1. Introduction
Many studies have recognized southern South America (south of 15°S; Fig. 1) as a worldwide hotspot for strong deep moist convection (e.g., Zipser et al. 2006; Romatschke and Houze 2010; Rasmussen and Houze 2011; Rasmussen et al. 2014; Prein and Holland 2018; Ribeiro and Bosart 2018; Bruick et al. 2019; Zhou et al. 2021). Environmental conditions here often favor the development of organized storms capable of producing severe weather hazards (i.e., hail, strong wind gusts, and tornadoes). Several studies have analyzed the occurrence of severe storms in Argentina (Altinger de Schwarzkopf 1988; Matsudo and Salio 2011; Mezher et al. 2012; Kumjian et al. 2020; Trapp et al. 2020; Borque et al. 2020; Bechis et al. 2022; Veloso-Aguila et al. 2024), Brazil (Silva Dias 2011; Nascimento et al. 2014; Martins et al. 2017; Figueiredo et al. 2019; Ribeiro et al. 2019; Beal et al. 2020; Ferreira et al. 2022; Oliveira et al. 2022; dos Santos et al. 2023), Chile (Vicencio et al. 2021; Marín et al. 2021; Barrett et al. 2020), and Uruguay (Durañona et al. 2019) based on ground reports of large hail, damaging winds, tornadoes, and heavy precipitating events, but in general, the information available has been limited and analyses have focused only on an individual country or localized areas.
Map of the region of interest analyzed in the present paper. Shading indicates the South America’s population density from the Gridded Population of the World, version 4 (GPWv4)-2020 dataset (Center for International Earth Science Information Network (CIESIN), Columbia University, 2018). In gray contour is the 1000-m topography from ETOPO-2022 (NOAA/National Centers for Environmental Information, 2022). Blue boxes indicate the domain of the figures shown in the corresponding sidebars, see the “No borders: A widespread severe weather event” sidebar (Fig. SB1), Figs. SB2a and SB2b corresponding to the “Encouraging individuals to accurately report natural hazards: Giant hail” sidebar, and “Tornadoes: The need for damage surveys” sidebar (Fig. SB3).
Citation: Bulletin of the American Meteorological Society 105, 7; 10.1175/BAMS-D-23-0063.1
In the last few years, as the recording and cataloging of hazards and their socioenvironmental impacts have become a key element in the understanding of disaster risk, various initiatives have begun to catalog severe weather reports in different regions. Among successful initiatives can be mentioned the Severe Weather Database from National Centers for Environmental Information at National Oceanic and Atmospheric Administration (NOAA) in the United States, with reports since 1955 (www.ncdc.noaa.gov/IPS/sd/sd.html); the European Severe Weather Database (ESWD; Dotzek et al. 2009), that consists of reports from all European countries since the early 2000s, as well as historical reports, some dating back centuries; and Northern Tornadoes Project in Canada focused on detecting and accurately assessing tornado occurrence (Sills et al. 2020), among others. One example in Brazil is the initiative called Storm Spotting Network and Platform for Severe Weather Reports [Plataforma de Registros e Rede Voluntaria de Observadores de Tempestades Severas (PREVOTS); x.com: @prevots_svr], which collects severe weather reports. Another example was implemented in Argentina during the Remote sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign (Nesbitt et al. 2021) where reports and their socioenvironmental impacts were collected during the warm season 2018/19 in central Argentina by a group of students (x.com: @RELAMPAGO_edu). While these represent important initiatives to collect the reports of hazardous weather events and their socioenvironmental impacts, they are not part of a fully coordinated (nor standardized) effort to produce a true routinely updated database. The availability of a high-quality South American high-impact weather (HIW) report database is essential for a large number of applications, such as (i) understanding the atmospheric environments that lead the development, maintenance, and decay of these events (e.g., Mezher et al. 2012; Lopes and Nascimento 2024); (ii) evaluating and developing useful remote sensing proxies of intense convective storms and their associated hazards (e.g., Bang and Cecil 2019; Ribeiro et al. 2019; Piscitelli et al. 2022); (iii) selecting environmental parameters for ingredients-based analysis of the meteorological situations favorable for these events (e.g., Brooks et al. 2003; Prein and Holland 2018; Piscitelli et al. 2022; Glazer et al. 2021; Taszarek et al. 2021; dos Santos et al. 2023); (iv) training artificial intelligence algorithms (e.g., McGovern et al. 2023); (v) forecast verification of convective hazards (e.g., Tsonevsky et al. 2018; Barras et al. 2019; Marsigli et al. 2021); (vi) quantitative analysis of social and economic impacts of HIW events (e.g., Púčik et al. 2019); and (vii) climate change baselines and trends (Groenemeijer et al. 2017).
