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1. Rising global hail damage potential in a warming world

   https://doi.org/10.1038/s41586-026-10543-2  

   Zhang, Shiyi; Zhang, Qinghong; Allen, John T; Lin, Xiangyu (May 2026, Nature)

   Anthropogenic climate change (ACC) is expected to modify severe convective storms and their associated hazards, including hailstorms, a primary driver of weather-related economic losses1,2,3,4. Despite some research on the response of hailstorms to ACC, most studies have focused on regional-scale changes2,3,4,5,6,7,8,9, whereas global-scale assessments of hailstone size remain scarce. Here we show a 36.5–42.1% increase in global hailstorm-induced damage potential by the late twenty-first century, with the magnitude determined by the emission scenario. Our results arise from hailstone trajectory simulations conducted under historical and future scenarios, driven by EC-Earth3 ensemble outputs10 that are cross-validated through multimodel comparisons. Globally, increased low-level temperature and specific humidity drive a shift towards larger hailstones, with the frequency of ≥30-mm-diameter hailstones rising by 37.9–51.8% and <30-mm-diameter hailstones declining by 4.2–12.3%. Regionally, the mid-high latitudes predominantly exhibit increased hail damage potential owing to strong warming and weak moistening, amplifying instability sufficiently to counteract enhanced drag and melting effects. Conversely, tropical and monsoonal regions experience reduced hail damage potential owing to weak warming, strong moistening and limited hail growth depth. Our findings highlight the non-uniform impacts of ACC on global hailstorm damage, providing critical insights for future disaster prevention and mitigation strategies. 

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2. Meteorological Benefits and Operational Applications of S-Band, Rapid-Scanning, Dual-Polarization Radar for Hail-Producing Storms

   https://doi.org/10.1175/WAF-D-25-0089.1  

   Shedd, Laura; Bodine, David; Reinhart, Anthony; Schvartzman, David; Snyder, Jeffrey (April 2026, Weather and Forecasting)

   Abstract Most operational radar systems to date produce data with volumetric update times that are insufficient for capturing some of the rapidly evolving processes of hailstorms. Recent advances in rapid scan radar technology and innovative approaches to obtain faster scans have paved the way for a better analysis of hailstorms. This study utilizes the National Severe Storms Laboratory (NSSL)’s research Weather Surveillance Radar-1988 Doppler (WSR-88D) S-band radar, KOUN, located in Norman, Oklahoma, to examine rapidly evolving polarimetric features in hailstorms. Quantities such as rate of hail descent and changes in the updraft can be calculated through the analysis of these rapidly evolving polarimetric features, which can increase forecaster confidence in hailstorm evolution and hail sizing. Multi-Radar Multi-Sensor (MRMS) system products were generated using KOUN data, which captured the full life cycle of a hailstorm, including the cyclic evolution of hail production and its relation to tornado production. These data were compared to KTLX, located in Oklahoma City, Oklahoma, to explore what features would not be resolved without a rapid-scanning radar system. Rapid-scanning radar observations provided more details on storm-scale processes related to hail, which can aid in operational forecasting and give insight into future operational phased array radar benefits. 

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3. Projecting End-of-Century Human Exposure from Tornadoes and Severe Hailstorms in Eastern Colorado: Meteorological and Population Perspectives

   https://doi.org/10.1175/WCAS-D-19-0153.1  

   Childs, Samuel J.; Schumacher, Russ S.; Strader, Stephen M. (July 2020, Weather, Climate, and Society)

