The NOAA/CIMSS ProbSevere Model: Incorporation of Total Lightning and ValidationSource: Weather and Forecasting:;2018:;volume 033:;issue 001::page 331Author:Cintineo, John L.
,
Pavolonis, Michael J.
,
Sieglaff, Justin M.
,
Lindsey, Daniel T.
,
Cronce, Lee
,
Gerth, Jordan
,
Rodenkirch, Benjamin
,
Brunner, Jason
,
Gravelle, Chad
DOI: 10.1175/WAF-D-17-0099.1Publisher: 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 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.
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contributor author | Cintineo, John L. | |
contributor author | Pavolonis, Michael J. | |
contributor author | Sieglaff, Justin M. | |
contributor author | Lindsey, Daniel T. | |
contributor author | Cronce, Lee | |
contributor author | Gerth, Jordan | |
contributor author | Rodenkirch, Benjamin | |
contributor author | Brunner, Jason | |
contributor author | Gravelle, Chad | |
date accessioned | 2019-09-19T10:05:15Z | |
date available | 2019-09-19T10:05:15Z | |
date copyright | 2/1/2018 12:00:00 AM | |
date issued | 2018 | |
identifier other | waf-d-17-0099.1.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4261369 | |
description 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 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. | |
publisher | American Meteorological Society | |
title | The NOAA/CIMSS ProbSevere Model: Incorporation of Total Lightning and Validation | |
type | Journal Paper | |
journal volume | 33 | |
journal issue | 1 | |
journal title | Weather and Forecasting | |
identifier doi | 10.1175/WAF-D-17-0099.1 | |
journal fristpage | 331 | |
journal lastpage | 345 | |
tree | Weather and Forecasting:;2018:;volume 033:;issue 001 | |
contenttype | Fulltext |