea level pressure, precipitation and equivalent potential temperature. Various statistical tools were used to determine how well the SOM algorithm was able to identify convective events compared to a manual identification of events. It was found that by considering wind speed alone, the SOM was able to perform well compared to methods that combine other variables such as temperature, pressure, and change in wind direction.To extend this station-based analysis and facilitate the development of a spatially complete convective wind storm climatology across the Australian continent, observational and global reanalysis data are coupled to determine the probability of severe wind storms occurring in different parts of Australia, even where there is no observational data available. A Bayesian hierarchical framework was used to develop the relationship between the SOM identified AWS convective events and severe weather indices calculated from ERA-Interim reanalysis data, while minimizing the impact of the spatial and temporal biases inherit to the AWS data. Using this model, the expected number of severe wind storms occurring in all parts of Australia was estimated. The Bayesian model was run using data between 2005-2015 and showed that there are significantly more severe convective wind storms occurring in northern Western Australia, southern Northern Territory and western Queensland than observational datasets show. Resampling techniques minimised the effects of the short observational period and helped determine the index or indices that best relates the observational and reanalysis data. These relationships were then used to extend the length of the observed dataset over the entire ERA-Interim reanalysis period (1979-2015).The flexibility of the Bayesian Hierarchal model allows the ERA-Interim reanalysis data to be replaced by other global datasets, including global climate models. Here, the Bayesian model is run with CMIP5 data to estimate how the climatology of severe convective wind storms might change under different climate scenarios. The BOM-CSIRO ACCESS-CM 1.3 under the RCP8.5 scenario was used. Mean severe weather indices were calculated for the projection period of 2090-2100 and the historical period from 1990-2000. Using this global model input for the Bayesian model, the change in the severe convective wind storm event counts from 1990-2000 to 2090-2100 were examined. To understand potential changes to convective wind storm hazard under an Intergovernmental Panel on Climate Change (IPCC) climate change scenario, large-scale global climate model environmental parameters (i.e. CAPE, Wind Shear) used in the stochastic model to estimate convective wind storm frequency were studied. Running the stochastic model with these “changed” environments showed that an increase in the number of severe convective wind storms can be expected during the spring, summer, and autumn, especially over northern Western Australia, and Queensland. This resulting data can be used to generate hazard maps and stochastic event sets to inform wind-resistant design standards and facilitate risk-based decision making by government and industry.

<p>The European Storm Forecast Experiment (ESTOFEX) is a team of volunteer forecasters that have been providing experimental convective outlooks for Europe since 2002. Probabilistic storm forecasts issued by ESTOFEX address threats posed by severe convective storms, i.e. lightning, large hail, severe wind gusts, tornadoes and excessive precipitation. ESTOFEX also serves as a platform for exchange of knowledge about forecasting severe convective storms with a goal of improving their understanding among both members of ESTOFEX and others. While not official, ESTOFEX products have been widely used by national meteorological services, severe storm communities and the public. ESTOFEX forecasters have regularly contributed to the ESSL Testbeds and are using an ingredients based forecasting methodology to forecast severe storms. Consistently improving severe storm reporting in the European Severe Weather Database (ESWD) and availability of ground-based lightning detection measurements over the last decade enabled the verification of a large number of ESTOFEX forecasts. Thus, in this work we evaluate 4019 convective outlooks issued by ESTOFEX forecasters since 2007. Our goals are to detect spatiotemporal patterns in convective outlooks and test the reliability of issued threat level polygons, i.e. for a low and high probability of lightning, and an increasing probabilities of severe weather: level 1, level 2 and level 3. We performed the verification by applying a number of methods, including contingency table statistics, receiver operating characteristic curves, practically perfect hindcasts and by calculating spatial coverage of detected lightning (ATDnet network) and local storm reports (ESWD) within issued polygons. Results indicate that products issued by ESTOFEX over the last 15 years, when combined together, are consistent with convective climatologies based on reanalyses and lightning detection data. However, we note that forecasters tend to issue outlooks relatively more often for severe weather outbreaks across western and central Europe. We found that while 95% of the issued lightning probability areas fulfilled the required criterion of coverage, this was only true for 40% of the severe weather probability areas. One reason is that while lightning observations are relatively homogeneous across the forecast domain, the same cannot be said about severe weather observations. These are lacking in regions such as southeastern or eastern Europe, while forecasters calibrated themselves to the higher observed coverage in western and central Europe. The reliability of ESTOFEX forecasts increased over the time, but we found underestimation of lightning probabilities over southern Europe and an overestimation of lightning probabilities over British Isles and Scandinavia.</p>

