Process-oriented evaluation of climate and weather forecasting models

Realistic climate and weather prediction models are necessary to produce confidence in projections of future climate over many decades and predictions for days to seasons. These models must be physically justified and validated for multiple weather and climate processes. A key opportunity to accelerate model improvement is greater incorporation of process-oriented diagnostics (PODs) into standard packages that can be applied during the model development process, allowing the application of diagnostics to be repeatable across multiple model versions and used as a benchmark for model improvement. A POD characterizes a specific physical process or emergent behavior that is related to the ability to simulate an observed phenomenon. This paper describes the outcomes of activities by the Model Diagnostics Task Force (MDTF) under the NOAA Climate Program Office (CPO) Modeling, Analysis, Predictions and Projections (MAPP) program to promote development of PODs and their application to climate and weather prediction models. MDTF and modeling center perspectives on the need for expanded process-oriented diagnosis of models are presented. Multiple PODs developed by the MDTF are summarized, and an open-source software framework developed by the MDTF to aid application of PODs to centers’ model development is presented in the context of other relevant community activities. The paper closes by discussing paths forward for the MDTF effort and for community process-oriented diagnosis.

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Author Maloney, Eric D.
Gettelman, Andrew
Ming, Yi
Neelin, J. David
Barrie, Daniel
Mariotti, Annarita
Chen, Chin-Chieh
Coleman, Danielle R. B.
Kuo, Yi-Hung
Singh, Bohar
Annamalai, H.
Berg, Alexis
Booth, James F.
Camargo, Suzana J.
Dai, Aiguo
Gonzalez, Alex
Hafner, Jan
Jiang, Xianan
Jing, Xianwen
Kim, Daehyun
Kumar, Arun
Moon, Yumin
Naud, Catherine M.
Sobel, Adam H.
Suzuki, Kentaroh
Wang, Fuchang
Wang, Junhong
Wing, Allison A.
Xu, Xiaobiao
Zhao, Ming
Publisher UCAR/NCAR - Library
Publication Date 2019-09-01T00:00:00
Digital Object Identifier (DOI) Not Assigned
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Resource Version N/A
Topic Category geoscientificInformation
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Metadata Date 2023-08-18T19:08:16.041559
Metadata Record Identifier edu.ucar.opensky::articles:22919
Metadata Language eng; USA
Suggested Citation Maloney, Eric D., Gettelman, Andrew, Ming, Yi, Neelin, J. David, Barrie, Daniel, Mariotti, Annarita, Chen, Chin-Chieh, Coleman, Danielle R. B., Kuo, Yi-Hung, Singh, Bohar, Annamalai, H., Berg, Alexis, Booth, James F., Camargo, Suzana J., Dai, Aiguo, Gonzalez, Alex, Hafner, Jan, Jiang, Xianan, Jing, Xianwen, Kim, Daehyun, Kumar, Arun, Moon, Yumin, Naud, Catherine M., Sobel, Adam H., Suzuki, Kentaroh, Wang, Fuchang, Wang, Junhong, Wing, Allison A., Xu, Xiaobiao, Zhao, Ming. (2019). Process-oriented evaluation of climate and weather forecasting models. UCAR/NCAR - Library. http://n2t.net/ark:/85065/d7g44tf4. Accessed 28 July 2025.

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