   Abstract Severe convective storms along the Front Range and eastern plains of Colorado frequently produce tornadoes and hail, leading to substantial damage and crop losses annually. Determination of future human exposure from these events must consider both changes in meteorological conditions and population dynamics. Projections of EF0 + tornadoes (on the enhanced Fujita scale) and severe [1.0+ in. (25.4+ mm)] hail reports out to the year 2100 are computed using convective parameter proxies generated from dynamically downscaled GFDL Climate Model, version 3 (GFDL CM3), output by the WRF Model for control and future climate scenarios. The proxies suggest that tornado and hail days in the region may increase by up to one tornado day and three hail days per year by 2100, with the greatest increases across northeastern Colorado. Using a spatially explicit Monte Carlo model, projected future frequency and spatial changes in tornadoes and hail are superimposed with population projections from the shared socioeconomic pathways (SSPs) to provide a range of possible scenarios for end-of-century human exposure to tornadoes and hailstorms. Changes in hazard frequency and spatial distribution may amplify human exposure up to 117% for tornadoes and 178% for hail in the region by 2100, although specific results are sensitive to uncertain combinations of future overlaps between hazard spatial distribution and population. Findings presented herein not only will provide the public, insurers, policy makers, land-use planners, and researchers with estimates of potential future tornado and hail impacts in the Front Range region, they also will allow the weather enterprise to better understand, prepare for, and communicate tornado and hail risk to eastern Colorado communities. 

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4. Modeling Nonspherical Hailstones

   https://doi.org/10.1175/JAS-D-23-0231.1  

   Lin, Yuzhu; Kumjian, Matthew R; Soderholm, Joshua; Giammanco, Ian (November 2024, Journal of the Atmospheric Sciences)

   Abstract Hail research and forecasting models necessarily involve explicit or implicit—and uncertain—physical assumptions regarding hailstones’ shape, tumbling behavior, fall speed, and thermal energy transfer. Whereas most models assume spherical hailstones, we relax this assumption by using hailstone shape data from field observations to establish empirical size–shape relationships with reasonable degrees of randomness considering hailstones’ natural shape variability, capturing the observed distribution of triaxial ellipsoidal shapes. We also incorporate explicit, random tumbling of individual hailstones during their growth to simulate their free-falling behavior and the resultant changes in cross-sectional area (which affects growth by hydrometeor collection). These physical attributes are incorporated in calculating hailstones’ fall speeds, using either empirical relationships or analytical relationships based on each hailstone’s Best and Reynolds numbers. Options for drag coefficient modification are added to emulate hailstones’ rough surfaces (lobes), which then modifies their thermal energy and vapor exchange with the environment. We investigate how applying these physical assumptions about nonspherical hail to the Penn State hail growth trajectory model, coupled with Cloud Model 1 supercell simulations, impacts hail production and examine the reasons behind the resulting variability in hail statistics. The choice of hailstone size–mass relation and fall speed scheme have the strongest influence on hail sizes. Using nonspherical, tumbling hailstones reduces the number of large hailstones produced. Applying shape-specific thermal energy transfer coefficients subtly increases sizes; the effects of lobes vary depending on the fall speed scheme used. These physical assumptions, although adding complexity to modeling, can be parameterized efficiently and potentially used in microphysics schemes. Significance StatementIn numerical modeling of hailstorms, we usually consider hailstones to be spherical to simplify calculations, but in nature, hailstones generally are not spheres and can be rather lumpy and have spikes. The purpose of this study is to examine how the model result would change when nonspherical hailstone shape is implemented. We examine the relationship between hailstone shape and physical processes during hail growth in effort to explain why these changes occur and offer insights on how nonspherical hailstone shape may be parameterized in bulk microphysics schemes. 

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5. Quantifying hail size distributions from the sky &#8211; application of drone aerial photogrammetry

   https://doi.org/10.5194/amt-13-747-2020  

   Soderholm, Joshua S.; Kumjian, Matthew R.; McCarthy, Nicholas; Maldonado, Paula; Wang, Minzheng (January 2020, Atmospheric Measurement Techniques)

   Abstract. A new technique, named “HailPixel”, is introduced for measuring the maximum dimension and intermediate dimension of hailstones from aerial imagery. The photogrammetry procedure applies a convolutional neural network for robust detection of hailstones against complex backgrounds and an edge detection method for measuring the shape of identified hailstones. This semi-automated technique is capable of measuring many thousands of hailstones within a single survey, which is several orders of magnitude larger (e.g. 10 000 or more hailstones) than population sizes from existing sensors (e.g. a hail pad). Comparison with a co-located hail pad for an Argentinian hailstorm event during the RELAMPAGO project demonstrates the larger population size of the HailPixel survey significantly improves the shape and tails of the observed hail size distribution. When hail fall is sparse, such as during large and giant hail events, the large survey area of this technique is especially advantageous for resolving the hail size distribution. 

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