Background error covariance with balance constraints for aerosol species and
applications in variational data assimilation

Zang, Zengliang; Hao, Zilong; Li, Yi; Pan, Xiaobin; You, Wei; Li, Zhijin; Chen, Dan

doi:https://doi.org/10.5194/gmd-9-2623-2016

Articles | Volume 9, issue 8

https://doi.org/10.5194/gmd-9-2623-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/gmd-9-2623-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 9, issue 8

Development and technical paper

|

10 Aug 2016

Development and technical paper |

| 10 Aug 2016

Background error covariance with balance constraints for aerosol species and applications in variational data assimilation

Zengliang Zang, Zilong Hao, Yi Li, Xiaobin Pan, Wei You, Zhijin Li, and Dan Chen

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Final revised paper (published on 10 Aug 2016)
Preprint (discussion started on 16 Nov 2015)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

SC C3052: 'Executive Editor Comment on "Background error covariance with balance constraints for aerosol species and applications in data assimilation"', Astrid Kerkweg, 23 Nov 2015
- AC C3651: 'response to A. Kerkweg's comments', Zengliang Zang, 10 Jan 2016
RC C3687: 'Referee comment, gmd-2015-212', Anonymous Referee #1, 12 Jan 2016
RC C4361: 'review comments', Anonymous Referee #2, 11 Apr 2016
EC C4431: 'References', Adrian Sandu, 30 Jun 2016

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Zengliang Zang on behalf of the Authors (09 Jun 2016) Author's response Manuscript

ED: Referee Nomination & Report Request started (30 Jun 2016) by Adrian Sandu

RR by Anonymous Referee #2 (10 Jul 2016)

ED: Publish as is (11 Jul 2016) by Adrian Sandu

Short summary

The aerosol data assimilation and forecasts can be improved by adopting balance constraints that spread observation information across variables, thus producing balanced initial distributions. Surface and aircraft aerosol observations were assimilated to demonstrate the impact of the balance constraints. The results showed that the forecasting experiment with balance constraints is more skillful and durable than the experiment without balance constraints.