Articles | Volume 16, issue 23
https://doi.org/10.5194/gmd-16-7123-2023
https://doi.org/10.5194/gmd-16-7123-2023
Development and technical paper
 | 
08 Dec 2023
Development and technical paper |  | 08 Dec 2023

Data assimilation for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 2.0.0-beta): ensemble of 3D ensemble-variational (En-3DEnVar) assimilations

Jonathan J. Guerrette, Zhiquan Liu, Chris Snyder, Byoung-Joo Jung, Craig S. Schwartz, Junmei Ban, Steven Vahl, Yali Wu, Ivette Hernández Baños, Yonggang G. Yu, Soyoung Ha, Yannick Trémolet, Thomas Auligné, Clementine Gas, Benjamin Ménétrier, Anna Shlyaeva, Mark Miesch, Stephen Herbener, Emily Liu, Daniel Holdaway, and Benjamin T. Johnson

Viewed

Total article views: 1,879 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,139 672 68 1,879 53 41
  • HTML: 1,139
  • PDF: 672
  • XML: 68
  • Total: 1,879
  • BibTeX: 53
  • EndNote: 41
Views and downloads (calculated since 06 Apr 2023)
Cumulative views and downloads (calculated since 06 Apr 2023)

Viewed (geographical distribution)

Total article views: 1,879 (including HTML, PDF, and XML) Thereof 1,804 with geography defined and 75 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
Short summary
We demonstrate an ensemble of variational data assimilations (EDA) with the Model for Prediction Across Scales and the Joint Effort for Data assimilation Integration (JEDI) software framework. When compared to 20-member ensemble forecasts from operational initial conditions, those from 80-member EDA-generated initial conditions improve flow-dependent error covariances and subsequent 10 d forecasts. These experiments are repeatable for any atmospheric model with a JEDI interface.