Articles | Volume 20, issue 2
https://doi.org/10.5194/acp-20-931-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-20-931-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Jan 2020
Research article |
| 24 Jan 2020
| 24 Jan 2020
Evaluation of a multi-model, multi-constituent assimilation framework for tropospheric chemical reanalysis
Kazuyuki Miyazaki
CORRESPONDING AUTHOR
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA, USA
Earth Surface System Research Center, Japan Agency for Marine-Earth
Science and Technology (JAMSTEC), Yokohama, 236-0001, Japan
Kevin W. Bowman
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA, USA
Keiya Yumimoto
Research Institute for Applied Mechanics, Kyushu University, Kasuga
Park 6-1, Fukuoka, 816-8580, Japan
Thomas Walker
Department of Civil and Environmental Engineering, Carleton
University, Ottawa, Ontario, Canada
Kengo Sudo
Graduate School of Environmental Studies, Nagoya University, Nagoya,
Japan
Earth Surface System Research Center, Japan Agency for Marine-Earth
Science and Technology (JAMSTEC), Yokohama, 236-0001, Japan
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- An aerosol vertical data assimilation system (NAQPMS-PDAF v1.0): development and application H. Wang et al. https://doi.org/10.5194/gmd-15-3555-2022
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- Changes in US background ozone associated with the 2011 turnaround in Chinese NOx emissions K. Miyazaki et al. https://doi.org/10.1088/2515-7620/ac619b
- An intercomparison of tropospheric ozone reanalysis products from CAMS, CAMS interim, TCR-1, and TCR-2 V. Huijnen et al. https://doi.org/10.5194/gmd-13-1513-2020
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Saved (final revised paper)
Latest update: 11 Jun 2026
Short summary
We introduce a multi-model, multi-constituent chemical data assimilation framework that directly accounts for model error in transport and chemistry by integrating a portfolio of forward chemical transport models. The assimilation was able to reduce ensemble forward model spread and bias relative to independent measurements. Diagnostic information readily available from the framework has the potential to improve chemical predictions through relationships such as emergent constraints.
We introduce a multi-model, multi-constituent chemical data assimilation framework that directly...
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