Creating high-resolution hail datasets using social media and post ...

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Eight hail stones, identified from the NWS post-storm ground survey and the search through social media-based web pages, exceeded the previous Kansas record diameter stone of 144 mm (5.67 in.). The residents that discovered the Wichita stone uploaded an image of the stone to one of the local television station’s social media-based photo forums, and the station promptly shared the information with the NWS ICT during the ongoing severe weather event. It is worthy to note how important of a role such technologies served to document hailstones of this magnitude. While giant hail is a relatively rare phenomenon based on records in Storm Data, it has likely been underreported in the past (Blair et al. 2011). Table 1 reveals that the seven largest verifiable United States hailstones have occurred since 2003. Thus, it is reasonable to conclude more high-end hail events will continue to be documented as photo-sharing technologies become more widespread and utilized by the public and the meteorological community. Figure 8. Inverse distance weighted interpolation from 464 hail data points. Color-filled contours correspond to one-inch increments of hail size. Urban areas (black outline) of the Wichita, Kansas, metropolitan area, primary Interstates (red lines), and state highways (blue lines) are shown for reference. 4. Summary A high-resolution hail dataset was created from a prolific giant hail-producing supercell that impacted portions of the Wichita, Kansas, metropolitan area on 15 September 2010. The storm provided a unique opportunity to create very highly-spatially-resolved information within 43
an urban and rural landscape, derived from supplemental hail data not available from Storm Data. The three main sources of hail data were NWS LSRs, a post-storm ground survey, and social media-related web sites. A total of 464 hail data points were collected and incorporated into the database, with 83% of the reports stemming from social media web sites. Approximately 93% of the data points had accompanying photos of the maximum diameter hailstones, mitigating some of the size errors and biases associated with typical public-based hail reports. The NWS post-storm ground survey and social media data uncovered several useful findings. The survey found that approximately 80% of the sampled residents preserved hailstones in freezers, primarily for insurance purposes. Additionally, the survey showed the challenge of receiving hail reports from the public. None of the surveyed residents reported hail to the NWS, while approximately 7% contacted local authorities or the media. While this illustrates the challenge of receiving hail reports using traditional verification methods even when extraordinarily large hailstones occur in urban areas, the survey showed that social media outlets, especially those operated by local television stations, can be a significant source for obtaining additional severe weather reports. Lastly, the post-storm ground survey and searches through the Wichita-based social media web pages identified eight hail stones that exceeded the previous Kansas record hail size diameter. Growing photo and video technologies and the usage of social media are allowing the operational and research communities to document meteorological events that would have gone unreported in the past. These data provide a meaningful contribution to the science, and are presumably available for most other notable weather phenomena. High-resolution hail data are critical to accurately represent the hail-fall behavior in a storm, in order to discriminate sensitive storm-scale changes and determine the utility of radar-based hail signatures. It is hoped that this type of supplemental hail data will be incorporated into Storm Data to improve the national hail climatology database, to support additional research efforts in hail size prediction utilizing dualpolarization radar products, and in the development and refinement of new hail-related algorithms. Acknowledgements. The authors wish to recognize Jennifer Laflin (NWS Sioux Falls, SD), George Phillips (NWS Topeka, KS), Albert Pietrycha (NWS Goodland, KS), Derek Deroche (NWS Pleasant Hill, MO), Amos Magliocco (University of North Texas), Jeff Manion (NWS Central Region Headquarters), and two anonymous NWA reviewers for their role in improving the manuscript through technical editing. We would also like to thank Ken Cook and Chance Hayes (NWS Wichita, KS) for their assistance during the post-storm ground survey. Lastly, the authors thank the 2011 HailSTONE participants for their involvement in collecting data used in Fig. 1. The views expressed within this manuscript are those of the authors and do not necessarily represent those of the National Weather Service. REFERENCES Amburn, S. A. and P. L. Wolf, 1997: VIL density as a hail indicator. Wea. Forecasting, 12, 473– 478. Blair, S. F., D. R. Deroche, J. M. Boustead, J. W. Leighton, B. L. Barjenbruch, and W. P. Gargan, 2011: A radar-based assessment of the detectability of giant hail. Electronic J. Severe Storms Meteor., 6 (7), 1–30. 44
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