Articles | Volume 15, issue 20
https://doi.org/10.5194/gmd-15-7859-2022
https://doi.org/10.5194/gmd-15-7859-2022
Development and technical paper
 | 
26 Oct 2022
Development and technical paper |  | 26 Oct 2022

Data assimilation for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 1.0.0): EnVar implementation and evaluation

Zhiquan Liu, Chris Snyder, Jonathan J. Guerrette, Byoung-Joo Jung, Junmei Ban, Steven Vahl, Yali Wu, Yannick Trémolet, Thomas Auligné, Benjamin Ménétrier, Anna Shlyaeva, Stephen Herbener, Emily Liu, Daniel Holdaway, and Benjamin T. Johnson

Viewed

Total article views: 2,687 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,734 892 61 2,687 263 61 73
  • HTML: 1,734
  • PDF: 892
  • XML: 61
  • Total: 2,687
  • Supplement: 263
  • BibTeX: 61
  • EndNote: 73
Views and downloads (calculated since 05 Jul 2022)
Cumulative views and downloads (calculated since 05 Jul 2022)

Viewed (geographical distribution)

Total article views: 2,687 (including HTML, PDF, and XML) Thereof 2,548 with geography defined and 139 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 19 Jul 2024
Download
Short summary
JEDI-MPAS 1.0.0, a new data assimilation (DA) system for the MPAS model, was publicly released for community use. This article describes JEDI-MPAS's implementation of the ensemble–variational DA technique and demonstrates its robustness and credible performance by incrementally adding three types of microwave radiances (clear-sky AMSU-A, all-sky AMSU-A, clear-sky MHS) to a non-radiance DA experiment. We intend to periodically release new and improved versions of JEDI-MPAS in upcoming years.