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Abstract This research uses image classification and machine learning methods on radar reflectivity mosaics to segment, classify, and track quasi-linear convective systems (QLCSs) in the United States for a 22-yr period. An algorithm is trained and validated using radar-derived spatial and intensity information from thousands of manually labeled QLCS and non-QLCS event slices. The algorithm is then used to automate the identification and tracking of over 3000 QLCSs with high accuracy, affording the first, systematic, long-term climatology of QLCSs. Convective regions determined by the procedure to be QLCSs are used as foci for spatiotemporal filtering of observed severe thunderstorm reports; this permits an estimation of the number of severe storm hazards due to this morphology. Results reveal that nearly 32% of MCSs are classified as QLCSs. On average, 139 QLCSs occur annually, with most of these events clustered from April through August in the eastern Great Plains and central/lower Mississippi and Ohio River Valleys. QLCSs are responsible for a spatiotemporally variable proportion of severe hazard reports, with a maximum in QLCS-report attribution (30%–42%) in the western Ohio and central Mississippi River Valleys. Over 21% of tornadoes, 28% of severe winds, and 10% of severe hail reports are due to QLCSs across the central and eastern United States. The proportion of QLCS-affiliated tornado and severe wind reports maximize during the overnight and cool season, with more than 50% of tornadoes and wind reports in some locations due to QLCSs. This research illustrates the utility of automated storm-mode classification systems in generating extensive, systematic climatologies of phenomena, reducing the need for time-consuming and spatiotemporal-limiting methods where investigators manually assign morphological classifications.
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On Discrete Convective Updrafts and Tornadoes in Quasi-Linear Convective Systems
Abstract This research attempts to use operational radar and satellite products to identify potential locations of quasi-linear convective system (QLCS) tornadogenesis, which can be difficult to predict. It is hypothesized that deep, discrete updrafts indicate portions of the QLCS capable of producing tornadoes, whereas shallower convection indicates more benign portions of the QLCS. To address this hypothesis, storm reports and storm surveys on 30–31 March 2022, during the second intensive observing period of the 2022 Propagation, Evolution, and Rotation in Linear Storms (PERiLS) field campaign, are used to identify locations of tornadoes within the QLCS. These tornado locations are then compared to representations of upper-tropospheric updrafts, namely, overshooting tops (OTs), which are identified with an algorithm using 1-min-resolution mesoscale sector data fromGOES-16Advanced Baseline Imager infrared brightness temperatures, and radar reflectivity cores aloft, identified with Multi-Radar Multi-Sensor (MRMS) 3D mosaic reflectivity products. Only a fraction (less than 30%) of tornadoes within the QLCS are associated with OTs, though over 85% of tornadoes are located near convective cores as indicated by cores of enhanced reflectivity at 9 km MSL. A numerical simulation of the event is also conducted using the Weather Research and Forecasting (WRF) Model which shows a strong relationship between simulated updraft intensity and reflectivity aloft. Given this apparent support of the hypothesis, the identification of updraft signatures within MRMS and high-resolution geostationary satellite data may ultimately help improve the identification of regions within QLCSs most likely to result in tornadoes.
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- Award ID(s):
- 2020462
- PAR ID:
- 10609285
- Publisher / Repository:
- American Meteorological Society
- Date Published:
- Journal Name:
- Weather and Forecasting
- Volume:
- 40
- Issue:
- 7
- ISSN:
- 0882-8156
- Format(s):
- Medium: X Size: p. 1065-1083
- Size(s):
- p. 1065-1083
- Sponsoring Org:
- National Science Foundation
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