Articles | Volume 16, issue 23
https://doi.org/10.5194/gmd-16-7123-2023
© Author(s) 2023. 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-16-7123-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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
Mesoscale and Microscale Meteorology Laboratory, National Center for Atmospheric Research, Boulder, CO 80301, USA
now at: Tomorrow.io, Golden, CO 80401, USA
Zhiquan Liu
Mesoscale and Microscale Meteorology Laboratory, National Center for Atmospheric Research, Boulder, CO 80301, USA
Chris Snyder
Mesoscale and Microscale Meteorology Laboratory, National Center for Atmospheric Research, Boulder, CO 80301, USA
Byoung-Joo Jung
Mesoscale and Microscale Meteorology Laboratory, National Center for Atmospheric Research, Boulder, CO 80301, USA
Craig S. Schwartz
Mesoscale and Microscale Meteorology Laboratory, National Center for Atmospheric Research, Boulder, CO 80301, USA
Junmei Ban
Mesoscale and Microscale Meteorology Laboratory, National Center for Atmospheric Research, Boulder, CO 80301, USA
Steven Vahl
Mesoscale and Microscale Meteorology Laboratory, National Center for Atmospheric Research, Boulder, CO 80301, USA
now at: Joint Center for Satellite Data Assimilation, University Center for Atmospheric Research, Boulder, CO 80301, USA
Yali Wu
Mesoscale and Microscale Meteorology Laboratory, National Center for Atmospheric Research, Boulder, CO 80301, USA
now at: Shenzhen Institute of Meteorological Innovation, Shenzhen, China
Ivette Hernández Baños
Mesoscale and Microscale Meteorology Laboratory, National Center for Atmospheric Research, Boulder, CO 80301, USA
Yonggang G. Yu
Mesoscale and Microscale Meteorology Laboratory, National Center for Atmospheric Research, Boulder, CO 80301, USA
now at: Science Applications International Corporation, Reston, VA 20190, USA
Soyoung Ha
Mesoscale and Microscale Meteorology Laboratory, National Center for Atmospheric Research, Boulder, CO 80301, USA
Yannick Trémolet
Joint Center for Satellite Data Assimilation, University Center for Atmospheric Research, Boulder, CO 80301, USA
Thomas Auligné
Joint Center for Satellite Data Assimilation, University Center for Atmospheric Research, Boulder, CO 80301, USA
Clementine Gas
Joint Center for Satellite Data Assimilation, University Center for Atmospheric Research, Boulder, CO 80301, USA
Benjamin Ménétrier
Joint Center for Satellite Data Assimilation, University Center for Atmospheric Research, Boulder, CO 80301, USA
Anna Shlyaeva
Joint Center for Satellite Data Assimilation, University Center for Atmospheric Research, Boulder, CO 80301, USA
Mark Miesch
Joint Center for Satellite Data Assimilation, University Center for Atmospheric Research, Boulder, CO 80301, USA
now at: CIRES, University of Colorado, NOAA Space Weather Prediction Center, Boulder, CO 80309, USA
Stephen Herbener
Joint Center for Satellite Data Assimilation, University Center for Atmospheric Research, Boulder, CO 80301, USA
Emily Liu
Joint Center for Satellite Data Assimilation, University Center for Atmospheric Research, Boulder, CO 80301, USA
now at: National Centers for Environmental Prediction, NOAA, College Park, MD 20740, USA
Daniel Holdaway
Joint Center for Satellite Data Assimilation, University Center for Atmospheric Research, Boulder, CO 80301, USA
now at: NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Benjamin T. Johnson
Joint Center for Satellite Data Assimilation, University Center for Atmospheric Research, Boulder, CO 80301, USA
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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.
We demonstrate an ensemble of variational data assimilations (EDA) with the Model for Prediction...