Warn-on-Forecast System: From Vision to Reality

Pamela L. Heinselman aNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
cSchool of Meteorology, University of Oklahoma, Norman, Oklahoma

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Patrick C. Burke aNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

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Louis J. Wicker aNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
cSchool of Meteorology, University of Oklahoma, Norman, Oklahoma

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Adam J. Clark aNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
cSchool of Meteorology, University of Oklahoma, Norman, Oklahoma

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John S. Kain aNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
jIBSS Corp., Silver Spring, Maryland

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Jidong Gao aNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
cSchool of Meteorology, University of Oklahoma, Norman, Oklahoma

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Nusrat Yussouf aNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
bCooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma
cSchool of Meteorology, University of Oklahoma, Norman, Oklahoma

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Thomas A. Jones aNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
bCooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma
cSchool of Meteorology, University of Oklahoma, Norman, Oklahoma

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Patrick S. Skinner aNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
bCooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma
cSchool of Meteorology, University of Oklahoma, Norman, Oklahoma

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Corey K. Potvin aNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
cSchool of Meteorology, University of Oklahoma, Norman, Oklahoma

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Katie A. Wilson aNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
bCooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma
gRand Corporation, Santa Monica, California

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Burkely T. Gallo bCooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma
dNOAA/NWS/NCEP Storm Prediction Center, Norman, Oklahoma
h16th Weather Squadron, Offutt Air Force Base, Bellevue, Nebraska

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Montgomery L. Flora aNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
bCooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma
cSchool of Meteorology, University of Oklahoma, Norman, Oklahoma

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Joshua Martin aNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
bCooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma

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Gerry Creager aNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
bCooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma

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Kent H. Knopfmeier aNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
bCooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma

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Yunheng Wang aNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
bCooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma

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Brian C. Matilla aNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
bCooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma

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David C. Dowell eNOAA/Earth System Research Laboratories/Global Systems Laboratory, Boulder, Colorado

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Edward R. Mansell aNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

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Brett Roberts aNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
bCooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma
iCoreLogic, Irvine, California

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Kimberly A. Hoogewind aNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
bCooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma

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Derek R. Stratman aNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
bCooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma

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Jorge Guerra aNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
bCooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma
kProject Canary, Denver, Colorado

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Anthony E. Reinhart aNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

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Christopher A. Kerr aNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
bCooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma

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William Miller aNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
bCooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma
fEarth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland

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Abstract

In 2009, advancements in NWP and computing power inspired a vision to advance hazardous weather warnings from a warn-on-detection to a warn-on-forecast paradigm. This vision would require not only the prediction of individual thunderstorms and their attributes but the likelihood of their occurrence in time and space. During the last decade, the warn-on-forecast research team at the NOAA National Severe Storms Laboratory met this challenge through the research and development of 1) an ensemble of high-resolution convection-allowing models; 2) ensemble- and variational-based assimilation of weather radar, satellite, and conventional observations; and 3) unique postprocessing and verification techniques, culminating in the experimental Warn-on-Forecast System (WoFS). Since 2017, we have directly engaged users in the testing, evaluation, and visualization of this system to ensure that WoFS guidance is usable and useful to operational forecasters at NOAA national centers and local offices responsible for forecasting severe weather, tornadoes, and flash floods across the watch-to-warning continuum. Although an experimental WoFS is now a reality, we close by discussing many of the exciting opportunities remaining, including folding this system into the Unified Forecast System, transitioning WoFS into NWS operations, and pursuing next-decade science goals for further advancing storm-scale prediction.

Significance Statement

The purpose of this research is to develop an experimental prediction system that forecasts the probability for severe weather hazards associated with individual thunderstorms up to 6 h in advance. This capability is important because some people and organizations, like those living in mobile homes, caring for patients in hospitals, or managing large outdoor events, require extended lead time to protect themselves and others from potential severe weather hazards. Our results demonstrate a prediction system that enables forecasters, for the first time, to message probabilistic hazard information associated with individual severe storms between the watch-to-warning time frame within the United States.

Gerry Creager: Retired.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Publisher’s Note: This article was revised on 18 April 2024 to remove its original Open Access designation, as requested by the work’s funder.

Corresponding author: Pamela Heinselman, pam.heinselman@noaa.gov

Abstract

In 2009, advancements in NWP and computing power inspired a vision to advance hazardous weather warnings from a warn-on-detection to a warn-on-forecast paradigm. This vision would require not only the prediction of individual thunderstorms and their attributes but the likelihood of their occurrence in time and space. During the last decade, the warn-on-forecast research team at the NOAA National Severe Storms Laboratory met this challenge through the research and development of 1) an ensemble of high-resolution convection-allowing models; 2) ensemble- and variational-based assimilation of weather radar, satellite, and conventional observations; and 3) unique postprocessing and verification techniques, culminating in the experimental Warn-on-Forecast System (WoFS). Since 2017, we have directly engaged users in the testing, evaluation, and visualization of this system to ensure that WoFS guidance is usable and useful to operational forecasters at NOAA national centers and local offices responsible for forecasting severe weather, tornadoes, and flash floods across the watch-to-warning continuum. Although an experimental WoFS is now a reality, we close by discussing many of the exciting opportunities remaining, including folding this system into the Unified Forecast System, transitioning WoFS into NWS operations, and pursuing next-decade science goals for further advancing storm-scale prediction.

Significance Statement

The purpose of this research is to develop an experimental prediction system that forecasts the probability for severe weather hazards associated with individual thunderstorms up to 6 h in advance. This capability is important because some people and organizations, like those living in mobile homes, caring for patients in hospitals, or managing large outdoor events, require extended lead time to protect themselves and others from potential severe weather hazards. Our results demonstrate a prediction system that enables forecasters, for the first time, to message probabilistic hazard information associated with individual severe storms between the watch-to-warning time frame within the United States.

Gerry Creager: Retired.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Publisher’s Note: This article was revised on 18 April 2024 to remove its original Open Access designation, as requested by the work’s funder.

Corresponding author: Pamela Heinselman, pam.heinselman@noaa.gov
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