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Weather Radar Network Benefit Model for Tornadoes

Author(s)
Cho, John Y; Kurdzo, James M.
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.

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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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Abstract
A monetized tornado benefit model is developed for arbitrary weather radar network configurations. Geospatial regression analyses indicate that improvement of two key radar parameters—fraction of vertical space observed and cross-range horizontal resolution—leads to better tornado warning performance as characterized by tornado detection probability and false-alarm ratio. Previous experimental results showing faster volume scan rates yielding greater warning performance are also incorporated into the model. Enhanced tornado warning performance, in turn, reduces casualty rates. In addition, lower false-alarm ratios save costs by cutting down on work and personal time lost while taking shelter. The model is run on the existing contiguous U.S. weather radar network as well as hypothetical future configurations. Results show that the current radars provide a tornado-based benefit of ~$490 million (M) yr⁻¹. The remaining benefit pool is about $260M yr⁻¹, split roughly evenly between coverage- and rapid-scanning-related gaps. Keywords: Tornadoes; Radars/Radar observations; Statistical techniques; Economic value; Geographic information systems (GIS); Societal impacts
Date issued
2019-05
URI
https://hdl.handle.net/1721.1/124005
Department
Lincoln Laboratory
Journal
Journal of Applied Meteorology and Climatology
Publisher
American Meteorological Society
Citation
Cho, John Y. N. and Kurdzo, James M. "Weather Radar Network Benefit Model for Tornadoes ." Journal of Applied Meteorology and Climatology 58, 5 (May 2019) © 2019 American Meteorological Society
Version: Final published version
ISSN
1558-8424
1558-8432

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