An exemplary initiative is the “Early Warnings for All” initiative, spearheaded by the World Meteorological Organization (WMO 2022). This groundbreaking effort is focused on delivering advance warnings for a range of natural hazards, including heatwaves, storms, floods, and tsunamis. Numerous technology companies and governmental agencies have expressed keen interest in this undertaking, driven by the application of a broad spectrum of techniques, including advanced artificial intelligence models. However, what potential biases might arise in the calibration, validation, and/or training of such systems when local ground truth information is scarce? What are the consequences on population and infrastructure due to incorrect forecast calibration and evaluation only possible on regions with data availability?
No borders: A widespread severe weather event
Some extreme events often account for a large number of reports on a single day. On 30 June 2020, a quasi-linear convective system (QLCS) caused hundreds of reports in southern Brazil, northeastern Argentina, and southern Paraguay (Fig. SB1). This event can be classified as a serial derecho (Johns and Hirt 1987) based on the wind reports. This QLCS occurred in an environment with strong synoptic-scale forcing for ascent, very intense wind shear, and was followed by explosive cyclogenesis off the coast of southern Brazil. This event underlines the critical importance of having an integrated severe storm reports database in order to effectively characterize large-scale convective systems that traverse international borders.
Reports from Brazil, Argentina, and Paraguay between 0400 UTC 30 Jun and 0000 UTC 1 Jul 2020. Most reports were caused by a quasi-linear convective system. There were 592 wind reports (5 measured wind gusts > 120 km h−1), 91 hail reports (5 reports of hail > 5 cm size), and one tornado report. Gray areas denote the approximate location of the 50-dBZ area every 2 h based on radar imagery.
Citation: Bulletin of the American Meteorological Society 105, 7; 10.1175/BAMS-D-23-0063.1
When users seek weather-related information, their primary concern often revolves around how the weather conditions will influence their day-to-day activities. For example, if a storm is forecast, they may want to know if there will be flooding, structural damage, power outages, etc. In these cases, the simple forecast of the storm becomes less relevant, as users are more concerned with the potential impact on their lives and property. Only recently have weather services turned their attention to this viewpoint, constituting a paradigm shift that requires a thorough understanding of the close and complex relationship between meteorological events and their associated impacts, taking into account the diversity of effects depending on location (Potter et al. 2018; Potter et al. 2021). Coordinated and methodical efforts to document this knowledge represent the most effective approach to uncovering the connections between significant meteorological events and their resulting socioenvironmental and economic consequences. As a necessary complement to this multihazard-impact-based forecasting approach, the WMO advocates for the establishment of records of meteorological events alongside their corresponding impacts (WMO 2020). The recording of hydrometeorological events accompanied by socioenvironmental implications has garnered substantial attention, with the DesInventar (desinventar.net) platform emerging as a pivotal source of information, endorsed by the United Nations Office for Disaster Risk Reduction since 1993. Serving as a central repository, it facilitates the gathering and scrutiny of data concerning a wide spectrum of natural hazards in tandem with their impact effects, primarily within the South American region, but this database is principally focused on widespread HIW events and not regularly updated.
The current lack of a unified database in South America remains an obstacle to improving our understanding of and building resilience toward HIW events, and in particular, those associated with deep moist convection. To tackle this problem, researchers, national weather service members, and weather enthusiasts from Argentina, Brazil, Chile, Paraguay, and Uruguay have embarked on a multinational collaboration to generate a standardized database of HIW reports and their impacts that is unprecedented in South America. This article presents the South American Meteorological Hazards and their Impacts (SAMHI) Database (https://samhi.cima.fcen.uba.ar/), an effort based on long-lasting collaborations and friendships initiated during experimental field campaigns such as South American Low-Level Jet Experiment (SALLJEX) (Vera et al. 2006), Cloud Processes of the Main Precipitation Systems in Brazil: A Contribution to Cloud-Resolving Modeling and to the GPM (CHUVA) (Machado et al. 2014), and RELAMPAGO–Cloud, Aerosol, and Complex Terrain Interactions (RELAMPAGO-CACTI) (Nesbitt et al. 2021; Varble et al. 2021). For many years now, this community has recognized the need to unite and converge efforts, since HIW events know no borders. Building climatologies or analyzing case studies from the perspective of each individual country is insufficient to provide an integral understanding of HIW events. Following the expansion of the operational meteorological radar networks in the region, along with the launch of a new Geostationary Operational Environmental Satellites generation, the local weather community is presented with the significant challenge to effectively manage this information and translate it into multihazard-impact-based forecasting and warning products.
