CondiDiag1.0: a flexible online diagnostic tool for conditional sampling and budget analysis in the E3SM atmosphere model (EAM)
<p>Numerical 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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-04-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/15/3205/2022/gmd-15-3205-2022.pdf |
| _version_ | 1818273055372214272 |
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| author | H. Wan K. Zhang P. J. Rasch V. E. Larson V. E. Larson X. Zeng S. Zhang R. Dixon |
| author_facet | H. Wan K. Zhang P. J. Rasch V. E. Larson V. E. Larson X. Zeng S. Zhang R. Dixon |
| author_sort | H. Wan |
| collection | DOAJ |
| description | <p>Numerical 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.</p>
<p>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.</p> |
| first_indexed | 2024-12-12T21:51:53Z |
| format | Article |
| id | doaj.art-c8c6cc36b72a4b5fb21dc41d2cb44053 |
| institution | Directory Open Access Journal |
| issn | 1991-959X 1991-9603 |
| language | English |
| last_indexed | 2024-12-12T21:51:53Z |
| publishDate | 2022-04-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Geoscientific Model Development |
| spelling | doaj.art-c8c6cc36b72a4b5fb21dc41d2cb440532022-12-22T00:10:46ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032022-04-01153205323110.5194/gmd-15-3205-2022CondiDiag1.0: a flexible online diagnostic tool for conditional sampling and budget analysis in the E3SM atmosphere model (EAM)H. Wan0K. Zhang1P. J. Rasch2V. E. Larson3V. E. Larson4X. Zeng5S. Zhang6R. Dixon7Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USAAtmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USAAtmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USAAtmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USADepartment of Mathematical Sciences, University of Wisconsin–Milwaukee, Milwaukee, Wisconsin, USADepartment of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Arizona, USAAtmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USADepartment of Earth and Atmospheric Sciences, University of Nebraska–Lincoln, Lincoln, Nebraska, USA<p>Numerical 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.</p> <p>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.</p>https://gmd.copernicus.org/articles/15/3205/2022/gmd-15-3205-2022.pdf |
| spellingShingle | H. Wan K. Zhang P. J. Rasch V. E. Larson V. E. Larson X. Zeng S. Zhang R. Dixon CondiDiag1.0: a flexible online diagnostic tool for conditional sampling and budget analysis in the E3SM atmosphere model (EAM) Geoscientific Model Development |
| title | CondiDiag1.0: a flexible online diagnostic tool for conditional sampling and budget analysis in the E3SM atmosphere model (EAM) |
| title_full | CondiDiag1.0: a flexible online diagnostic tool for conditional sampling and budget analysis in the E3SM atmosphere model (EAM) |
| title_fullStr | CondiDiag1.0: a flexible online diagnostic tool for conditional sampling and budget analysis in the E3SM atmosphere model (EAM) |
| title_full_unstemmed | CondiDiag1.0: a flexible online diagnostic tool for conditional sampling and budget analysis in the E3SM atmosphere model (EAM) |
| title_short | CondiDiag1.0: a flexible online diagnostic tool for conditional sampling and budget analysis in the E3SM atmosphere model (EAM) |
| title_sort | condidiag1 0 a flexible online diagnostic tool for conditional sampling and budget analysis in the e3sm atmosphere model eam |
| url | https://gmd.copernicus.org/articles/15/3205/2022/gmd-15-3205-2022.pdf |
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