Articles | Volume 29, issue 3
Nonlin. Processes Geophys., 29, 317–327, 2022
https://doi.org/10.5194/npg-29-317-2022
Nonlin. Processes Geophys., 29, 317–327, 2022
https://doi.org/10.5194/npg-29-317-2022
Research article
01 Sep 2022
Research article | 01 Sep 2022

Applying prior correlations for ensemble-based spatial localization

Chu-Chun Chang and Eugenia Kalnay

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on npg-2022-5', Zheqi Shen, 26 Mar 2022
    • AC2: 'Reply on RC1', Chu-Chun Chang, 08 Jun 2022
  • RC2: 'Comment on npg-2022-5', Anonymous Referee #2, 28 Mar 2022
    • AC3: 'Reply on RC2', Chu-Chun Chang, 08 Jun 2022
  • AC1: 'Comment on npg-2022-5', Chu-Chun Chang, 08 Jun 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Chu-Chun Chang on behalf of the Authors (05 Jul 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (06 Jul 2022) by Wansuo Duan
RR by Zheqi Shen (15 Jul 2022)
ED: Publish as is (20 Jul 2022) by Wansuo Duan
AR by Chu-Chun Chang on behalf of the Authors (29 Jul 2022)  Author's response    Manuscript
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Short summary
This study introduces a new approach for enhancing the ensemble data assimilation (DA), a technique that combines observations and forecasts to improve numerical weather predictions. Our method uses the prescribed correlations to suppress spurious errors, improving the accuracy of DA. The experiments on the simplified atmosphere model show that our method has comparable performance to the traditional method but is superior in the early stage and is more computationally efficient.