Encouraging individuals to accurately report natural hazards: Giant hail
Severe hail events (diameter larger than 2 cm) are frequent in South America. Their magnitudes are illustrated by two cases shown in Fig. SB2. On 22 February 2019 (left), the database holds several reports along the track of a storm as it moved northeast toward Córdoba, Argentina, as evidenced by the progression of reports and their associated pictures (Fig. SB2). On 27 January 2023 (right), one of the largest hail diameters was reported in the SAMHI database in Villa Mercedes (San Luis, Argentina). This storm resulted in one fatality, numerous injuries, and substantial infrastructure damage, amplifying its impact as it coincided with a well-attended summer festival (see Figs. SB2d–f). Table SB1 shows some of the data fields that were recorded into the databases that are relevant for these two hail events to illustrate what the database looks like. For both cases, the photographic evidence from social media and digital newspapers allowed the determination of the location and time of the event, and to estimate the corresponding maximum hail dimensions. Both these cases rank among the reports with the largest hail dimensions (>10 cm). The maximum hail size was estimated considering the standard size of an adult human hand but the absence of other reference objects or a ruler introduces uncertainty in the hail diameter estimation. Providing training in simple reporting techniques to weather enthusiasts, emergency managers, and the general public can be the decisive factor in transforming a mere crowd-sourced post into valuable data for numerous tools and applications.
GOES-16 ABI 10.3-μm brightness temperatures observed over Argentina during two different convective storms at (left) 2130 UTC 22 Feb 2019 and (right) 0200 UTC 27 Jan 2023. Hail report locations are shown by white stars, and photographs of hailstones are shown below each map. The images were posted in the following links: (a) http://tinyurl.com/2x7ydhkp, (b) http://tinyurl.com/2bdxt7je, (c) http://tinyurl.com/mr3vcyr2, (d) http://tinyurl.com/v7tuam69, (e) http://tinyurl.com/mvkuswdm, and (f) http://tinyurl.com/3vdtncnt.
Citation: Bulletin of the American Meteorological Society 105, 7; 10.1175/BAMS-D-23-0063.1
Data records in the actual database for the reports shown in Fig. SB2. Database holds other data fields that are not shown in the table for clarity.
2. The database: Data collection and verification
The South American members that are part of this initiative started building their respective databases at different times. However, in 2020, drawing inspiration from NOAA and ESWD, a virtual dialogue started during the COVID-19 pandemic, leading to the definition of a set of parameters aimed at comprehensively describing hazardous weather events and their socioenvironmental impacts. All members agreed on standardizing the reports to contain the following:
Date and time of occurrence, including a temporal uncertainty estimation ranging from 10 min to 24 h.
Location and spatial uncertainty estimation ranging from 100 m to 100 km. Administrative names and geographical coordinates can be selected from a list of cities, towns, and neighborhoods extracted from the national geographical institutes databases (Argentina, http://www.bahra.gob.ar/; Brazil, https://www.ibge.gov.br; Chile, https://www.bcn.cl; and Uruguay, https://direcciones.ide.uy/swagger-ui.html), or they can be manually incorporated in case the location is not present in the database or for higher precision (i.e., to give specific coordinates within a city). All information is recorded although priority is given to the manually selected coordinates for dissemination purposes.
Source of the information: social media, government agencies, newspaper, personal weather stations, and storm spotters, among others.
Type of event: the following events associated with deep moist convection are being recorded into the database: hail, tornado, wind gusts, and lightning. All sizes of hail are included in the database. Severe wind gusts are only added to the database if there is either measured wind gust ≥ 80 km h−1 or wind damage. Though this threshold is lower than that used by other initiatives, sometimes we observe wind damage associated with wind gusts around 80 km h−1. A tornado report is only confirmed when a reliable picture/video is available. All locations with damage or visual observation of a tornado are included as reports, even if these reports are from the same tornado. A lightning report is included when an impact can be determined (i.e., damage or disrupt activities, and injured people, among others). The database also includes reports of storms and rainfall, particularly when they are linked to socioenvironmental impacts (e.g., flood or flash flood). Other weather categories following the interest of aviation meteorology that have socioeconomic impacts such as dust, fog, foehn wind, frost, ice, snow, and blizzard are available. Additional categories of weather hazards can be incorporated in the future depending on member interests.
Socioenvironmental impacts: infrastructure damage includes buildings, vehicles, trees, powerlines, urban infrastructure and services, and agricultural or livestock activity. Information regarding direct implications to people’s life, health, and safety, such as injuries and deaths, and to the livelihood of the affected population can also be included. Impact categories were adapted and expanded from ESWD to the region.
Additional meteorological variables: the database can host observed or estimated meteorological information such as hail size, total precipitation, precipitation type (solid or liquid), and wind intensity and direction. Tornadoes are not rated in the database due to existing resource constraints, and the available photos/videos are often insufficient to reliably estimate tornado categories. However, when information of reliable damage surveys is available, it is incorporated as an additional comment in the correspondent report.
Optional description section is open to any additional comments and possibility to upload pictures, especially important for estimating hail size and damage evaluation.
