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    The Development and Initial Capabilities of ThunderCast, a Deep Learning Model for Thunderstorm Nowcasting in the United States 

    Source: Artificial Intelligence for the Earth Systems:;2023:;volume( 002 ):;issue: 004
    Author(s): Ortland, Stephanie M.; Pavolonis, Michael J.; Cintineo, John L.
    Publisher: American Meteorological Society
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    Evolution of Severe and Nonsevere Convection Inferred from GOES-Derived Cloud Properties 

    Source: Journal of Applied Meteorology and Climatology:;2013:;volume( 052 ):;issue: 009:;page 2009
    Author(s): Cintineo, John L.; Pavolonis, Michael J.; Sieglaff, Justin M.; Heidinger, Andrew K.
    Publisher: American Meteorological Society
    Abstract: eostationary satellites [e.g., the Geostationary Operational Environmental Satellite (GOES)] provide high temporal resolution of cloud development and motion, which is essential to the study of many mesoscale phenomena, ...
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    An Objective High-Resolution Hail Climatology of the Contiguous United States 

    Source: Weather and Forecasting:;2012:;volume( 027 ):;issue: 005:;page 1235
    Author(s): Cintineo, John L.; Smith, Travis M.; Lakshmanan, Valliappa; Brooks, Harold E.; Ortega, Kiel L.
    Publisher: American Meteorological Society
    Abstract: he threat of damaging hail from severe thunderstorms affects many communities and industries on a yearly basis, with annual economic losses in excess of $1 billion (U.S. dollars). Past hail climatology has typically relied ...
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    An Empirical Model for Assessing the Severe Weather Potential of Developing Convection 

    Source: Weather and Forecasting:;2014:;volume( 029 ):;issue: 003:;page 639
    Author(s): Cintineo, John L.; Pavolonis, Michael J.; Sieglaff, Justin M.; Lindsey, Daniel T.
    Publisher: American Meteorological Society
    Abstract: he formation and maintenance of thunderstorms that produce large hail, strong winds, and tornadoes are often difficult to forecast due to their rapid evolution and complex interactions with environmental features that are ...
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    A Deep-Learning Model for Automated Detection of Intense Midlatitude Convection Using Geostationary Satellite Images 

    Source: Weather and Forecasting:;2020:;volume( 035 ):;issue: 006:;page 2567
    Author(s): Cintineo, John L.;Pavolonis, Michael J.;Sieglaff, Justin M.;Wimmers, Anthony;Brunner, Jason;Bellon, Willard
    Publisher: American Meteorological Society
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    NOAA ProbSevere v2.0—ProbHail, ProbWind, and ProbTor 

    Source: Weather and Forecasting:;2020:;volume( 035 ):;issue: 004:;page 1523
    Author(s): Cintineo, John L.;Pavolonis, Michael J.;Sieglaff, Justin M.;Cronce, Lee;Brunner, Jason
    Publisher: American Meteorological Society
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    The NOAA/CIMSS ProbSevere Model: Incorporation of Total Lightning and Validation 

    Source: Weather and Forecasting:;2018:;volume 033:;issue 001:;page 331
    Author(s): Cintineo, John L.; Pavolonis, Michael J.; Sieglaff, Justin M.; Lindsey, Daniel T.; Cronce, Lee; Gerth, Jordan; Rodenkirch, Benjamin; Brunner, Jason; Gravelle, Chad
    Publisher: American Meteorological Society
    Abstract: AbstractThe empirical Probability of Severe (ProbSevere) model, developed by the National Oceanic and Atmospheric Administration (NOAA) and the Cooperative Institute for Meteorological Satellite Studies (CIMSS), automatically ...
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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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