Surface weather stations are a critical technology for understanding severe convective storms due to their unique capabilities. While weather radar effectively detects precipitation and wind patterns aloft, its limitation is its inability to capture crucial surface-level weather where it matters the most for people and property. Documenting and analyzing such events offer invaluable insights into their causes, impacts, and potential future occurrences.Existing surface weather stations in Canada frequently face several known limitations that impede their effectiveness, especially when it comes to severe convective storms. In general, Canadian surface weather stations are widely spaced which often fail to capture highly localized severe convective storms. Additionally, the operation of these stations is managed by different federal and provincial agencies, which makes it difficult to utilize existing surface weather stations for nowcasting severe convective storms. Furthermore, it poses challenges in collecting data after an event, as the ease of access to this data can vary greatly across these different agencies.This is the reason for the creation of Northern Mesonet Project (NMP), a new program under the Canadian Severe Storms Laboratory, which aims to better monitor severe convective storms by increasing the spatial density of real-time advanced weather observations and enhancing data availability & quality for severe weather analysis and prediction. This presentation will specifically highlight the Canadian Mesonet Portal, a central repository and access point established by NMP to address some of the limitations faced by Canadian surface weather stations. By connecting over 30 individual surface weather station networks, the Canadian Mesonet Portal provides a unified platform for accessing over 2800 publicly available surface weather observations across Canada. This unified platform allows for better nowcasting of severe convective storms in Canada, and for better analysis of the damage caused by severe convective storms.

Observational data of thunderstorm hazards, such as hail reports in the European Severe Weather Database, suggest that severe convective storms are especially frequent over the surrounding slopes of mountain ranges. The same geographic regions are also projected to experience the strongest increases in severe weather occurrence as a result of global warming. Given the potential high impact of intensifying severe convection in these densely populated parts of Europe (and other regions of the world), it is critical to better understand where and why these storms become more severe. While a number of field campaigns has investigated either terrain effects on the atmospheric boundary layer or severe storm dynamics away from terrain, their topical overlap, severe convection dynamics near terrain, is less researched.For these reasons, ESSL is currently coordinating efforts to plan an international field campaign on severe convective storms surrounding the Alps and lower mountain ranges in central Europe from the Pyrenees to the West to the Tatras mountains to the East. A network of over 20 partner institutions across these countries has already been engaged. The research focus is on high-resolution data collection of the pre-convective environment and within individual severe storms. However, the field campaign will also provide wider opportunities to validate recent innovations in forecasting, nowcasting, and measurements, such as new instruments on the Meteosat Third Generation satellite, numerical model parametrizations of terrain-related processes, drone-based surveys and profiling, and polarimetric radar algorithms. This presentation will give a short review of the scientific literature on the topic of severe convective storms near complex terrain and summarize the most important research questions. Based on that, the current plans for the TIM field campaign are presented to invite collaborations from the EMS community.

The European Severe Storms Laboratory (ESSL) was founded in 2006 to advance the science and forecasting of severe convective storms in Europe. ESSL was a grassroots effort of individual scientists from various European countries. The purpose of this article is to describe the 10-yr history of ESSL and present a sampling of its successful activities. Specifically, ESSL developed and manages the only multinational database of severe weather reports in Europe: the European Severe Weather Database (ESWD). Despite efforts to eliminate biases, the ESWD still suffers from spatial inhomogeneities in data collection, which motivates ESSL’s research into modeling climatologies by combining ESWD data with reanalysis data. ESSL also established its ESSL Testbed to evaluate developmental forecast products and to provide training to forecasters. The testbed is organized in close collaboration with several of Europe’s national weather services. In addition, ESSL serves a central role among the European scientific and forecast communities for convective storms, specifically through its training activities and the series of European Conferences on Severe Storms. Finally, ESSL conducts wind and tornado damage assessments, highlighted by its recent survey of a violent tornado in northern Italy.

