This study assesses the impact of urban land use on the climatological distribution of thunderstorm initiation occurrences in the humid subtropical region of the southeast United States, which includes the Atlanta, Georgia metropolitan area. Initially, an automated technique is developed to extract the locations of isolated convective initiation (ICI) events from 17 years (1997-2013) of composite reflectivity radar data for the study area. Nearly 26 000 ICI points were detected during 85 warm-season months, providing the foundation for first long-term, systematic assessment of the influence of urban land use on thunderstorm development. Results reveal that ICI events occur more often over the urban area compared to its surrounding rural counterparts, confirming that anthropogenic-induced changes in land cover in moist tropical environments lead to more initiation events, resulting thunderstorms and affiliated hazards over the developed area. The ICI risk for Atlanta is greatest during the late afternoon and early evening in July and August in synoptically benign conditions. Greater ICI counts downwind of Atlanta suggest that prevailing wind direction also influences the location of these events. Moreover, ICI occurrences over the city were significantly higher on weekdays compared to weekend days -a result that was not apparent in a rural control region located west of the city. This suggests that the weekly commuting cycle and associated aerosol levels of Atlanta may amplify ICI rates. The investigation provides a methodological framework for future studies that examine the effect of land use, land cover, and terrain discontinuities on the spatio-temporal character of ICI events. A. M. Haberlie et al.a geostatistical analysis using an unprecedented sample size of ICI events, while also creating an experimental framework that permits a comparison of ICI activity around Atlanta to a rural control region (Lowry, 1998;Ashley et al, 2012). Here, we present the first objective, long-term climatology of land-use induced ICI events for Atlanta and the surrounding region.

Visibility-related weather hazards have significant impacts on motor vehicle operators because of decreased driver vision, reduced roadway speed, amplified speed variability, and elevated crash risk. This research presents a national analysis of fog-, smoke-, and dust storm–associated vehicular fatalities in the United States. Initially, a database of weather-related motor vehicle crash fatalities from 1994 to 2011 is constructed from National Highway Traffic Safety Administration data. Thereafter, spatiotemporal analyses of visibility-related (crashes where a vision hazard was reported at time of event) and vision-obscured (driver’s vision was recorded as obscured by weather, and a weather-related vision hazard was reported) fatal vehicular crashes are presented. Results reveal that the annual number of fatalities associated with weather-related, vision-obscured vehicular crashes is comparable to those of more notable and captivating hazards such as tornadoes, floods, tropical cyclones, and lightning. The majority of these vision-obscured crash fatalities occurred in fog, on state and U.S. numbered highways, during the cool season and during the morning commuting hours of 0500 to 0800 local time. Areas that experience the greatest frequencies of vision-obscured fatal crashes are located in the Central Valley of California, Appalachian Mountain and mid-Atlantic region, the Midwest, and along the Gulf Coast. From 2007 to 2011, 72% of all vision-obscured fatal crashes occurred when there was no National Weather Service weather-related visibility advisory in effect. The deadliest weather-related visibility hazard crashes during the period are exhibited, revealing a spectrum of environmental and geographical settings that can trigger these high-end events.

This research applies an automated mesoscale convective system (MCS) segmentation, classification, and tracking approach to composite radar reflectivity mosaic images that cover the contiguous United States (CONUS) and span a relatively long study period of 22 years (1996–2017). These data afford a novel assessment of the seasonal and interannual variability of MCSs. Additionally, hourly precipitation data from 16 of those years (2002–17) are used to systematically examine rainfall associated with radar-derived MCS events. The attributes and occurrence of MCSs that pass over portions of the CONUS east of the Continental Divide (ECONUS), as well as five author-defined subregions—North Plains, High Plains, Corn Belt, Northeast, and Mid-South—are also examined. The results illustrate two preferred regions for MCS activity in the ECONUS: 1) the Mid-South and Gulf Coast and 2) the Central Plains and Midwest. MCS occurrence and MCS rainfall display a marked seasonal cycle, with most of the regions experiencing these events primarily during the warm season (May–August). Additionally, MCS rainfall was responsible for over 50% of annual and seasonal rainfall for many locations in the ECONUS. Of particular importance, the majority of warm-season rainfall for regions with high agricultural land use (Corn Belt) and important aquifer recharge properties (High Plains) is attributable to MCSs. These results reaffirm that MCSs are a significant aspect of the ECONUS hydroclimate.

A supercell is a distinct type of intense, long-lived thunderstorm that is defined by its quasi-steady, rotating updraft. Supercells are responsible for most damaging hail and deadly tornadoes, causing billions of dollars in losses and hundreds of casualties annually. This research uses high-resolution, convection-permitting climate simulations across 15-yr epochs that span the 21st century to assess how supercells may change across the United States. Specifically, the study explores how late 20th century supercell populations compare with their late 21st century counterparts for two—intermediate and pessimistic—anthropogenic climate change trajectories. An algorithm identifies, segments, and tracks supercells in the simulation output using updraft helicity, which measures the magnitude of corkscrew flow through a storm’s updraft and is a common proxy for supercells. Results reveal that supercells will be more frequent and intense in future climates, with robust spatiotemporal shifts in their populations. Supercells are projected to become more numerous in regions of the eastern United States, while decreasing in frequency in portions of the Great Plains. Supercell risk is expected to escalate outside of the traditional severe storm season, with supercells and their perils likely to increase in late winter and early spring months under both emissions scenarios. Conversely, the latter part of the severe storm season may be curtailed, with supercells expected to decrease midsummer through early fall. These results suggest the potential for more significant tornadoes, hail, and extreme rainfall that, when combined with an increasingly vulnerable society, may produce disastrous consequences.

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. K E Y W O R D S climate modeling, mesoscale convective systems, convolutional neural network