Abstract On 4 July 2025, multiple factors combined to produce a catastrophic flash flood in the Texas Hill Country. The event caused 137 fatalities across South Texas, with the greatest impacts in Kerr County (119 fatalities with two additional missing persons) near the Camp Mystic for Girls summer camp. Widespread convection occurred across the Texas Hill Country, including a focused 10-county area that received > 150 mm of rainfall between 0300 and 1800 UTC, while a quasi-stationary supercell generated localized totals > 250 mm largely within a 3-h period from 0600 to 0900 UTC over the Guadalupe River basin headwaters. This study synthesizes observations, reanalyses, convection-allowing model analyses, and hydrologic simulations to diagnose the event. Results show that the supercell formed as a northwestward-moving surface boundary intersected strengthening midlevel westerly flow on the southern flank of a mesoscale convective vortex that tracked from northern Mexico into south-central Texas. Increasing southwesterly flow aloft and a strengthening southeasterly low-level jet enhanced vertical shear, supporting supercell organization and limited storm motion. Deep tropical moisture from the southeast, modulated by Tropical Cyclone Barry, together with mid- and upper-level moisture from the tropical eastern North Pacific associated with Tropical Cyclone Flossie, promoted high precipitation efficiency and extreme rain rates. Hydrologic simulations indicate that storm placement over the basin promoted near-synchronous flood-wave superposition from the South Fork of the Guadalupe River and Cypress Creek at Camp Mystic, exacerbating inundation. A companion study examines short-term predictability using convection-allowing ensemble forecasts coupled with hydrologic prediction tools. Significance Statement Flash floods in the Texas Hill Country can quickly develop when storms stall over steep terrain and small river basins. We analyze the 4 July 2025 disaster, which resulted in 119 fatalities in Kerr County, to explain why rainfall became so extreme near Camp Mystic. An unusually deep plume of tropical moisture and a persistent circulation enhanced lift and wind shear, allowing a supercell thunderstorm to linger for hours and produce exceptional rain rates. The storm initiated within the Guadalupe basin headwaters as a surface boundary intersected strengthening winds aloft, placing peak rainfall over a vulnerable watershed. Hydrologic simulations show this placement led to nearly simultaneous flood surges on the South Fork of the Guadalupe River and Cypress Creek at Camp Mystic, worsening inundation.
Abstract Wildfire smoke is drastically altering the air quality landscape across the United States (US), forcing environmental agencies to confront frequent exceedances of the National Ambient Air Quality Standards (NAAQS). Currently, the operational workflow for developing an Exceptional Event Demonstration (EED) is a resource-intensive process requiring disparate data portals and specialized software. This makes it challenging for state and federal agencies to demonstrate the relationship between smoke and air pollution. Additionally, few tools exist for health scientists to rigorously assess the PM 2.5 and O 3 impacts of wildfire smoke. To meet this need, we developed “Smokelyze”, an open-access, web-based app. Based on peer-reviewed research, it integrates surface observations, satellite data, meteorological data combined with machine learning (Generalized Additive Models or GAMs) to quantify smoke contributions to PM 2.5 and O 3 at nearly every regulatory monitor in all states. The app features a user-friendly workspace for visualizing the results, with fusion of our GAMs with multiple surface, satellite and transport parameters, including EPA’s AirNow monitoring data, NOAA HMS smoke polygons and fire locations, NASA’s TEMPO and TROPOMI for NO 2 and HCHO retrievals, and on-demand cloud-based HYSPLIT model results. Smokelyze provides an optimized environment for environmental agencies to understand and quantify the degree to which wildfire smoke influences PM 2.5 and O 3 . This understanding can be used to support state efforts to build EEDs. Smokelyze also provides a consistent tool to quantify surface smoke for health researchers, and democratizes complex atmospheric analyses and transforms static academic methodologies into an accessible, rapid verification product.
Abstract When Pressure reached theatres in May 2026, audiences met James Stagg and the forecast that set the date of the Normandy landings. They did not see the two different surface analyses drawn for the Channel that morning, one Allied and one German. Both weather maps caught the same large-scale pattern, and archival work shows the German forecasters knew far more about the North Atlantic than the popular account allows. The gap was finer than that. Only one chart rested on direct observation of the ground immediately upwind of the Channel — Britain and Ireland. Years of Allied effort to deny Germany weather data had produced that gap at exactly the scale the invasion forecast needed. Weather observation was a weapon then, and the gap was the wound. This essay follows the international exchange of observations from the 1947 Convention of the World Meteorological Organization through the Korean War, the International Geophysical Year and the satellite era, and argues that modern forecasting — numerical and machine-learned alike — borrows its resilience from the archive that exchange produced. Some stations that go quiet now go quiet for the same reasons they did in 1944.
Abstract The 2024 Indian heat waves highlighted the vulnerability of urban populations to extreme temperatures and related health risks. Current urban climate modeling practices, aimed at informing on the expected impacts of climate-sensitive designs, often rely on complex, high-resolution simulations that are time-consuming and resource-intensive. These “real case” scenarios, while valuable, may also not explore the full range of adaptive options available to cities. We therefore propose to use simpler, more efficient modeling approaches using “idealized” scenarios to inform early stage decision-making, especially during research and design phases. We demonstrate this by running 100 simulations per common urban climate intervention—including cool roofs, urban greening, or air conditioning—using the WRF building effect parameterization coupled with its building energy model (BEP–BEM) urban climate model. Focusing on the city of Bhubaneswar, Odisha, India, we quantify how incremental one-percentage-point changes in each intervention may affect local temperatures and surface energy balance during the hottest-ever recorded day and the warmest day of 2024 following the wet monsoon period. We then compare outcomes from idealized simulations with a selection of real-case scenarios based on modeled temperature changes to see if idealized simulation outputs are reliable. We show that idealized simulations can deliver clear, actionable insights on how common urban climate interventions can impact the local climate, useful for planners and designers. Limitations from simple two-dimensional simulations are also identified suggesting that our methods should be seen as a first actionable tool for identifying viable adaptation paths that will require further three-dimensional realistic simulations to estimate the expected impacts with greater certainty. Significance Statement Scoping for viable solutions to respond to challenges imposed by global climate changes and local urban climates on urban populations can be difficult. Current modeling practices involve climate models that are complex and use a lot of resources, therefore limiting the amount of “what-if” scenarios that can be run. We hereby propose a framework that employs simplified simulations to inform about viable interventions at the city scale to help design responses to local climate challenges in the most adequate and efficient way. Our study focuses on the city of Bhubaneshwar in the state of Odisha, India, and shows the applicability of this framework in areas that are understudied and critically impacted by climate changes.
