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 from
Elevated mixed layers (EMLs) influence the severe convective storm climatology in the contiguous United States (CONUS), playing an important role in the initiation, sustenance, and suppression of storms. This study creates a high-resolution climatology of the EML to analyze variability and potential changes in EML frequency and characteristics for the first time. An objective algorithm is applied to ERA5 to detect EMLs, defined in part as layers of steep lapse rates (≥8.0°C km−1) at least 200 hPa thick, in the CONUS and northern Mexico from 1979 to 2021. EMLs are most frequent over the Great Plains in spring and summer, with a standard deviation of 4–10 EML days per year highlighting sizable interannual variability. Mean convective inhibition associated with the EML’s capping inversion suggests many EMLs prohibit convection, although—like nearly all EML characteristics—there is considerable spread and notable seasonal variability. In the High Plains, statistically significant increases in EML days (4–5 more days per decade) coincide with warmer EML bases and steeper EML lapse rates, driven by warming and drying in the low levels of the western CONUS during the study period. Additionally, increases in EML base temperatures result in significantly more EML-related convective inhibition over the Great Plains, which may continue to have implications for convective storm frequency, intensity, severe perils, and precipitation if this trend persists.
Elevated mixed layers (EMLs) play a role in the spatiotemporal frequency of severe convective storms and precipitation across the contiguous United States and northern Mexico. This research creates a detailed EML climatology from a modern reanalysis dataset to uncover patterns and potential changes in EML frequency and associated meteorological characteristics. EMLs are most common over the Great Plains in spring and summer, but show significant variability year-to-year. Robust increases in the number of days with EMLs have occurred since 1979 across the High Plains. Lapse rates associated with EMLs have trended steeper, in part due to warmer EML base temperatures. This has resulted in increasing EML convective inhibition, which has important implications for regional climate.
- NSF-PAR ID:
- 10490717
- Publisher / Repository:
- American Meteorological Society
- Date Published:
- Journal Name:
- Journal of Climate
- Volume:
- 37
- Issue:
- 5
- ISSN:
- 0894-8755
- Format(s):
- Medium: X Size: p. 1833-1851
- Size(s):
- ["p. 1833-1851"]
- Sponsoring Org:
- National Science Foundation
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