Abstract Tornado outbreaks cause some of the most impactful extreme events associated with convective storms. Despite their destructive potential, these phenomena are barely forecasted in Brazil, and only recently have their reports been systematically documented in that country. On 7 November 2025, a historic tornado outbreak occurred in southeastern South America, specifically in Paraguay, Argentina, and Brazil, with Brazil being the hardest hit. Multiple supercells produced tornadoes, of which two were long tracked and violent. One of the tornadoes struck the town of Rio Bonito do Iguaçu, causing 6 deaths, over 830 injuries, and the destruction of 80% of the town. The other tornado devastated forests, villages, and caused one fatality in the municipality of Guarapuava. This article provides an overview of the tornado outbreak and its forecast by PREVOTS (Portuguese for Platform for Severe Weather Reports and Storm Spotter Network). PREVOTS has issued experimental convective outlooks since 2020 to test their operational applicability and assess their potential for improving severe weather forecasting in Brazil. The tornado outbreak was well forecasted by PREVOTS, who issued a maximum level 4 convective outlook on the day of the event. Of the 12 tornadoes reported in Brazil, 8 occurred within the level 4 area, including three of the four significant tornadoes. Noteworthy aspects of this event include a relatively rare, progressive continental extratropical cyclone that established a broad warm sector with moderate conditional instability and high near-surface antistreamwise horizontal vorticity, a key ingredient for tornadogenesis. The article discusses exceptional aspects of the outbreak and addresses lessons to be learned from this catastrophe.
Abstract The current effort provided an historical review of the literature on urban impacts on summer convective precipitation. It then summarized the methods used in such analyses and recommended methodological and practical techniques that provide insights into interactions between the urban processes that produce the variety of observed and modeled precipitation (PP) impacts. It then discussed studies that have provided new insights into these processes. The review finally reevaluated the classic Metropolitan Meteorological Experiment (METROMEX) urban downwind PP maximum, in light of the newer observations of urban-induced thunderstorm (TS) bifurcation. Two questions addressed are as follows: (i) What are the best practices to better understand the relevant urban impacts on resultant summer TS PP patterns and (ii) can the METROMEX-observed downwind urban PP maximum be reconciled with the newer observed urban TS bifurcation effect? The most significant results from this reinterpretation of the “classic” METROMEX plot of total summer TS rainfall have revealed that (i) its downwind maximum can be revisualized to show two downwind bifurcated lateral PP maxima and (ii) the original results included storms from all directions, and thus, the individual twin bifurcation PP maxima seemingly blended into a single contiguous downwind maximum. Less-cited original METROMEX analyses reproduced herein did in fact show that the original study showed (i) a diurnal late-afternoon PP peak associated with UHI storm initiation over the city and (ii) that the predominant north-northeast-moving storms did produce a clearly bifurcated downwind maxima. These expanded results now are consistent with the newer results discussed in the paper. Significance Statement This article reevaluates the main result from the classic Metropolitan Meteorological Experiment (METROMEX) study, still the “gold standard” of how cities impact summer thunderstorm rainfall. It says that cities produce a peak in precipitation along the downwind urban edge. The key finding from the current effort is that two conflicting urban effects alter such storms. During calm periods, warm cities initiate storms to produce rainfall peaks over their center. During stormy conditions, UHI tends to be weaker due to less sunshine and to generally faster winds, and thus, storms moving over cities are split by their tall buildings to produce a peak rain area on each lateral downwind urban edge. The original METROMEX claim resulted because it considered all storms together. This new understanding will help to refine the forecasting of urban flooding from such storms.
Abstract Advancing atmospheric science increasingly depends on how effectively we design, execute, and adapt observational experiments. While major progress has been made in numerical modeling and artificial intelligence (AI), observational experimentation has not kept pace. Most observing systems—including radars—still operate as largely stand-alone instruments, using fixed or heuristically defined strategies that limit our ability to capture fast-evolving phenomena and fully exploit emerging analytical tools. Here, we argue for a shift from static, hardware-centric observations toward connected, outcome-driven experimentation. Drawing on lessons from the Multisensor Agile Adaptive Sampling (MAAS) project, we introduce the concept of Connected Radar, in which radars operate as part of an integrated, cloud-based experimental infrastructure that incorporates satellite data, lightning observations, cameras, drones, and AI-driven data fusion. In this paradigm, radar operation is treated as part of a controlled experiment, where sensing decisions are guided by scientific intent and information gain rather than predefined scan strategies. Artificial intelligence plays a central role by linking sensing actions to experimental outcomes through self-supervised learning, uncertainty estimation, and active sampling. This reframes radar agility as information (or cognitive) agility, not just physical beam steering. We show how this framework provides a natural pathway for fully exploiting phased-array radar capabilities and for rethinking future radar facilities as autonomous laboratories for atmospheric research—where scientific value is defined by insight gained, not hardware alone.
