Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 9
Articles | Volume 17, issue 9
https://doi.org/10.5194/gmd-17-3879-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-3879-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 9
Development and technical paper
 | 
15 May 2024
Development and technical paper |  | 15 May 2024

Three-dimensional variational assimilation with a multivariate background error covariance for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 2.0.0-beta)

Byoung-Joo Jung, Benjamin Ménétrier, Chris Snyder, Zhiquan Liu, Jonathan J. Guerrette, Junmei Ban, Ivette Hernández Baños, Yonggang G. Yu, and William C. Skamarock

Viewed

Total article views: 1,361 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
913 342 106 1,361 57 55
  • HTML: 913
  • PDF: 342
  • XML: 106
  • Total: 1,361
  • BibTeX: 57
  • EndNote: 55
Views and downloads (calculated since 14 Jul 2023)
Cumulative views and downloads (calculated since 14 Jul 2023)

Viewed (geographical distribution)

Total article views: 1,361 (including HTML, PDF, and XML) Thereof 1,350 with geography defined and 11 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 23 Nov 2024
Download
Short summary
We describe the multivariate static background error covariance (B) for the JEDI-MPAS 3D-Var data assimilation system. With tuned B parameters, the multivariate B gives physically balanced analysis increment fields in the single-observation test framework. In the month-long cycling experiment with a global 60 km mesh, 3D-Var with static B performs stably. Due to its simple workflow and minimal computational requirements, JEDI-MPAS 3D-Var can be useful for the research community.
Read more