Observations of the 14 July 2011 Fort Collins Hailstorm: Implications for WSR-88D-Based Hail Detection and Warnings

Abstract

The issuance of timely warnings for the occurrence of severe-class hail (hailstone diameters of 2.5 cm or larger) remains an ongoing challenge for operational forecasters. This study examines the application of two remotely sensed data sources between 0100 and 0400 UTC 14 July 2011 when pulse-type severe thunderstorms occurred in the jurisdiction of the Denver/Boulder National Weather Service (NWS) Forecast Office in Colorado. First, a developing hailstorm was jointly observed by the dual-polarization Colorado State University-University of Chicago-Illinois State Water Survey (CSU-CHILL) research radar and by the operational, single-polarization NWS radar at Denver/Front Range (KFTG). During the time period leading up to the issuance of the initial severe thunderstorm warning, the dual-polarization radar data near the 0 8C altitude contained a positive differential reflectivity Z DR column (indicating a strong updraft lofting supercooled raindrops above the freezing level). Correlation coefficient r HV reductions to ;0.93, probably due to the presence of growing hailstones, were observed above the freezing level in portions of the developing .55-dBZ echo core. Second, data from the National Lightning Detection Network (NLDN), including the locations and polarity of cloud-to-ground (CG) discharges produced by several of the evening's storms, were processed. Some association was found between the prevalence of positive CGs and storms that produced severe hail. The analyses indicate that the use of the dual-polarization data provided by the upgraded Weather Surveillance Radar-1988 Doppler (WSR-88D), in combination with the NLDN data stream, can assist operational forecasters in the real-time identification of thunderstorms that pose a severe hail threat.

Colorado Plowable Hailstorms: Synoptic Weather, Radar, and Lightning Characteristics

“…50.8 mm) is typically characterized by Z DR , 20.5 dB . The Z DR measurements of large hail (25.4 # d # 50.8 mm) are near 0 dB (Balakrishnan and Zrnić 1990b;Kumjian and Ryzhkov 2008;Snyder et al 2010;Kennedy et al 2014), as a result of the tumbling nature of hailstones (Lesins and List 1986;Herzegh and Jameson 1992). Finally, small (d , 25.4 mm), wet hail has Z DR .…”

Explicit prediction of hail using multimoment microphysics schemes for a hailstorm of 19 March 2014 in eastern China

“…Many previous studies have noted that a high concentration of supercooled liquid water in the updraft is required for vigorous hail growth, and in some areas of the storm, they even form accumulation zones [e.g., Foote, 1984;Bringi et al, 1996;Kennedy et al, 2014]. In the initial stage of the storm, large quantities of small rain drops are transported as high as 8 km (x~1155 km), with N tr = 10-10 3 m À3 (Figure 13a).…”

“…Most existing studies on hailstorms have been based on either observations or idealized laboratory or numerical experiments [e.g., Cheng and English , ; Heymsfield , ; Guo and Huang , ; Kennedy et al ., ; Fernández‐González et al ., ]; relatively few studies compare simulated microphysical fields to observed hail events. Given the reasonable resemblance of the model‐predicted MESH and reflectivity fields for the Taizhou hailstorms to radar observations, we will further examine hail‐related microphysical fields and rain PSDs during different stages of the hailstorm development.…”

A Review of Machine Learning for Convective Weather

“…Ensuring that the subgrid physics parameterizations remain realistic as model resolution increases is challenging (e.g., Morrison et al 2020;Angevine et al 2020). New sensing capabilities, such as the shift in the national radar network to dual polarization (Kennedy et al 2014;Loeffler and Kumjian 2018;Kuster et al 2019), can improve assimilation and prediction of thunderstorms (e.g., Putnam et al 2021), but these improvements will be constrained by model limitations. Optimizing the prediction of thunderstorms and their hazards requires postprocessing techniques that leverage previously learned statistical relationships between observations and NWP output and convective events.…”