Data from the C-band weather radar located in central Estonia in conjunction with the latest reanalysis of the European Centre for Medium-Range Weather Forecasts (ECMWF), ERA5, and Nordic Lightning Information System (NORDLIS) lightning location system data are used to investigate the climatology of convective storms for nine summer periods (2010–2019, 2017 excluded). First, an automated 35-dBZ reflectivity threshold-based storm area detection algorithm is used to derive initial individual convective cells from the base level radar reflectivity. Those detected cells are used as a basis combined with convective available potential energy (CAPE) values from ERA5 reanalysis to find thresholds for a severe convective storm in Estonia. A severe convective storm is defined as an area with radar reflectivity at least 51 dBZ and CAPE at least 80 J/kg. Verification of those severe convective storm areas with lightning data reveals a good correlation on various temporal scales from hourly to yearly distributions. The probability of a severe convective storm day in the study area during the summer period is 45%, and the probability of a thunderstorm day is 54%. Jenkinson Collison’ circulation types are calculated from ERA5 reanalysis to find the probability of a severe convective storm depending on the circulation direction and the representativeness of the investigated period by comparing it against 1979–2019. The prevailing airflow direction is from SW and W, whereas the probability of the convective storm to be severe is in the case of SE and S airflow. Finally, the spatial distribution of the severe convective storms shows that the yearly mean number of severe convective days for the 100 km2 grid cell is mostly between 3 and 8 in the distance up to 150 km from radar. Severe convective storms are most frequent in W and SW parts of continental Estonia.

Convective storms are the result of a disequilibrium created by solar heating in the presence of abundant low-level moisture, resulting in the development of buoyancy in ascending air. Buoyancy typically is measured by the Convective Available Potential Energy (CAPE) associated with air parcels. When CAPE is present in an environment with strong vertical wind shear (winds changing speed and/or direction with height), convective storms become increasingly organized and more likely to produce hazardous weather: strong winds, large hail, heavy precipitation, and tornadoes. Because of their associated hazards and their impact on society, in some nations (notably, the United States), there arose a need to have forecasts of convective storms. Pre-20th-century efforts to forecast the weather were hampered by a lack of timely weather observations and by the mathematical impossibility of direct solution of the equations governing the weather. The first severe convective storm forecaster was J. P. Finley, who was an Army officer, and he was ordered to cease his efforts at forecasting in 1887. Some Europeans like Alfred Wegener studied tornadoes as a research topic, but there was no effort to develop convective storm forecasting. World War II aircraft observations led to the recognition of limited storm science in the topic of convective storms, leading to a research program called the Thunderstorm Product that concentrated diverse observing systems to learn more about the structure and evolution of convective storms. Two Air Force officers, E. J. Fawbush and R. C. Miller, issued the first tornado forecasts in the modern era, and by 1953 the U.S. Weather Bureau formed a Severe Local Storms forecasting unit (SELS, now designated the Storm Prediction Center of the National Weather Service). From the outset of the forecasting efforts, it was evident that more convective storm research was needed. SELS had an affiliated research unit called the National Severe Storms Project, which became the National Severe Storms Laboratory in 1963. Thus, research and operational forecasting have been partners from the outset of the forecasting efforts in the United States—with major scientific contributions from the late T. T. Fujita (originally from Japan), K. A. Browning (from the United Kingdom), R. A. Maddox, J. M. Fritsch, C. F. Chappell, J. B. Klemp, L. R. Lemon, R. B. Wilhelmson, R. Rotunno, M. Weisman, and numerous others. This has resulted in the growth of considerable scientific understanding about convective storms, feeding back into the improvement in convective storm forecasting since it began in the modern era. In Europe, interest in both convective storm forecasting and research has produced a European Severe Storms Laboratory and an experimental severe convective storm forecasting group. The development of computers in World War II created the ability to make numerical simulations of convective storms and numerical weather forecast models. These have been major elements in the growth of both understanding and forecast accuracy. This will continue indefinitely.

