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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Latest update: 23 Apr 2024
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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.