This research quantifies the spatiotemporal statistics of composite radar reflectivity in the vicinity of severe thunderstorm reports. By using over 20 years (1996–2017) of data and 500,000 severe thunderstorm reports, this study presents the most comprehensive analysis of the mesoscale presentation of radar reflectivity composites during severe weather events to date. We first present probability matched mean composites of approximately 5,000 radar images centred on tornado reports that contain one of three types of manually‐labelled convective storm modes—namely, (a) quasi‐linear convective system (QLCS); (b) cellular; or (c) tropical system. Next, we generate composites for tornado report data stratified by EF‐scale and for four temporal periods during which notable severe weather events took place. The data are then stratified by hazard, region, season, and time of day. The results show marked spatiotemporal and intra‐hazard variability in radar presentation. In general, cellular convection is favoured in the Great Plains of the United States, whereas QLCS convection is favoured in the Southeast United States. Night and cool‐season subsets showed a preference for QLCS convection, whereas day and warm‐season subsets showed a preference for cellular convection. These results agree well with the existing literature and suggest that the data extraction and organization approach is sound. Because of this, these data will be useful for future image classification studies in climate and atmospheric sciences—particularly those involving storm mode classification.
This research assesses the utility and validity of using simulated radar reflectivity to detect potential changes in linear and nonlinear mesoscale convective system (MCS) occurrence in the Midwest United States between the early and late 21st century using convection‐permitting climate simulation output. These data include a control run and a pseudo‐global warming (PGW) run that is based on RCP 8.5. First, using a novel segmentation, classification, and tracking procedure, MCS tracks are extracted from observed and simulated radar reflectivity. Next, a comparison between observed and the control run MCS statistics is performed, which finds a negative summertime bias that agrees with previous work. Using a convolutional neural network to perform probabilistic predictions, the MCS dataset is further stratified into highly organized, quasi‐linear convective systems (QLCSs)—which can include bow echoes, squall lines, and line echo wave patterns—and generally less‐organized, non‐QLCS events. The morphologically stratified data reveal that the negative MCS bias in this region is largely driven by too few QLCSs. Although comparisons between the control run and a PGW run suggest that all MCS events are less common in the future (including QLCS and non‐QLCS events), these changes are not spatially significant, whereas the biases between the control run and observations are spatially significant. A discussion on the importance and challenges of simulating QLCSs in convection‐permitting climate model runs is provided. Finally, potential avenues of exploration are suggested related to the aforementioned issues.
more » « less- Award ID(s):
- 1637225
- NSF-PAR ID:
- 10462857
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- International Journal of Climatology
- Volume:
- 39
- Issue:
- 2
- ISSN:
- 0899-8418
- Page Range / eLocation ID:
- p. 1144-1153
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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