Tornadoes: The need for damage surveys
On 14 August 2020, multiple tornadoes occurred in southern Brazil. One member of the PREVOTS team was able to assess the poststorm damage in the following day and compile detailed information about the areas affected by tornadoes (Fig. SB3). Damage indicators and radar signatures were used to distinguish tornado damage from straight-line wind damage. For at least 50 years, there has been some information about tornado damage reporting in South America, a work championed by Dr. Altinger (Altinger de Schwarzkopf 1988), and now, there is an emerging interest for these assessments to evolve into a standard practice within the national weather services of South America. This evolution is driven by the recognition that damage evaluation plays a pivotal role in the disaster management cycle, contributing significantly to risk reduction and preparedness efforts. Occasionally, remote sensing can be used to determine tornado damage to vegetation and confirm tornado reports, but field surveys enable a much more accurate assessment and help improve remote sensing algorithms calibration and verification.
(a) Tornado reports on 14 Aug 2020 in Santa Catarina state, southern Brazil (map source: Google Maps). (b),(c) Photos of the damage associated with tornadoes (pictures courtesy of Vitor Goede, used with permission).
Citation: Bulletin of the American Meteorological Society 105, 7; 10.1175/BAMS-D-23-0063.1
All information is collected through a web application featuring GraphQL application programming interface (API), developed and hosted by the Centro de Investigaciones del Mar y la Atmósfera (CIMA), Argentina. This interface provides tools for report validation and makes available the content of the database in different formats, including a display of the spatial distribution of the most recent events. Information is available upon request following the terms and conditions outlined on the website.
The reports are first logged in the database by users with a “RAW” quality-control flag, and then quality controlled. The quality control protocol by expert users includes the verification of the estimated time and spatial location of the report, along with the spatial and temporal uncertainties. Depending on their availability, satellite and weather radar data are used to verify and improve accuracy of localization in time and space. Once the report passes the quality control, a “VERIFIED” flag is assigned to it.
Partners of the SAMHI database are expected to be national meteorological services, members of academia (universities and research institutes, among other agencies), and trained voluntary observers.
At present, the number of reports contained in the database is close to 40 000 and includes all types of events described above. Figures 2a and 2b show the monthly number of reports associated with severe convective weather (hail, convectively induced wind gusts, and tornadoes) for all the countries currently participating in the initiative. The Brazilian dataset (Fig. 2a) begins in June 2018 after a tornado outbreak in southern Brazil on 11 June 2018, and it was built retrospectively to 1 June 2018. The initiative is fully volunteer and is embedded in the PREVOTS project. The Argentinean dataset (Fig. 2b) was initiated during the RELAMPAGO project in late 2018 to support the field campaign and continued after the intensive observation period ended. It is supported by the CIMA and the National Meteorological Service from Argentina [Servicio Meteorológico Nacional (SMN)]. In Uruguay (Fig. 2b), the dataset was built initially by Severe Weather Data–Uruguay (Datos de Tiempo Severo–Uruguay) (x.com: @dts_uruguay) starting in August 2021, and now, the National Meteorological Institute from Uruguay [Instituto Uruguayo de Meteorología (INUMET)] supports the initiative. The Paraguayan dataset (Fig. 2b) began in 2022 and is maintained by volunteers from Severe Weather Event Reports in Paraguay [Registro de Eventos de Tiempo Severo en Paraguay (RETSPY)]. The database encompasses a range of significant events. It begins with the inaugural report from the PREVOTS initiative and delves into the comprehensive reports from the RELAMPAGO campaign. Notable among these is the thorough analysis of the “Chilean Tornado Outbreak” that occurred in late May 2019. The collaboration from SMN includes Luciano Vidal’s reports from the warm season 2019/20 and SMN’s Meteorology and Society information including relevant impact information. Coordinated reporting in the database by CIMA and SMN is initiated in August 2021, and reports from Uruguay and Paraguay are incorporated in August 2021 and June 2022, respectively, from weather enthusiasts. Socioenvironmental impacts (Fig. 2c) started to be collected during RELAMPAGO in Argentina, while in 2021, the information acquired a significant value given the decision of the SMN to envision a transition to multihazard-impact-based forecasting and warnings.
(a) Number of severe storm reports in Brazil per month since 2018. (b) As in (a), but for Argentina, Chile, Paraguay, and Uruguay. (c) Number of reported impacts by category in Argentina over the same period, considering all event types available in SAMHI database. Note vertical data scaling has different intervals along the three panels. Circles indicate relevant milestones for the database: 1) PREVOTS first report; 2) RELAMPAGO field campaign reports; 3) the Chilean tornado outbreak; 4) reports generated by Luciano Vidal at SMN; 5) the 30 Jun 2020 severe weather outbreak; 6) PREVOTS started to gather only hail reports with known sizes and/or associated with damage; 7) SMN—Meteorology and Society department starts reporting; 8) beginning of coordinated reporting in the database by CIMA and SMN; 9) first report from Uruguay; 10) first report from Paraguay.
