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Abstract Geostationary satellite imagers provide historical and near-real-time observations of cloud-top patterns that are commonly associated with severe convection. Environmental conditions favorable for severe weather are thought to be represented well by reanalyses. Predicting exactly where convection and costly storm hazards like hail will occur using models or satellite imagery alone, however, is extremely challenging. The multivariate combination of satellite-observed cloud patterns with reanalysis environmental parameters, linked to Next Generation Weather Radar (NEXRAD) estimated maximum expected size of hail (MESH) using a deep neural network (DNN), enables estimation of potentially severe hail likelihood for any observed storm cell. These estimates are made where satellites observe cold clouds, indicative of convection, located in favorable storm environments. We seek an approach that can be used to estimate climatological hailstorm frequency and risk throughout the historical satellite data record. Statistical distributions of convective parameters from satellite and reanalysis show separation between nonsevere and severe hailstorm classes for predictors that include overshooting cloud-top temperature and area characteristics, vertical wind shear, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN to produce a likelihood estimate with a critical success index of 0.511 and Heidke skill score of 0.407, which is exceptional among analogous hail studies. Furthermore, applications of the DNN to case studies demonstrate good qualitative agreement between hail likelihood and MESH. These hail classifications are aggregated across an 11-yr Geostationary Operational Environmental Satellite (GOES) image database fromGOES-12/13to derive a hail frequency and severity climatology, which denotes the central Great Plains, the Midwest, and northwestern Mexico as being the most hail-prone regions within the domain studied. 

Abstract Hailstorms in subtropical South America are known to be some of the most frequent anywhere in the world, causing significant damage to the local agricultural economy every year. Convection in this region tends to be orographically forced, with moisture supplied from the Amazon rain forest by the South American low-level jet. Previous climatologies of hailstorms in this region have been limited to localized and sparse observational networks. Because of the lack of sufficient ground-based radar coverage, objective radar-derived hail climatologies have also not been produced for this region. As a result, this study uses a 16-yr dataset of TRMM Precipitation Radar and Microwave Imager observations to identify possible hailstorms remotely, using 37-GHz brightness temperature as a hail proxy. By combining satellite instruments and ERA-Interim reanalysis data, this study produces the first objective study of hailstorms in this region. Hailstorms in subtropical South America have an extended diurnal cycle, often occurring in the overnight hours. In addition, they tend to be multicellular in nature, rather than discrete. High-probability hailstorms (≥50% probability of containing hail) tend to be deeper by 1–2 km and horizontally larger by greater than 15 000 km2 than storms having a low probability of containing hail (<25% probability of containing hail). Hailstorms are supported synoptically by strong upper- and lower-level jets, anomalously warm and moist low levels, and enhanced instability. The findings of this study will support the forecasting of these severe storms and mitigation of their damage within this region.