The Spatial Area and Other Attributes of GOES-16 Overshooting Tops as Indicators of Potential Hail.
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| Title: | The Spatial Area and Other Attributes of GOES-16 Overshooting Tops as Indicators of Potential Hail. |
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| Authors: | Christo, Gabrielle1 (AUTHOR) gabbyc26@sbcglobal.net, Trapp, Robert1 (AUTHOR), Nesbitt, Stephen1 (AUTHOR), Di Girolamo, Larry1 (AUTHOR), Wolff, Edward C.1 (AUTHOR), Homeyer, Cameron R.2 (AUTHOR), Hong, Yulan3 (AUTHOR) |
| Source: | Monthly Weather Review. Oct2025, Vol. 153 Issue 10, p2121-2137. 17p. |
| Subjects: | Hailstorms, Risk assessment, Geostationary satellites, Quantitative research, Meteorological precipitation measurement, Forecasting methodology, Convective clouds |
| Geographic Terms: | United States |
| Abstract: | Recent studies using idealized simulations suggest that storms that generate large hail should exhibit deep and wide overshooting tops (OTs). Our work herein extends these and related studies to explore possible relationships between observed OT characteristics and hail size observed at the ground. All hail reports from 2018 through 2022 across the contiguous United States were organized using a grid-hour approach. An OT detection algorithm applied to GOES-16 data was used to find the nearest OT to each selected report. OT area (OTA), OT depth (OTD), and OT volume (OTV) were quantified and statistically related to hail size. OTA tended to exhibit a statistically significant difference across the three hail size groups (nonsevere, severe, and significant severe), with a decrease with increasing hail size. OTD also tended to exhibit a statistically significant difference across the hail size groups, with an increase with increasing hail size. A maximum expected hail size (MESH)-based report proxy was used to explore possible dependencies of these results on hail reports; the general tendencies of increased hail size for decreased OTA and increased OTD were also found using this proxy. Such tendencies were additionally found using a dataset limited to hail associated with supercells. Radar-derived OTA for this limited dataset was also explored and tended to increase with hail size. Finally, possible relationships between hail size and area of a proximal, midtropospheric radar reflectivity core were evaluated and found to be positive and statistically significant. Applications of these findings for risk assessment and operational forecasting are possible but will require further analyses. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Recent studies using idealized simulations suggest that storms that generate large hail should exhibit deep and wide overshooting tops (OTs). Our work herein extends these and related studies to explore possible relationships between observed OT characteristics and hail size observed at the ground. All hail reports from 2018 through 2022 across the contiguous United States were organized using a grid-hour approach. An OT detection algorithm applied to GOES-16 data was used to find the nearest OT to each selected report. OT area (OTA), OT depth (OTD), and OT volume (OTV) were quantified and statistically related to hail size. OTA tended to exhibit a statistically significant difference across the three hail size groups (nonsevere, severe, and significant severe), with a decrease with increasing hail size. OTD also tended to exhibit a statistically significant difference across the hail size groups, with an increase with increasing hail size. A maximum expected hail size (MESH)-based report proxy was used to explore possible dependencies of these results on hail reports; the general tendencies of increased hail size for decreased OTA and increased OTD were also found using this proxy. Such tendencies were additionally found using a dataset limited to hail associated with supercells. Radar-derived OTA for this limited dataset was also explored and tended to increase with hail size. Finally, possible relationships between hail size and area of a proximal, midtropospheric radar reflectivity core were evaluated and found to be positive and statistically significant. Applications of these findings for risk assessment and operational forecasting are possible but will require further analyses. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00270644 |
| DOI: | 10.1175/MWR-D-24-0150.1 |
