Articles | Volume 15, issue 20
https://doi.org/10.5194/gmd-15-7859-2022
© Author(s) 2022. 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-15-7859-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
26 Oct 2022
Development and technical paper |
| 26 Oct 2022
| 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
National Center for Atmospheric Research, Boulder, Colorado 80301, USA
Chris Snyder
National Center for Atmospheric Research, Boulder, Colorado 80301, USA
Jonathan J. Guerrette
National Center for Atmospheric Research, Boulder, Colorado 80301, USA
Byoung-Joo Jung
National Center for Atmospheric Research, Boulder, Colorado 80301, USA
Junmei Ban
National Center for Atmospheric Research, Boulder, Colorado 80301, USA
Steven Vahl
National Center for Atmospheric Research, Boulder, Colorado 80301, USA
now at: Joint Center for Satellite Data Assimilation, Boulder, USA
National Center for Atmospheric Research, Boulder, Colorado 80301, USA
now at: Shenzhen Institute of Meteorological Innovation, Shenzhen, China
Yannick Trémolet
Joint Center for Satellite Data Assimilation, Boulder, Colorado 80301, USA
Thomas Auligné
Joint Center for Satellite Data Assimilation, Boulder, Colorado 80301, USA
Benjamin Ménétrier
Joint Center for Satellite Data Assimilation, Boulder, Colorado 80301, USA
Anna Shlyaeva
Joint Center for Satellite Data Assimilation, Boulder, Colorado 80301, USA
Stephen Herbener
Joint Center for Satellite Data Assimilation, Boulder, Colorado 80301, USA
Emily Liu
Joint Center for Satellite Data Assimilation, Boulder, Colorado 80301, USA
now at: NOAA National Centers for Environmental Prediction, College Park, USA
Daniel Holdaway
Joint Center for Satellite Data Assimilation, Boulder, Colorado 80301, USA
now at: NASA Goddard Space Flight Center, Greenbelt, USA
Benjamin T. Johnson
Joint Center for Satellite Data Assimilation, Boulder, Colorado 80301, USA
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The assimilation of all-sky radiances in MPAS–JEDI, the data assimilation system for the Model for Prediction Across Scales–Atmosphere (MPAS-A) based on the Joint Effort for Data Assimilation Integration (JEDI), has been extended in this study to incorporate ATMS observations. Month-long cycling experiments demonstrate consistent and encouraging improvements in dynamical, thermodynamic, and moisture-related fields resulting from the assimilation of all-sky ATMS radiances.
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Geosci. Model Dev., 18, 8569–8587, https://doi.org/10.5194/gmd-18-8569-2025, https://doi.org/10.5194/gmd-18-8569-2025, 2025
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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.
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
Geosci. Model Dev., 16, 7123–7142, https://doi.org/10.5194/gmd-16-7123-2023, https://doi.org/10.5194/gmd-16-7123-2023, 2023
Short summary
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.
Cheng-Hsuan Lu, Quanhua Liu, Shih-Wei Wei, Benjamin T. Johnson, Cheng Dang, Patrick G. Stegmann, Dustin Grogan, Guoqing Ge, Ming Hu, and Michael Lueken
Geosci. Model Dev., 15, 1317–1329, https://doi.org/10.5194/gmd-15-1317-2022, https://doi.org/10.5194/gmd-15-1317-2022, 2022
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Short summary
This article is a technical note on the aerosol absorption and scattering calculations of the Community Radiative Transfer Model (CRTM) v2.2 and v2.3. It also provides guidance for prospective users of the CRTM aerosol option and Gridpoint Statistical Interpolation (GSI) aerosol-aware radiance assimilation. Scientific aspects of aerosol-affected BT in atmospheric data assimilation are also briefly discussed.
Xinghong Cheng, Zilong Hao, Zengliang Zang, Zhiquan Liu, Xiangde Xu, Shuisheng Wang, Yuelin Liu, Yiwen Hu, and Xiaodan Ma
Atmos. Chem. Phys., 21, 13747–13761, https://doi.org/10.5194/acp-21-13747-2021, https://doi.org/10.5194/acp-21-13747-2021, 2021
Short summary
Short summary
We develop a new inversion method of emission sources based on sensitivity analysis and the three-dimension variational technique. The novel explicit observation operator matrix between emission sources and the receptor’s concentrations is established. Then this method is applied to a typical heavy haze episode in North China, and spatiotemporal variations of SO2, NO2, and O3 concentrations simulated using a posterior emission sources are compared with results using an a priori inventory.
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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.
JEDI-MPAS 1.0.0, a new data assimilation (DA) system for the MPAS model, was publicly released...