Citation: Bulletin of the American Meteorological Society 105, 7; 10.1175/BAMS-D-23-0063.1
Figure 3 shows all severe weather reports (hail, wind gusts, and tornadoes) available in the SAMHI database from 2018 up to July 2023 (Fig. 3a) and prior to that date (Fig. 3b). All reports after 2018 in Fig. 3 are associated with a convective storm that is evident in either radar or satellite data. Reports are predominant over south-central Brazil, north-central Argentina, Uruguay, and Paraguay, but a large number of events can also be observed in northern Patagonia, central-southern Chile, as well as the Amazon region. Uninhabited regions, identifiable in Fig. 1, are correlated with regions with no reports in Fig. 3. Severe convective reports total to 5545 including tornadoes (Fig. 3c), large hail (greater than or equal to 2 cm, Fig. 3d), and wind gusts (greater than or equal to 80 km h−1 and/or associated with damage, Fig. 3e) between 2018 and July 2023. In addition to the reports collected in near–real time, there is a continuous effort to integrate into the SAMHI pre-2018 records (Fig. 3b). This includes the daily hail occurrence reports gathered from traditional surface stations affiliated with the SMN and the National Agricultural Technology Institute in Argentina. These records, which date back to 1908, were analyzed by Mezher et al. (2012). Tornado and severe storm reports in Argentina since 1930 reported by Altinger de Schwarzkopf (1988) and Balbi and Barbieri (2017) are also included, as well as HIW events from Uruguay and Brazil (Lopes and Nascimento 2024). In addition, the Chilean tornado database, maintained by researchers in the Department of Geophysics at the University of Chile, with reports going as far back to 1554 will also be included in the SAMHI database. Many retrospective activities are undertaken to enhance the database, such as conducting newspaper research at the National Library or photo search at the Digital Library of Argentinean National Archives. Similar efforts are currently under way in Brazil regarding the documentation of pre-2018 tornado events reported in the scientific literature (e.g., Silva Dias 2011), media clippings, and Civil Defense reports. These efforts involve the digitization of historical reports, thereby enriching the database’s content and accessibility.
Geographical distribution of all the severe weather reports in the SAMHI database. (a) All the categories since 2018, (b) all the categories before 2018, (c) tornadoes (including waterspouts), (d) hail, and (e) damaging wind gusts.
Citation: Bulletin of the American Meteorological Society 105, 7; 10.1175/BAMS-D-23-0063.1
3. Future challenges
Databases containing information about HIW and their effects are uncommon in many regions (Golding 2022), access to them is mostly restricted, and observation protocols often differ among databases. To avoid propagating such issues, we believe that the construction, maintenance, and verification of the SAMHI database is a fundamental component for understanding the physical processes leading to HIW events, validation of severe weather remote sensing tools, numerical modeling verification, development of locally calibrated early warning systems, and possibly a vast array of innovative products on the horizon, driven by the rapid advancement of artificial intelligence techniques.
The major challenge in this endeavor is to identify the institution(s) that can maintain leadership in this initiative, since long-standing endeavors cannot rely on a few individuals. These institutions play a crucial role in fostering engagement across the entire community, establishment and maintenance of reporting standards, and data-sharing policies. Moreover, these institutions should actively collaborate with information providers, including emergency managers and volunteer networks, while advocating for high-quality reports from partners and consistency in the database throughout the years.
The database sustainability hinges on the active participation of a broad and diverse community that recognizes the value of the information for their specific needs. Several actions will be taken to boost the number of high-quality reports, especially in areas with large temporal and spatial gaps. One way to achieve this goal is to get elementary school students and weather enthusiasts directly involved with the reporting processes. We are currently in the process of creating multiple tools to simplify this involvement (i.e., chatbot, training courses, and educational material, among others). Pursuing the active participation of students and weather activists has the potential to improve weather awareness. We are also working toward involving emergency managers to report directly to the database and opening a discussion about their needs as potential users of multihazard-impact-based forecasts and warnings. This latter point is important as the inherent design of the SAMHI API allows enhancing data access to end users like emergency managers.
It is imperative to actively engage national weather services across South America that are currently not involved with the SAMHI database to contribute to it, enabling the assessment of cross-border risks that have the potential to impact their respective nations. In pursuit of this goal, meetings were organized with the WMO Regional Office III in 2022 and new actions will be implemented in 2024.
In the coming years, a crucial challenge will be securing the necessary financial resources to ensure the project’s long-term sustainability. To achieve this goal, the team must develop a comprehensive plan and deliver a compelling pitch to potential investors or donors. With this in mind, an annual in-person meeting will be organized to address these challenges and build strategies for the future.
Acknowledgments.
The authors are thankful for the valuable contributions made by Candela Casanovas and Franco Piscitelli during the initial phase of this project. Additionally, they thank John T. Allen, Pieter Groenemeijer, and anonymous reviewer for productive discussion and their insightful comments and suggestions to improve the manuscript. The authors wish to thank the CPA staff from CIMA for their generous support and technical assistance. They especially thank Rodrigo Marquez, Claudio Mattera, Paula Richter, Alfredo Rolla, Pablo Roselli, and Gabriel Vieytes.
