Leveraging Co-Production to Bridge Research and Operations in Operational Meteorology

David R. Harrison aCooperative Institute for Severe and High-Impact Weather Research and Operations, The University of Oklahoma, Norman, Oklahoma
bSchool of Meteorology, The University of Oklahoma, Norman, Oklahoma
dNOAA/NWS/Storm Prediction Center, Norman, Oklahoma

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Amy McGovern bSchool of Meteorology, The University of Oklahoma, Norman, Oklahoma
cSchool of Computer Science, The University of Oklahoma, Norman, Oklahoma
fNSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography, University of Oklahoma, Norman, Oklahoma

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Christopher D. Karstens dNOAA/NWS/Storm Prediction Center, Norman, Oklahoma
bSchool of Meteorology, The University of Oklahoma, Norman, Oklahoma

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Ann Bostrom eSchool of Public Policy and Governance, The University of Washington, Seattle, Washington
fNSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography, University of Oklahoma, Norman, Oklahoma

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Israel L. Jirak dNOAA/NWS/Storm Prediction Center, Norman, Oklahoma

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Patrick T. Marsh dNOAA/NWS/Storm Prediction Center, Norman, Oklahoma

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Abstract

The benefits of collaboration between the research and operational communities during the research-to-operations (R2O) process have long been documented in the scientific literature. Operational forecasters have a practiced, expert insight into weather analysis and forecasting but typically lack the time and resources for formal research and development. Conversely, many researchers have the resources, theoretical knowledge, and formal experience to solve complex meteorological challenges but lack an understanding of operation procedures, needs, requirements, and authority necessary to effectively bridge the R2O gap. Collaboration then serves as the most viable strategy to further a better understanding and improved prediction of atmospheric processes via ongoing multi-disciplinary knowledge transfer between the research and operational communities. However, existing R2O processes leave room for improvement when it comes to collaboration throughout a new product’s development cycle. This study assesses the subjective importance of collaboration at various stages of product development via a survey presented to participants of the 2021 Hazardous Weather Testbed Spring Forecasting Experiment. This feedback is then applied to create a proposed new R2O workflow that combines components from existing R2O procedures and modern co-production philosophies.

© 2025 American Meteorological Society. This is an Author Accepted Manuscript distributed under the terms of the default AMS reuse license. For information regarding reuse and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: David R. Harrison, david.harrison@noaa.gov

Abstract

The benefits of collaboration between the research and operational communities during the research-to-operations (R2O) process have long been documented in the scientific literature. Operational forecasters have a practiced, expert insight into weather analysis and forecasting but typically lack the time and resources for formal research and development. Conversely, many researchers have the resources, theoretical knowledge, and formal experience to solve complex meteorological challenges but lack an understanding of operation procedures, needs, requirements, and authority necessary to effectively bridge the R2O gap. Collaboration then serves as the most viable strategy to further a better understanding and improved prediction of atmospheric processes via ongoing multi-disciplinary knowledge transfer between the research and operational communities. However, existing R2O processes leave room for improvement when it comes to collaboration throughout a new product’s development cycle. This study assesses the subjective importance of collaboration at various stages of product development via a survey presented to participants of the 2021 Hazardous Weather Testbed Spring Forecasting Experiment. This feedback is then applied to create a proposed new R2O workflow that combines components from existing R2O procedures and modern co-production philosophies.

© 2025 American Meteorological Society. This is an Author Accepted Manuscript distributed under the terms of the default AMS reuse license. For information regarding reuse and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: David R. Harrison, david.harrison@noaa.gov
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