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dc.contributor.authorWan, H.
dc.contributor.authorZhang, K.
dc.contributor.authorRasch, P.J.
dc.contributor.authorLarson, V.E.
dc.contributor.authorZeng, X.
dc.contributor.authorZhang, S.
dc.contributor.authorDixon, R.
dc.date.accessioned2022-05-20T01:37:05Z
dc.date.available2022-05-20T01:37:05Z
dc.date.issued2022
dc.identifier.citationWan, H., Zhang, K., Rasch, P. J., Larson, V. E., Zeng, X., Zhang, S., & Dixon, R. (2022). CondiDiag1.0: A flexible online diagnostic tool for conditional sampling and budget analysis in the E3SM atmosphere model (EAM). Geoscientific Model Development.
dc.identifier.issn1991-959X
dc.identifier.doi10.5194/gmd-15-3205-2022
dc.identifier.urihttp://hdl.handle.net/10150/664515
dc.description.abstractNumerical models used in weather and climate prediction take into account a comprehensive set of atmospheric processes (i.e., phenomena) such as the resolved and unresolved fluid dynamics, radiative transfer, cloud and aerosol life cycles, and mass or energy exchanges with the Earth's surface. In order to identify model deficiencies and improve predictive skills, it is important to obtain process-level understanding of the interactions between different processes. Conditional sampling and budget analysis are powerful tools for process-oriented model evaluation, but they often require tedious ad hoc coding and large amounts of instantaneous model output, resulting in inefficient use of human and computing resources. This paper presents an online diagnostic tool that addresses this challenge by monitoring model variables in a generic manner as they evolve within the time integration cycle. The tool is convenient to use. It allows users to select sampling conditions and specify monitored variables at run time. Both the evolving values of the model variables and their increments caused by different atmospheric processes can be monitored and archived. Online calculation of vertical integrals is also supported. Multiple sampling conditions can be monitored in a single simulation in combination with unconditional sampling. The paper explains in detail the design and implementation of the tool in the Energy Exascale Earth System Model (E3SM) version 1. The usage is demonstrated through three examples: a global budget analysis of dust aerosol mass concentration, a composite analysis of sea salt emission and its dependency on surface wind speed, and a conditionally sampled relative humidity budget. The tool is expected to be easily portable to closely related atmospheric models that use the same or similar data structures and time integration methods. © Copyright:
dc.language.isoen
dc.publisherCopernicus GmbH
dc.rights© Author(s) 2022. This work is distributed under the Creative Commons Attribution 4.0 License.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleCondiDiag1.0: A flexible online diagnostic tool for conditional sampling and budget analysis in the E3SM atmosphere model (EAM)
dc.typeArticle
dc.typetext
dc.contributor.departmentDepartment of Hydrology and Atmospheric Sciences, The University of Arizona
dc.identifier.journalGeoscientific Model Development
dc.description.noteOpen access journal
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
dc.eprint.versionFinal published version
dc.source.journaltitleGeoscientific Model Development
refterms.dateFOA2022-05-20T01:37:05Z


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© Author(s) 2022. This work is distributed under the Creative Commons Attribution 4.0 License.
Except where otherwise noted, this item's license is described as © Author(s) 2022. This work is distributed under the Creative Commons Attribution 4.0 License.