  Recently, the increase in convective storms that develop rapidly within a short period of time and on a very small area causes severe damage to property and human life. Thus, it is important to understand the characteristics of convective activities and to provide the information about severity of the developing storms.  In order to address these issues, object-based analysis of convective systems is essential to provide severity information on convective precipitation systems including their life-cycle from initiation to dissipation.   In this study, we analyzed the developing stage of convective storms by using the statistics of storms detected by the Fuzzy Logic Algorithm for Storm Tracking (FAST). The Column Maximum (CMAX) was used to provide the information on detection and severity of storms. A convective storm was defined as a CMAX values above 35dBZ and small convective cells with an area less than 20km2 were filtered out. The identified storm was tracked on a fuzzy basis using storm speed and its morphological characteristics. Within the detected storm area, we analyzed the characteristics of the storm by averaging variables such as reflectivity (ZH), echo top height corresponding to ZH, rainfall rate at 1.5km altitude, VIL (Vertical Integrated Liquid) contents, etc.  This study aims to provide quantitative information on severity of individual storms by using these radar variables and storm characteristics. We calculated and modified the threshold values of each predictor for determining the severity of the convective storms. Furthermore, we plan to analyze the intensity and frequency of severe precipitation storms in associated with the occurrence or absence of lightning event during their life cycle.Key words : Weather Radar, convective storms, Radar parameter, storm severity※ This research was supported by the "Development of radar based severe weather monitoring technology (KMA2021-03121)" of "Development of integrated application technology for Korea weather radar" project funded by the Weather 

Over the last decades, the NOAA Storm Prediction Center has developed a unique technique of predicting severe convective storm hazards by issuing so-called convective outlooks. Over time, those products have gained recognition and proved to be an effective tool in informing the general public about possible hazards associated with severe convective storms. Following the SPC idea, similar solutions have also been used in other regions of the world (e.g., ESTOFEX, PREVOTS). SPC outlooks translate the probability of occurrence of large hail, severe wind and tornadoes over the period of 24h into specific risk level categories, which since 2014 involve: (level 0) thunderstorm, (level 1) marginal, (level 2) slight, (level 3) enhanced, (level 4) moderate, and (level 5) high. In this work, we employ ERA5 reanalysis data between 1960 and 2024 (3h steps at 0.25 deg grid), and ASTORP models (Automated Severe Thunderstorm Outlooks from the thundeR Package) to construct a preliminary global climatology and trends of convective hazard probabilities corresponding to specific SPC risk categories. By doing this, we want to address two main aspects. Globally, the United States has the most comprehensive severe storm dataset, which may suggest that environments favoring extreme storms are the most frequent in this country. First, we test this hypothesis by comparing the modeled frequency of SPC risk categories between different parts of the world to determine how globally unique severe storm environments are in the United States. Second, we explore how the frequency of modeled SPC risk categories and specific severe storm hazards changed over time on the global scale, also in the context of a warming climate and with a special focus on densely populated areas. Our preliminary results indicate that while a frequency of situations resulting in non-severe or marginally severe thunderstorms has decreased over time, we observed an increase in the environments associated with particularly large probabilities for the occurrence of severe and significant severe convective hazards.

