Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 22
Articles | Volume 15, issue 22
https://doi.org/10.5194/gmd-15-8325-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-8325-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 22
Development and technical paper
 | 
18 Nov 2022
Development and technical paper |  | 18 Nov 2022

A local particle filter and its Gaussian mixture extension implemented with minor modifications to the LETKF

Shunji Kotsuki, Takemasa Miyoshi, Keiichi Kondo, and Roland Potthast

Viewed

Total article views: 3,067 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,174 802 91 3,067 89 125
  • HTML: 2,174
  • PDF: 802
  • XML: 91
  • Total: 3,067
  • BibTeX: 89
  • EndNote: 125
Views and downloads (calculated since 07 Jun 2022)
Cumulative views and downloads (calculated since 07 Jun 2022)

Viewed (geographical distribution)

Total article views: 3,067 (including HTML, PDF, and XML) Thereof 2,946 with geography defined and 121 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Nov 2025
Download
Short summary
Data assimilation plays an important part in numerical weather prediction (NWP) in terms of combining forecasted states and observations. While data assimilation methods in NWP usually assume the Gaussian error distribution, some variables in the atmosphere, such as precipitation, are known to have non-Gaussian error statistics. This study extended a widely used ensemble data assimilation algorithm to enable the assimilation of more non-Gaussian observations.
Read more
Share