Abstract The Earth’s middle atmosphere spans the deep region from the upper troposphere/lower stratosphere at around 10 km altitude to the mesosphere/lower thermosphere at around 100 km altitude. It is being increasingly recognized for its role in driving extreme surface weather and regional climate change. Climate models predict large ongoing and future changes in the middle atmosphere composition and circulation. However, the observations needed to detect, attribute and understand these changes and their impacts, to test predictions, and thereby to improve our models, are lacking. Here we show the capacity of infrared limb-imaging tomography to provide the needed observations. This evaluation is based on studies performed within a recent satellite mission concept – the Changing-Atmosphere Infrared Tomography Explorer, CAIRT. Observing thermal infrared emissions simultaneously from the middle troposphere at about 4 km up to the lower thermosphere at about 115 km altitude this technique provides observations of temperature and an extensive range of trace gases with unprecedented spatial resolution of about 50 by 50 km horizontally and about 1 km vertically. We show how these observations would (a) help to quantify the changing atmospheric circulation, (b) allow characterization and quantification of the gravity waves that are critical in driving this circulation, (c) reveal how variability in solar radiation and energetic particles propagate downward to affect regional climate at the surface, (d) detect how volcanic eruptions and wildfires impact the middle atmosphere and climate, and (e) resolve how stratosphere-troposphere exchange affects ozone and water vapor in the crucial and climate-relevant tropopause region.
Abstract The Clouds And Precipitation Experiment at Kennaook (CAPE-k) student workshop leveraged international scientific expertise as students explored data collected using advanced instrumentation during the CAPE-k campaign in Tasmania, Australia from April 2024 to October 2025. By linking the workshop directly to CAPE-k, we aimed to inspire the next generation of atmospheric scientists by exposing them to state-of-the-science instrumentation and the excitement that accompanies a major international campaign of this nature. We harnessed the enthusiasm and skills of the cohort of students to start the initial exploration of the CAPE-k data via four projects, which are presented by the students in this article, foreshadowing some of the exciting science to emerge from this campaign.
Abstract Seasonal climate forecasting in southern Africa has evolved over three decades from statistical rainfall outlooks to an integrated system combining coupled ocean–atmosphere models, statistical recalibration, probabilistic verification, and sector-specific climate services. This paper documents that development within a sustained collaborative framework linking South African institutions with U.S. modelling centres, highlighting contributions from ENSO prediction, the North American Multi-Model Ensemble, and the IRI Climate Predictability Tool. We demonstrate continuity of archived real-time Niño3.4 sea-surface temperature anomaly forecasts since 2015, their extension to full-field global SST anomaly prediction, and probabilistic verification of seasonal rainfall forecasts across the Southern African Development Community. Archived real-time rainfall forecasts demonstrate measurable probabilistic skill relative to climatology, with useful discrimination of extreme categories. Applications at farm scale and within a provincial malaria early warning system illustrate how calibrated probabilistic guidance is translated into agricultural and public-health decision contexts. The collaboration has been reciprocal: U.S. coupled model systems underpin regional rainfall forecasting, while South African ENSO forecasts contribute to international multi-model assessment efforts. Southern Africa’s seasonally varying and spatially heterogeneous predictability provides a structured environment for evaluating global seasonal prediction systems. Sustained bilateral engagement has contributed to advances in both regional climate services and the broader science of seasonal forecasting.
