Articles | Volume 20, issue 2
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


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Referee Nomination & Report Request started (07 Nov 2019) by Bryan N. Duncan
RR by Anonymous Referee #2 (19 Dec 2019)
AR by Anna Wenzel on behalf of the Authors (08 Nov 2019)  Author's response
ED: Publish as is (19 Dec 2019) by Bryan N. Duncan
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.
Final-revised paper