Articles | Volume 20, issue 2
https://doi.org/10.5194/acp-20-931-2020
https://doi.org/10.5194/acp-20-931-2020
Research article
 | 
24 Jan 2020
Research article |  | 24 Jan 2020

Evaluation of a multi-model, multi-constituent assimilation framework for tropospheric chemical reanalysis

Kazuyuki Miyazaki, Kevin W. Bowman, Keiya Yumimoto, Thomas Walker, and Kengo Sudo

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Latest update: 19 Jun 2024
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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.
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