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.
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.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
15 May 2024
Development and technical paper |
| 15 May 2024
| 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)
U.S. National Science Foundation National Center for Atmospheric Research, Boulder, Colorado 80301, USA
Benjamin Ménétrier
Joint Center for Satellite Data Assimilation, Boulder, Colorado 80301, USA
Norwegian Meteorological Institute, Oslo, Norway
Chris Snyder
U.S. National Science Foundation National Center for Atmospheric Research, Boulder, Colorado 80301, USA
Zhiquan Liu
U.S. National Science Foundation National Center for Atmospheric Research, Boulder, Colorado 80301, USA
Jonathan J. Guerrette
U.S. National Science Foundation National Center for Atmospheric Research, Boulder, Colorado 80301, USA
currently at: Tomorrow.io, Golden, Colorado, USA
Junmei Ban
U.S. National Science Foundation National Center for Atmospheric Research, Boulder, Colorado 80301, USA
Ivette Hernández Baños
U.S. National Science Foundation National Center for Atmospheric Research, Boulder, Colorado 80301, USA
Yonggang G. Yu
U.S. National Science Foundation National Center for Atmospheric Research, Boulder, Colorado 80301, USA
currently at: Science Applications International Corporation, Reston, Virginia, USA
William C. Skamarock
U.S. National Science Foundation National Center for Atmospheric Research, Boulder, Colorado 80301, USA
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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.
We describe the multivariate static background error covariance (B) for the JEDI-MPAS 3D-Var...
