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Process-Oriented Evaluation of Climate and Weather Forecasting Models.

Authors :
Maloney, Eric D.
Gettelman, Andrew
Ming, Yi
Neelin, J. David
Barrie, Daniel
Mariotti, Annarita
Chen, C.-C.
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
Source :
Bulletin of the American Meteorological Society. Sep2019, Vol. 100 Issue 9, p1665-1686. 22p.
Publication Year :
2019

Abstract

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00030007
Volume :
100
Issue :
9
Database :
Academic Search Index
Journal :
Bulletin of the American Meteorological Society
Publication Type :
Academic Journal
Accession number :
138855444
Full Text :
https://doi.org/10.1175/BAMS-D-18-0042.1