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    A Climatology of Operational Storm-Based Warnings: A Geospatial Analysis 

    Source: Weather and Forecasting:;2016:;volume( 032 ):;issue: 001:;page 47
    Author(s): Harrison, David R.; Karstens, Christopher D.
    Publisher: American Meteorological Society
    Abstract: his study provides a quantitative climatological analysis of the fundamental geospatial components of storm-based warnings and offers insight into how the National Weather Service (NWS) uses the current storm-based warning ...
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    Storm Evader: Using an iPad to Teach Kids about Meteorology and Technology 

    Source: Bulletin of the American Meteorological Society:;2014:;volume( 096 ):;issue: 003:;page 397
    Author(s): McGovern, Amy; Balfour, Andrea; Beene, Marissa; Harrison, David
    Publisher: American Meteorological Society
    Abstract: e have developed and released an iPad application, Storm Evader, to demonstrate to youth how technology can be used as a tool and to teach youth about weather and its impact on real-world activities, including flying. As ...
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    A Machine Learning Tutorial for Operational Meteorology. Part II: Neural Networks and Deep Learning 

    Source: Weather and Forecasting:;2023:;volume( 038 ):;issue: 008:;page 1271
    Author(s): Chase, Randy J.; Harrison, David R.; Lackmann, Gary M.; McGovern, Amy
    Publisher: American Meteorological Society
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    Development of a Human–Machine Mix for Forecasting Severe Convective Events 

    Source: Weather and Forecasting:;2018:;volume 033:;issue 003:;page 715
    Author(s): Karstens, Christopher D.; Correia, James; LaDue, Daphne S.; Wolfe, Jonathan; Meyer, Tiffany C.; Harrison, David R.; Cintineo, John L.; Calhoun, Kristin M.; Smith, Travis M.; Gerard, Alan E.; Rothfusz, Lans P.
    Publisher: American Meteorological Society
    Abstract: AbstractProviding advance warning for impending severe convective weather events (i.e., tornadoes, hail, wind) fundamentally requires an ability to predict and/or detect these hazards and subsequently communicate their ...
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    The Third Real-Time, Virtual Spring Forecasting Experiment to Advance Severe Weather Prediction Capabilities 

    Source: Bulletin of the American Meteorological Society:;2023:;volume( 104 ):;issue: 002:;page E456
    Author(s): Clark, Adam J.; Jirak, Israel L.; Gallo, Burkely T.; Roberts, Brett; Knopfmeier, Kent H.; Vancil, Jake; Jahn, David; Krocak, Makenzie; Karstens, Christopher D.; Loken, Eric D.; Dahl, Nathan A.; Harrison, David; Imy, David; Wade, Andrew R.; Milne, Jeffrey
    Publisher: American Meteorological Society
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    The First Hybrid NOAA Hazardous Weather Testbed Spring Forecasting Experiment for Advancing Severe Weather Prediction 

    Source: Bulletin of the American Meteorological Society:;2023:;volume( 104 ):;issue: 012:;page E2305
    Author(s): Clark, Adam J.; Jirak, Israel L.; Supinie, Timothy A.; Knopfmeier, Kent H.; Vancil, Jake; Jahn, David; Harrison, David; Brannan, Allison Lynn; Karstens, Christopher D.; Loken, Eric D.; Dahl, Nathan A.; Krocak, Makenzie; Imy, David; Wade, Andrew R.; Milne,
    Publisher: American Meteorological Society
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    The Third Real-Time, Virtual Spring Forecasting Experiment to Advance Severe Weather Prediction Capabilities 

    Source: Bulletin of the American Meteorological Society:;2023:;volume( 104 ):;issue: 002
    Author(s): Clark, Adam J.; Jirak, Israel L.; Gallo, Burkely T.; Roberts, Brett; Knopfmeier, Kent H.; Vancil, Jake; Jahn, David; Krocak, Makenzie; Karstens, Christopher D.; Loken, Eric D.; Dahl, Nathan A.; Harrison, David; Imy, David; Wade, Andrew R.; Milne, Jeffrey
    Publisher: American Meteorological Society
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