Abstract Nine years ago, the Department of Interior’s Bureau of Ocean Energy Management (BOEM), the agency with air quality (AQ) jurisdiction over the outer continental shelf (OCS) of the U.S. Gulf Coast west of 87.5°W longitude, asked the National Aeronautics and Space Administration (NASA) to determine the feasibility of using satellite data to measure offshore emissions in a region of concentrated oil and natural gas (ONG) operations. To study this issue, NASA and BOEM conducted the May 2019 Satellite Coastal and Oceanic Atmospheric Pollution Experiment (SCOAPE) cruise in the Gulf. SCOAPE addressed both technological and scientific issues related to measuring nitrogen dioxide (NO 2 ; a common air pollutant), including contrasting nearshore and deep-water regimes. Given the April 2023 launch of the geostationary Tropospheric Emissions: Monitoring of Pollution (TEMPO) AQ satellite, a 2024 SCOAPE-II was conducted in the Gulf with both ship and aircraft measurements. We present an overview of the SCOAPE-II campaign, an analysis and validation of satellite-observed NO 2 , and evaluate measurements of methane from ship, aircraft, and satellite near ONG platforms. Our SCOAPE-II results are as follows: 1) Satellite NO 2 measurements (∼1330 local time) from the Tropospheric Monitoring Instrument (TROPOMI) are more accurate than TEMPO’s hourly scans (8.6% vs 23.6% mean absolute bias); a new version of the TEMPO data is currently being processed; 2) ship and aircraft measurements captured dozens of NO 2 and methane plumes from ONG operations, showing that they are persistent emitters; and 3) satellite measurements of methane failed to replicate ship and aircraft measurements, presenting ongoing challenges for operational emissions monitoring over the Gulf. Significance Statement The exploration, extraction, and processing of oil and natural gas (ONG) from deposits off the U.S. Gulf Coast generate detectable emissions affecting air quality (AQ). However, the lack of regular monitoring of these emissions makes quantifying their impact challenging. We conducted a ship- and aircraft-based campaign in 2024 in the Gulf to characterize surface AQ, validate satellite AQ and methane measurements, and evaluate ONG emissions inventories. Our measurements indicate that ONG platforms are persistent emitters of nitrogen oxides (NO x ) that affect AQ, and methane, a potent greenhouse gas. Satellite nitrogen dioxide (NO 2 ) measurements are generally of sufficient accuracy for characterizing Gulf AQ, but technological constraints preclude similar monitoring of ONG methane emissions from space.
Abstract The risk of heavy rain and urban flood disasters is rapidly increasing, posing an urgent global challenge, particularly with the escalating vulnerabilities of underprivileged populations. PREVENIR is a five-year (2022-2027) international cooperation project between Argentina and Japan. Its objective is to create an operational impact-based early warning system for urban floods and heavy rains in Argentina’s two most populated and susceptible regions. Leveraging cutting-edge research on Big Data Assimilation (BDA) with Japan’s flagship supercomputer “Fugaku” and its predecessor “K”, PREVENIR addresses three critical gaps: the transfer of advanced scientific research to practical operations, the integration of meteorological forecasting with hydrological impact modeling, and the effective communication of actionable warnings to emergency managers and the public. PREVENIR aims to develop a comprehensive disaster prevention package, encompassing monitoring, quantitative precipitation estimation (QPE), nowcasting, BDA and numerical weather prediction (NWP), hydrological model prediction, warning communications, public education and outreach, and capacity building. This extensive objective is being pursued through a collaborative effort. The Argentine National Meteorological Service and RIKEN (Japan’s premier scientific research institute) are spearheading this initiative, which also involves other academic research institutions and various levels of government and communities, both national and local. This pioneering endeavor in Argentina is expected to provide valuable tools and recommendations for the implementation of similar systems worldwide.
