A Climatology of Quasi-Linear Convective Systems and Their Hazards in the United States

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.

Mean storms: Composites of radar reflectivity images during two decades of severe thunderstorm events

“…The results affirm previous work that examined CSM within the CONUS (Smith et al ., 2012; Ashley et al ., 2019). In particular, strong tornadoes were associated with a “Cellular” CSM tendency, whereas weaker tornadoes were associated with “QLCS” or mixed Cellular/QLCS CSM tendency.…”

“…To create this dataset, images centred on the starting location of tornado reports from 1996 to 2017 were manually assigned to one of the aforementioned CSMs . Although the classifications are subjective, we followed the guidance of previous work (e.g., Gallus Jr et al ., 2008; Smith et al ., 2012; Ashley et al ., 2019; Ellis et al ., 2019)—specifically, (a) QLCSs are identified by noting a linear organization of pixels ≥40 dBZ (i.e., at least a 3 to 1 length to width ratio) with a length of at least 100 km, (b) Cellular cases are identified by noting a circular organization to the ≥40 dBZ pixels in the vicinity of the report, and that contiguous circular region is entirely within a 100 × 100 km box around the report, and (c) Tropical cases are those that occurred near a HURDAT track (Landsea et al ., 2015). The reports were gathered from the Southern United States (i.e., Oklahoma, Texas, Arkansas, Louisiana, Mississippi, Tennessee, Alabama, Florida, Georgia, South Carolina, and North Carolina).…”

“…Previous work has shown a strong relationship between CSM and EF‐scale rating (Trapp et al ., 2005; Smith et al ., 2012; Ashley et al ., 2019). Namely, a Cellular CSM is most commonly associated with significant (≥EF2) to violent (≥EF4) tornadoes.…”

“…For example, mean composite frequency of radar‐derived events has been used to explore the relationship between locations with significant human‐made land use modification and thunderstorm frequency (Ashley et al ., 2012; Haberlie et al ., 2015). Additionally, the millions of radar images generated and archived since the 1990s have been a great resource for applied machine learning researchers in the atmospheric and climate sciences (McGovern et al ., 2019), including projects that train machine learning algorithms to identify CSM (e.g., Haberlie and Ashley, 2018; Ashley et al ., 2019; Jergensen et al ., 2020). However, until now, there has been no attempt to create a curated dataset of radar images centred on classifiable “objects.” These types of datasets are common in the field of machine learning, and are widely used for comparing the efficacy of different approaches.…”

A Review of Principal Component Analysis Algorithm for Dimensionality Reduction

“…[8]. Machine learning (ML) encompasses more and more research areas per year, including, for example, bioinformatics [9], [10], biochemistry [11], [12], meteorology of medicine [13]- [16], economics [17]- [19], aquaculture [20], chemo-ecology [21], robotics [22]- [25], and climatology [26], [27].…”