Journal cover Journal topic
Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 1.129 IF 1.129
  • IF 5-year value: 1.519 IF 5-year 1.519
  • CiteScore value: 1.54 CiteScore 1.54
  • SNIP value: 0.798 SNIP 0.798
  • SJR value: 0.610 SJR 0.610
  • IPP value: 1.41 IPP 1.41
  • h5-index value: 21 h5-index 21
  • Scimago H index value: 48 Scimago H index 48
Discussion papers
https://doi.org/10.5194/npg-2018-52
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-2018-52
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 12 Nov 2018

Research article | 12 Nov 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Nonlinear Processes in Geophysics (NPG).

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

Keiichi Kondo1,a and Takemasa Miyoshi1,2,3 Keiichi Kondo and Takemasa Miyoshi
  • 1RIKEN Center for Computational Science, Kobe, Japan
  • 2Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, USA
  • 3Japan Agency for Marine- Earth Science and Technology, Yokohama, Japan
  • anow at: Meteorological Research Institute, Japan Meteorological Agency, Japan

Abstract. We previously performed local ensemble transform Kalman filter experiments with up to 10240 ensemble members using an intermediate atmospheric general circulation model. While the previous study focused on the localization impact on the analysis accuracy, the present study focuses on the probability density functions (PDFs) represented by the 10240-member ensemble. The 10240-member ensemble can resolve the detailed structures of the PDFs and indicates that the non-Gaussian PDF is caused by multimodality and outliers. The results show that the spatial patterns of the analysis errors correspond well with the non-Gaussianity. While the outliers appear randomly, large multimodality corresponds well with large analysis error, mainly in the tropical regions where highly nonlinear convective processes appear frequently. Therefore, we further investigate the lifecycle of multimodal PDFs, and show that the multimodal PDFs are generated by the on-off switch of convective parameterization and disappear naturally. Sensitivity to the ensemble size suggests that approximately 1000 ensemble members be necessary to capture the detailed structures of the non-Gaussian PDF.

Keiichi Kondo and Takemasa Miyoshi
Interactive discussion
Status: open (until 07 Jan 2019)
Status: open (until 07 Jan 2019)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement
Keiichi Kondo and Takemasa Miyoshi
Keiichi Kondo and Takemasa Miyoshi
Viewed  
Total article views: 297 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
253 38 6 297 7 8
  • HTML: 253
  • PDF: 38
  • XML: 6
  • Total: 297
  • BibTeX: 7
  • EndNote: 8
Views and downloads (calculated since 12 Nov 2018)
Cumulative views and downloads (calculated since 12 Nov 2018)
Viewed (geographical distribution)  
Total article views: 275 (including HTML, PDF, and XML) Thereof 275 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Cited  
Saved  
No saved metrics found.
Discussed  
No discussed metrics found.
Latest update: 13 Dec 2018
Publications Copernicus
Download
Short summary
This study focuses on the non-Gaussian statistics based on a 10 240-member ensemble data assimilation. The 10 240 members can resolve the detailed structures of the probability density functions (PDFs) and indicates that the non-Gaussian PDF is caused by multimodality and outliers. The results show that the outliers appear randomly and that large multimodality corresponds well with large analysis error, mainly in the tropical regions where highly nonlinear convective processes appear frequently.
This study focuses on the non-Gaussian statistics based on a 10 240-member ensemble data...
Citation
Share