Vertically integrated liquid

Abstract: Through the use of digital radar data measured at successive elevation angles in a storm system, we developed a technique that presents a new dimension in mesoscale analysis. This technique, mapped vertically integrated liquid-water content (VIL), presents the three-dimensional characteristics of a storm system in a two-dimensional display. This analysis technique appears to hold real promise for both severe storm and hydrologic applications.

Abstract: This study tests hypothetical correspondences between size of severe hail, WSR-88D derived vertically integrated liquid water (VIL), and an array of thermodynamic variables derived from computationally modified sounding analyses. In addition, these associations are documented for normalized VIL using various sounding parameters, and statistical predictive value is assigned to the various VIL-based and sounding variables. The database was gathered from Weather Service Radar-1988 Doppler (WSR-88D) units nationwide from cases identified during real-time operations and consists of over 400 hail events, each associated with a radar-observed VIL value and a modified observational sounding. Some parameters are found to increase in the mean with larger hail-size categories. Specific hail size, however, varies widely across the spectra of VIL, thermodynamic sounding variables, and combinations thereof, with only a few exceptions. No operationally useful parameters of value in hail-size prediction were discovered in the database of VIL and thermodynamic sounding data. These largely antihypothetical findings are compared with hail forecasting and warning techniques developed in the WSR-88D era—few in number and mostly regionalized and informal in nature—and with more widespread and empirical forecasting assumptions involving many of the same variables.

Abstract: The occurrence of giant hail, defined as hail ≥102 mm (4.00 in) in diameter, is a relatively rare phenomenon, accounting for <1% of all hail reports in the United States. Despite the infrequent nature of these events, hail of this magnitude has the potential to cause extreme damage to property and a substantial threat to exposed life. The short-term prediction of these events has been challenging. For giant hail since 2005, only 7% of convective warnings and severe-weather statements issued by the National Weather Service (NWS) accurately predicted a maximum hail size ≥102 mm prior to the report, with an average underestimated size error of 55.6 mm (2.19 in). The objectives of this study are to determine the detectability of giant hail in convective storms and to improve advanced recognition of these events during NWS warning operations. A total of 568 giant-hail reports, gathered over a 15-y period from 1 January 1995 through 31 December 2009 throughout the contiguous United States, served as the primary database for the research. Weather Surveillance Radar1988 Doppler (WSR-88D) data and North American Regional Reanalysis (NARR) environmental data were collected for each case. Several radar signatures were examined to assess their utility in discriminating storms most favorable for giant hail. It was found that 99% of the storms were supercells with well-organized structure. Giant-hail producing storms were characterized by median values of rotational velocities of 24 m s (47 kt), storm-top divergence magnitudes of 72 m s (140 kt), and 50-dBZ and 60-dBZ echo heights of 13 100 m (43 000 ft) and 10 600 m (34 800 ft) respectively. Vertically integrated liquid water (VIL)-based products, maximum reflectivity within the storm, and reflectivity within the preferred hail-growth zone showed little to no skill in discriminating between giant hail and smaller hail sizes. –––––––––––––––––––––––

Abstract: Nowcasting of hailstorms still poses a major challenge to weather services, because of the limited availability of reliable large datasets and the short spatio‐temporal scales involved. Two novel Eulerian and Lagrangian hail climatologies for the Alps are applied to address important aspects of hailstorms in the Alps: the diurnal cycle, their spatio‐temporal development and the lightning properties. The database contains more than 100,000 ordinary and 30,000 hail storms (2002–2017). Based on that large sample of storms, the diurnal cycle of storm initiation and evolution is studied in the context of orographic forcing and cold‐front occurrence statistics. Results show that, during daytime, storms mainly initiate over the foothills (Prealps) and move towards areas with higher terrain elevations. During night‐time, the storms preferably move from the foothills to the plains. Five out of 16 years of the radar‐derived convective storms show a significant yearly positive hail anomaly, from which two years show relative hail‐initiation maxima evenly distributed over the 24 hour without a characteristic diurnal cycle. Relative hail maxima during night‐time cannot always be explained with a higher occurrence of cold fronts. Time series of storm vertically integrated liquid water content are used to separate ordinary and hail storm development. Differences are found between vertically integrated liquid and its density in cold air‐mass storms. Finally, lightning data from a ground‐based network are combined with the radar‐derived hailstreaks and evaluated with respect to their prediction skill as a function of lead time (flash rate, density, peak current, lightning jumps). Results show that lightning data provide only modest skill‐scores in nowcasting hailstorms. Only the sudden increase in lightning rate (referred to as lightning jump) may be used as additional data for hailstorm nowcasting. However, their application in automatic nowcasting systems remains challenging as the lightning jumps occurs at various lead times in the series.