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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- A new methodology for inferring surface ozone from multispectral satellite measurements N. Colombi et al. 10.1088/1748-9326/ac243d
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- The worldwide COVID-19 lockdown impacts on global secondary inorganic aerosols and radiative budget T. Sekiya et al. 10.1126/sciadv.adh2688
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- Updated tropospheric chemistry reanalysis and emission estimates, TCR-2, for 2005–2018 K. Miyazaki et al. 10.5194/essd-12-2223-2020
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- Impacts of Horizontal Resolution on Global Data Assimilation of Satellite Measurements for Tropospheric Chemistry Analysis T. Sekiya et al. 10.1029/2020MS002180
- Deep Learning to Evaluate US NOx Emissions Using Surface Ozone Predictions T. He et al. 10.1029/2021JD035597
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- Intercomparison of Magnitudes and Trends in Anthropogenic Surface Emissions From Bottom‐Up Inventories, Top‐Down Estimates, and Emission Scenarios N. Elguindi et al. 10.1029/2020EF001520
- Impacts of global NO<sub><i>x</i></sub> inversions on NO<sub>2</sub> and ozone simulations Z. Qu et al. 10.5194/acp-20-13109-2020
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- Global tropospheric ozone responses to reduced NO x emissions linked to the COVID-19 worldwide lockdowns K. Miyazaki et al. 10.1126/sciadv.abf7460
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Latest update: 23 Nov 2024
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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