The NOAA/CIMSS ProbSevere Model: Incorporation of Total Lightning and Validation

John L. Cintineo Cooperative Institute of Meteorological Satellite Studies, University of Wisconsin–Madison, Madison, Wisconsin

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Michael J. Pavolonis NOAA/NESDIS/Center for Satellite Applications and Research/Advanced Satellite Products Team, Madison, Wisconsin

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Justin M. Sieglaff Cooperative Institute of Meteorological Satellite Studies, University of Wisconsin–Madison, Madison, Wisconsin

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Daniel T. Lindsey NOAA/NESDIS/Center for Satellite Applications and Research/Regional and Mesoscale Meteorology Branch, Fort Collins, Colorado

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Lee Cronce Cooperative Institute of Meteorological Satellite Studies, University of Wisconsin–Madison, Madison, Wisconsin

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Jordan Gerth Cooperative Institute of Meteorological Satellite Studies, University of Wisconsin–Madison, Madison, Wisconsin

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Benjamin Rodenkirch Cooperative Institute of Meteorological Satellite Studies, University of Wisconsin–Madison, Madison, Wisconsin

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Jason Brunner Cooperative Institute of Meteorological Satellite Studies, University of Wisconsin–Madison, Madison, Wisconsin

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Chad Gravelle Cooperative Institute of Meteorological Satellite Studies, University of Wisconsin–Madison, Madison, Wisconsin
NWS Operations Proving Ground, Kansas City, Missouri

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Abstract

The 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 extracts information related to thunderstorm development from several data sources to produce timely, short-term, statistical forecasts of thunderstorm intensity. More specifically, ProbSevere utilizes short-term numerical weather prediction guidance (NWP), geostationary satellite, ground-based radar, and ground-based lightning data to determine the probability that convective storm cells will produce severe weather up to 90 min in the future. ProbSevere guidance, which updates approximately every 2 min, is available to National Weather Service (NWS) Weather Forecast Offices with very short latency. This paper focuses on the integration of ground-based lightning detection data into ProbSevere. In addition, a thorough validation analysis is presented. The validation analysis demonstrates that ProbSevere has slightly less skill compared to NWS severe weather warnings, but can offer greater lead time to initial hazards. Feedback from NWS users has been highly favorable, with most forecasters responding that ProbSevere increases confidence and lead time in numerous warning situations.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: John L. Cintineo, jlc248@gmail.com

Abstract

The 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 extracts information related to thunderstorm development from several data sources to produce timely, short-term, statistical forecasts of thunderstorm intensity. More specifically, ProbSevere utilizes short-term numerical weather prediction guidance (NWP), geostationary satellite, ground-based radar, and ground-based lightning data to determine the probability that convective storm cells will produce severe weather up to 90 min in the future. ProbSevere guidance, which updates approximately every 2 min, is available to National Weather Service (NWS) Weather Forecast Offices with very short latency. This paper focuses on the integration of ground-based lightning detection data into ProbSevere. In addition, a thorough validation analysis is presented. The validation analysis demonstrates that ProbSevere has slightly less skill compared to NWS severe weather warnings, but can offer greater lead time to initial hazards. Feedback from NWS users has been highly favorable, with most forecasters responding that ProbSevere increases confidence and lead time in numerous warning situations.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: John L. Cintineo, jlc248@gmail.com
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