Data availability statement.
The information available in this paper is based on data accessible upon request at https://samhi.cima.fcen.uba.ar/.
References
Altinger de Schwarzkopf, M. L., 1988: Climatología de los efectos de la convección severa en la República Argentina (Climatology of the effects of severe convection in the Argentine Republic). Ph.D. thesis, Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, 161 pp.
Balbi, M., and P. Barbieri, 2017: Enfoque científico del riesgo—Evaluación del potencial de tornados en Argentina (Scientific approach to risk—Assessment of tornado potential in Argentina). Anales de la Academia Nacional de Ciencias Naturales de Buenos Aires, 115 pp., https://www.ciencias.org.ar/user/Enfoque%20cientifico%20del%20riesgo%20-%20evaluacion%20del%20potencial%20de%20tornados%20en%20al%20republica%20argentina%202.pdf.
Bang, S. D., and D. J. Cecil, 2019: Constructing a multifrequency passive microwave hail retrieval and climatology in the GPM domain. J. Appl. Meteor. Climatol., 58, 1889–1904, https://doi.org/10.1175/JAMC-D-19-0042.1.
Barras, H., A. Hering, A. Martynov, P.-N. Noti, U. Germann, and O. Martius, 2019: Experiences with >50,000 crowdsourced hail reports in Switzerland. Bull. Amer. Meteor. Soc., 100, 1429–1440, https://doi.org/10.1175/BAMS-D-18-0090.1.
Barrett, B. S., J. C. Marin, and M. Jacques-Coper, 2020: A multiscale analysis of the tornadoes of 30–31 May 2019 in south-central Chile. Atmos. Res., 236, 104811, https://doi.org/10.1016/j.atmosres.2019.104811.
Beal, A., R. Hallak, L. D. Martins, J. A. Martins, G. Biz, A. P. Rudke, and C. R. T. Tarley, 2020: Climatology of hail in the triple border Paraná, Santa Catarina (Brazil) and Argentina. Atmos. Res., 234, 104747, https://doi.org/10.1016/j.atmosres.2019.104747.
Bechis, H., and Coauthors, 2022: A case study of a severe hailstorm in Mendoza, Argentina, during the RELAMPAGO-CACTI field campaign. Atmos. Res., 271, 106127, https://doi.org/10.1016/j.atmosres.2022.106127.
Borque, P., L. Vidal, M. Rugna, T. J. Lang, M. G. Nicora, and S. W. Nesbitt, 2020: Distinctive signals in 1‐min observations of overshooting tops and lightning activity in a severe supercell thunderstorm. J. Geophys. Res. Atmos., 125, e2020JD032856, https://doi.org/10.1029/2020JD032856.
Brooks, H. E., J. W. Lee, and J. P. Craven, 2003: The spatial distribution of severe thunderstorm and tornado environments from global reanalysis data. Atmos. Res., 67–68, 73–94, https://doi.org/10.1016/S0169-8095(03)00045-0.
Bruick, Z. S., K. L. Rasmussen, and D. J. Cecil, 2019: Subtropical South American hailstorm characteristics and environments. Mon. Wea. Rev., 147, 4289–4304, https://doi.org/10.1175/MWR-D-19-0011.1.
dos Santos, L. O., E. L. Nascimento, and J. T. Allen, 2023: Discriminant analysis for severe storm environments in south-central Brazil. Mon. Wea. Rev., 151, 2659–2681, https://doi.org/10.1175/MWR-D-22-0347.1.
Dotzek, N., P. Groenemeijer, B. Feuerstein, and A. M. Holzer, 2009: Overview of ESSL’s severe convective storms research using the European Severe Weather Database ESWD. Atmos. Res., 93, 575–586, https://doi.org/10.1016/j.atmosres.2008.10.020.
Durañona, V., E. Marchesoni, and R. Sallés, 2019: A first characterization of high winds that affect the energy distribution system of Uruguay and their related effects. J. Wind Eng. Ind. Aerodyn., 184, 128–138, https://doi.org/10.1016/j.jweia.2018.10.022.
Ferreira, V., V. Goede, and E. L. Nascimento, 2022: An environmental and polarimetric study of the 19 November 2015 supercell and multiple-vortex tornado in Marechal Cândido Rondon, southern Brazil. Meteor. Atmos. Phys., 134, 82, https://doi.org/10.1007/s00703-022-00922-5.
Figueiredo, E. L., E. L. Nascimento, and M. I. Oliveira, 2019: Analysis of two derecho events in Southern Brazil. Meteor. Atmos. Phys., 131, 1171–1190, https://doi.org/10.1007/s00703-018-0654-x.
Glazer, R. H., and Coauthors, 2021: Projected changes to severe thunderstorm environments as a result of twenty-first century warming from RegCM CORDEX-CORE simulations. Climate Dyn., 57, 1595–1613, https://doi.org/10.1007/s00382-020-05439-4.
