Articles | Volume 26, issue 3
https://doi.org/10.5194/npg-26-211-2019
https://doi.org/10.5194/npg-26-211-2019
Research article
 | 
08 Aug 2019
Research article |  | 08 Aug 2019

Non-Gaussian statistics in global atmospheric dynamics: a study with a 10 240-member ensemble Kalman filter using an intermediate atmospheric general circulation model

Keiichi Kondo and Takemasa Miyoshi

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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
AR by Keiichi Kondo on behalf of the Authors (27 May 2019)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (31 May 2019) by Olivier Talagrand
RR by Anonymous Referee #2 (04 Jun 2019)
RR by Anonymous Referee #3 (10 Jun 2019)
RR by Anonymous Referee #1 (17 Jun 2019)
ED: Publish subject to minor revisions (review by editor) (18 Jun 2019) by Olivier Talagrand
AR by Keiichi Kondo on behalf of the Authors (28 Jun 2019)  Author's response    Manuscript
ED: Publish subject to technical corrections (28 Jun 2019) by Olivier Talagrand
AR by Keiichi Kondo on behalf of the Authors (01 Jul 2019)  Author's response    Manuscript
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Short summary
This study investigates non-Gaussian statistics of the data from a 10240-member ensemble Kalman filter. The large ensemble size can resolve the detailed structures of the probability density functions (PDFs) and indicates that the non-Gaussian PDF is caused by multimodality and outliers. While the outliers appear randomly, large multimodality corresponds well with large analysis error, mainly in the tropical regions and storm track regions where highly nonlinear processes appear frequently.