Recent climate studies often predict the occurrence of extreme weather or climate conditions under certain global warming scenarios using climate models. However, it is usually unclear about the nature of such extreme weather and how such weather extremities occur as the resolution of the current generation climate models is usually not high enough to resolve individual storm systems let alone pinning down their physical mechanisms. This ambiguity in physical mechanism impedes the better understanding of the nature of these extreme weather/climate events and can lead to ineffective mitigation and/or adaptation measures. For example, when the term extreme rainfall is mentioned, it is unclear whether it is caused by severe convective storms or by regular storms that have higher liquid water contents (LWC), as both can lead to large amount of rainfall. But the detailed physical mechanisms of these two types of storms are different. Clearly it is desirable to remove such ambiguity and clarify what type of storms would occur in certain climate regime. In this study, we utilize the meteorological series derived from the REACHES climate database compiled from Chinese historical documents (Wang et al., 2018, Sci. Data 8:180288) as well modern weather data to pin down the type of storms and to study the respective physical mechanisms responsible for the extreme events that preferably occur in cold versus warm climate regime. We construct convection index series based on hailfall and lightning records in China in REACHES database for the period of 1368-1911 and compare them with the reconstructed temperature series from the same database for the same period (Wang et al., 2024, Sci. Data, 11, 1117). The comparison will reveal that severer convective storms tend to occur more frequently in cold climate regime than warm climate regime. On the other hand, modern observational data demonstrate that the high LWC (but not necessarily severe) storms are the type most likely to lead to extreme events in the present warming climate.Finally, storm thermodynamics and dynamics will be used to explain why such differences occur in different climate regimes.

Severe convection occurring in high-shear, low-CAPE (HSLC) environments is a common cool-season threat in the southeastern United States. Previous studies of HSLC convection document the increased operational challenges that these environments present compared to their high-CAPE counterparts, corresponding to higher false-alarm ratios and lower probability of detection for severe watches and warnings. These environments can exhibit rapid destabilization in the hours prior to convection, sometimes associated with the release of potential instability. Here, we use self-organizing maps (SOMs) to objectively identify environmental patterns accompanying HSLC cool-season severe events and associate them with variations in severe weather frequency and distribution. Large-scale patterns exhibit modest variation within the HSLC subclass, featuring strong surface cyclones accompanied by vigorous upper-tropospheric troughs and northward-extending regions of instability, consistent with prior studies. In most patterns, severe weather occurs immediately ahead of a cold front. Other convective ingredients, such as lower-tropospheric vertical wind shear, near-surface equivalent potential temperature (θe) advection, and the release of potential instability, varied more significantly across patterns. No single variable used to train SOMs consistently demonstrated differences in the distribution of severe weather occurrence across patterns. Comparison of SOMs based on upper and lower quartiles of severe occurrence demonstrated that the release of potential instability was most consistently associated with higher-impact events in comparison to other convective ingredients. Overall, we find that previously developed HSLC composite parameters reasonably identify high-impact HSLC events. Significance Statement Even when atmospheric instability is not optimal for severe convective storms, in some situations they can still occur, presenting increased challenges to forecasters. These marginal environments may occur at night or during the cool season, when people are less attuned to severe weather threats. Here, we use a sorting algorithm to classify different weather patterns accompanying such storms, and we distinguish which specific patterns and weather system features are most strongly associated with severe storms. Our goals are to increase situational awareness for forecasters and to improve understanding of the processes leading to severe convection in marginal environments.

The Canadian Severe Storms Laboratory (CSSL) was launched at Western University in October of 2024 with the aim of being the authority for severe convective storm (SCS) data and research in Canada. The CSSL will advance the detection, documentation and understanding of SCS and their impacts across the country.The CSSL’s mission is currently driven by three key projects: the Northern Tornadoes Project, the Northern Hail Project, and the Northern Mesonet Project. The Northern Tornadoes Project aims to improve tornado, downburst and derecho detection and documentation across Canada, utilizing aerial and ground surveys, satellite imagery and advanced research methods to improve the Canadian climatology and analyze trends.The Northern Hail Project focuses on understanding hailstorm frequency, intensity, and impacts, leveraging radar observations, hail collection and damage assessments to better characterize hail hazards.The Northern Mesonet Project supports these initiatives by increasing the spatial density of real-time advanced weather observations, enhancing data availability, and improving data quality for SCS analysis and prediction.It is anticipated that an additional project, focused on flash flooding hazards related to SCS, will be launched under the CSSL banner in the coming years. Some work has already begun in that area.The CSSL also provides unique training opportunities through its graduate student and internship programs. These programs aim to cultivate the next generation of SCS researchers by offering hands-on experience with in-field data collection, techniques development and applied research.This presentation will outline the strategic framework and technological advancements that underpin the CSSL’s operations and research. It will also showcase the latest findings from each project and explore future directions.