Golding, B., 2022: Towards the “Perfect” Weather Warning. Springer, 270 pp.
Groenemeijer, P., and Coauthors, 2017: Severe convective storms in Europe: Ten years of research and education at the European Severe Storms Laboratory. Bull. Amer. Meteor. Soc., 98, 2641–2651, https://doi.org/10.1175/BAMS-D-16-0067.1.
Johns, R. H., and W. D. Hirt, 1987: Derechos: Widespread convectively induced windstorms. Wea. Forecasting, 2, 32–49, https://doi.org/10.1175/1520-0434(1987)002<0032:DWCIW>2.0.CO;2.
Kumjian, M. R., and Coauthors, 2020: Gargantuan hail in Argentina. Bull. Amer. Meteor. Soc., 101, E1241–E1258, https://doi.org/10.1175/BAMS-D-19-0012.1.
Lopes, M. M., and E. L. Nascimento, 2024: Atmospheric environments associated with tornadoes in southern Brazil and neighboring areas as compared to other modes of convective hazards. Climate Dyn., 62, 3641–3667, https://doi.org/10.1007/s00382-023-07089-8.
Machado, L. A. T., and Coauthors, 2014: The CHUVA project: How does convection vary across Brazil? Bull. Amer. Meteor. Soc., 95, 1365–1380, https://doi.org/10.1175/BAMS-D-13-00084.1.
Marín, J. C., B. S. Barrett, and D. Pozo, 2021: The tornadoes of 30–31 May 2019 in south-central Chile: Sensitivity to topography and SST. Atmos. Res., 249, 105301, https://doi.org/10.1016/j.atmosres.2020.105301.
Marsigli, C., and Coauthors, 2021: Review article: Observations for high-impact weather and their use in verification. Nat. Hazards Earth Syst. Sci., 21, 1297–1312, https://doi.org/10.5194/nhess-21-1297-2021.
Martins, J. A., and Coauthors, 2017: Climatology of destructive hailstorms in Brazil. Atmos. Res., 184, 126–138, https://doi.org/10.1016/j.atmosres.2016.10.012.
Matsudo, C. M., and P. V. Salio, 2011: Severe weather reports and proximity to deep convection over Northern Argentina. Atmos. Res., 100, 523–537, https://doi.org/10.1016/j.atmosres.2010.11.004.
McGovern, A., R. J. Chase, M. Flora, D. J. Gagne II, R. Lagerquist, C. K. Potvin, N. Snook, and E. Loken, 2023: A review of machine learning for convective weather. Artif. Intell. Earth Syst., 2, e220077, https://doi.org/10.1175/AIES-D-22-0077.1.
Mezher, R. N., M. Doyle, and V. Barros, 2012: Climatology of hail in Argentina. Atmos. Res., 114–115, 70–82, https://doi.org/10.1016/j.atmosres.2012.05.020.
Nascimento, E. L., G. Held, and A. M. Gomes, 2014: A multiple-vortex tornado in southeastern Brazil. Mon. Wea. Rev., 142, 3017–3037, https://doi.org/10.1175/MWR-D-13-00319.1.
Nesbitt, S. W., and Coauthors, 2021: A storm safari in subtropical South America: Proyecto RELAMPAGO. Bull. Amer. Meteor. Soc., 102, E1621–E1644, https://doi.org/10.1175/BAMS-D-20-0029.1.
Oliveira, M. I., F. S. Puhales, E. L. Nascimento, and V. Anabor, 2022: Integrated damage, visual, remote sensing, and environmental analysis of a strong tornado in southern Brazil. Atmos. Res., 274, 106188, https://doi.org/10.1016/j.atmosres.2022.106188.
Piscitelli, F. M., J. J. Ruiz, P. Negri, and P. Salio, 2022: A multiyear radar-based climatology of supercell thunderstorms in central-eastern Argentina. Atmos. Res., 277, 106283, https://doi.org/10.1016/j.atmosres.2022.106283.
Potter, S., S. Harrison, and P. Kreft, 2021: The benefits and challenges of implementing impact-based severe weather warning systems: Perspectives of weather, food, and emergency management personnel. Wea. Climate Soc., 13, 303–314, https://doi.org/10.1175/WCAS-D-20-0110.1.
Potter, S. H., H. P. Kreft, P. Milojev, C. Noble, B. Montz, A. Dhellemmes, R. J. Woods, and S. Gauden-Ing, 2018: The influence of impact-based severe weather warnings on risk perceptions and intended protective actions. Int. J. Disaster Risk Reduct., 30, 34–43, https://doi.org/10.1016/j.ijdrr.2018.03.031.
Prein, A. F., and G. J. Holland, 2018: Global estimates of damaging hail hazard. Wea. Climate Extremes, 22, 10–23, https://doi.org/10.1016/j.wace.2018.10.004.