Abstract The four-quadrant jet streak model is a prime example of a scientific synthesis based on decades of research. It is a conceptual framework that has become a cornerstone of meteorological education since the 1960s. The model provides an intuitive understanding of jet streak dynamics and their influence on surface weather. One of its key take-home messages is the well-known forecaster rule that cloud formation, precipitation, and extratropical cyclogenesis are favored below the “right entrance or left exit.” In recent years, many studies have highlighted the influence of diabatic processes, as they occur in clouds, on the structure of jet streaks. This article aims to extend the four-quadrant model by explicitly including the role of diabatic processes. The potential vorticity gradient serves as a proxy variable to connect adiabatic and diabatic dynamics, and a climatological analysis of jet-streak-centered composites reveals consistent diabatic influences: Along the jet axis, radiative processes act to narrow and intensify the jet; moist diabatic processes associated with cloud formation strengthen the jet in the equatorward entrance, and turbulence is active along the jet axis and in the poleward exit. The results show that different diabatic processes dominate in different jet streak quadrants. In particular, the analyses show that moist diabatic processes in clouds matter for the intensification and propagation of jet streaks. Significance Statement The four-quadrant jet streak model has been a cornerstone of meteorological education for decades, explaining jet streak dynamics and their influence on surface weather. This study shows that diabatic processes—latent heating in clouds, radiative cooling, and clear-air turbulence—act systematically in distinct regions of the jet streak and that their influence intensifies with jet streak strength. These findings add a diabatic layer to the classical model that organizes and synthesizes many years of advances in process-level understanding into a coherent conceptual picture. This addition is relevant for understanding biases in jet streak forecasting and for anticipating potential changes of jet streak characteristics in a warmer atmosphere.
Abstract A variety of indices have been used to determine physiological risk imposed by extreme heat, humidity, and other variables. Given the breadth of such indices and their potential uses, it is important to assess correlations between these indices and human experiences and responses to heat conditions. This paper compares the widely recognized Heat Index; the newly developed Heat Risk tool, now used experimentally by the National Weather Service; and human perceptions of dangerous heat conditions in central South Carolina, assessed through perceived comfort. Data and forecasts from two consecutive summers suggest that the categories used in the new Heat Risk tool may underestimate heat risk on several summer days compared to the risk measured by the Heat Index and as reflected in perceived comfort among vulnerable populations exposed to heat stress.
Abstract The Multi-University Consortium for Advanced Data Assimilation Research and Education (CADRE) is a new initiative recently funded by the National Oceanic and Atmospheric Administration (NOAA) to accelerate data assimilation (DA) research, education, and workforce development. Unlike previous initiatives, CADRE fosters end-to-end, direct, and comprehensive collaboration between university faculty and government agencies. It supports innovative DA research, prepares the next-generation DA workforce, and facilitates the transition of DA research to operational applications. CADRE performs a broad scope of cutting-edge research to address multiscale, nonlinear, and coupled Earth system DA challenges to improve short-range (sub-hourly) to seasonal predictions. It achieves this task through innovative data assimilation algorithm development, novel applications of machine learning (ML) in DA, and optimizing the utilization of existing and new in-situ and remotely sensed observations. Beyond research, CADRE establishes a comprehensive education, workforce development, and community building program, which includes a novel graduate student advising model, new university class curriculum development, public training courses, community scientific workshops, an international exchange program, trans-disciplinary partnerships, an outreach program, and promotion of the open sharing of data, code, and educational materials. Through this unique holistic approach, CADRE is set to strengthen both the intellectual and software infrastructure in the broad community for DA research, increase the number of DA scientists with expanded skill sets, and revolutionize forecasting capabilities.
Abstract The Paris 2024 Olympics Research Demonstration Project lasted five years and was endorsed by the World Weather Research Programme within WMO to improve urban weather forecasts, using the metropolitan area of Paris as a case study. Meteorological institutes and universities from ten countries participated. The project had three objectives: to increase knowledge on urban summer meteorological hazards; to improve hectometric-scale numerical weather prediction models in cities; and to facilitate the co-production of weather information for large sporting events. Increased convective activity downwind of Paris was highlighted by a new radar- and lightning-based climatology, and numerical experiments identified its potential driving processes. Collaborative analyzes of past heat waves improved air quality models for the Olympics and simulations of thermal comfort variability. Advances in urban-scale modeling enabled real-time intercomparison of seven hectometric or kilometric scale atmospheric models that provided daily forecasts throughout the Olympics and Paralympics. Hectometric models showed similarities in convective precipitation characteristics, tending to have an excessive number of small showers and grid length dependence. Model intercomparisons also showed variability in Urban Heat Island intensity and unexpected differences in urban heat plume extent. A sociological study highlighted the differences in viewpoints between forecasters and sport managers and the importance of transparency in communicating uncertainties. Finally, a decision-making procedure to manage extreme heat contingencies for the “Marathon for All” Olympics public event was developed based on a 100-m grid-length model in collaboration with the weather forecasters’ team. All these insights open new scientific questions in urban climate research and ways forward for future hectometric numerical weather prediction.