Púčik, T., C. Castellano, P. Groenemeijer, T. Kühne, A. T. Rädler, B. Antonescu, and E. Faust, 2019: Large hail incidence and its economic and societal impacts across Europe. Mon. Wea. Rev., 147, 3901–3916, https://doi.org/10.1175/MWR-D-19-0204.1.
Rasmussen, K. L., and R. A. Houze Jr., 2011: Orogenic convection in subtropical South America as seen by the TRMM satellite. Mon. Wea. Rev., 139, 2399–2420, https://doi.org/10.1175/MWR-D-10-05006.1.
Rasmussen, K. L., M. D. Zuluaga, and R. A. Houze Jr., 2014: Severe convection and lightning in subtropical South America. Geophys. Res. Lett., 41, 7359–7366, https://doi.org/10.1002/2014GL061767.
Ribeiro, B. Z., and L. F. Bosart, 2018: Elevated mixed layers and associated severe thunderstorm environments in South and North America. Mon. Wea. Rev., 146, 3–28, https://doi.org/10.1175/MWR-D-17-0121.1.
Ribeiro, B. Z., L. A. T. Machado, J. H. C. Huamán, T. S. Biscaro, E. D. Freitas, K. W. Mozer, and S. J. Goodman, 2019: An evaluation of the GOES-16 rapid scan for nowcasting in southeastern Brazil: Analysis of a severe hailstorm case. Wea. Forecasting, 34, 1829–1848, https://doi.org/10.1175/WAF-D-19-0070.1.
Romatschke, U., and R. A. Houze Jr., 2010: Extreme summer convection in South America. J. Climate, 23, 3761–3791, https://doi.org/10.1175/2010JCLI3465.1.
Sills, D. M. L., and Coauthors, 2020: The Northern Tornadoes Project: Uncovering Canada’s true tornado climatology. Bull. Amer. Meteor. Soc., 101, E2113–E2132, https://doi.org/10.1175/BAMS-D-20-0012.1.
Silva Dias, M. A. F., 2011: An increase in the number of tornado reports in Brazil. Wea. Climate Soc., 3, 209–217, https://doi.org/10.1175/2011WCAS1095.1.
Taszarek, M., J. T. Allen, M. Marchio, and H. E. Brooks, 2021: Global climatology and trends in convective environments from ERA5 and rawinsonde data. npj Climate Atmos. Sci., 4, 35, https://doi.org/10.1038/s41612-021-00190-x.
Trapp, R. J., and Coauthors, 2020: Multiple-platform and multiple-Doppler radar observations of a supercell thunderstorm in South America during RELAMPAGO. Mon. Wea. Rev., 148, 3225–3241, https://doi.org/10.1175/MWR-D-20-0125.1.
Tsonevsky, I., C. A. Doswell Jr., and H. E. Brooks, 2018: Early warnings of severe convection using the ECMWF extreme forecast index. Wea. Forecasting, 33, 857–871, https://doi.org/10.1175/WAF-D-18-0030.1.
Varble, A. C., and Coauthors, 2021: Utilizing a storm-generating hotspot to study convective cloud transitions: The CACTI experiment. Bull. Amer. Meteor. Soc., 102, E1597–E1620, https://doi.org/10.1175/BAMS-D-20-0030.1.
Veloso-Aguila, D., K. L. Rasmussen, and E. D. Maloney, 2024: Tornadoes in Southeast South America: Mesoscale to planetary-scale environments. Mon. Wea. Rev., 152, 295–318, https://doi.org/10.1175/MWR-D-22-0248.1.
Vera, C., and Coauthors, 2006: The South American Low-Level Jet Experiment. Bull. Amer. Meteor. Soc., 87, 63–78, https://doi.org/10.1175/BAMS-87-1-63.
Vicencio, J., and Coauthors, 2021: The Chilean tornado outbreak of May 2019: Synoptic, mesoscale, and historical contexts. Bull. Amer. Meteor. Soc., 102, E611–E634, https://doi.org/10.1175/BAMS-D-19-0218.1.
WMO, 2020: WMO methodology for cataloguing hazardous weather, climate, water and space weather events. WMO, 4 pp., https://unfccc.int/documents/266772.
WMO, 2022: Early Warnings for All: The UN Global early warning initiative for the implementation of climate adaptation executive action plan 2023-2027. WMO, 56 pp., https://library.wmo.int/records/item/58209-early-warnings-for-all.
Zhou, Z., Q. Zhang, J. T. Allen, X. Ni, and C.-P. Ng, 2021: How many types of severe hailstorm environments are there globally? Geophys. Res. Lett., 48, e2021GL095485, https://doi.org/10.1029/2021GL095485.
Zipser, E. J., D. J. Cecil, C. Liu, S. W. Nesbitt, and D. P. Yorty, 2006: Where are the most intense thunderstorms on Earth? Bull. Amer. Meteor. Soc., 87, 1057–1072, https://doi.org/10.1175/BAMS-87-8-1057.
