Articles | Volume 14, issue 7
https://doi.org/10.5194/gmd-14-4401-2021
© Author(s) 2021. 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-14-4401-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| Highlight paper
| 
16 Jul 2021
Development and technical paper | Highlight paper |
| 16 Jul 2021
| 16 Jul 2021
fv3gfs-wrapper: a Python wrapper of the FV3GFS atmospheric model
Vulcan Inc., Seattle, WA, USA
Noah D. Brenowitz
Vulcan Inc., Seattle, WA, USA
Mark Cheeseman
Vulcan Inc., Seattle, WA, USA
Spencer K. Clark
Vulcan Inc., Seattle, WA, USA
Geophysical Fluid Dynamics Laboratory, NOAA, Princeton, NJ, USA
Johann P. S. Dahm
Vulcan Inc., Seattle, WA, USA
Eddie C. Davis
Vulcan Inc., Seattle, WA, USA
Oliver D. Elbert
Vulcan Inc., Seattle, WA, USA
Geophysical Fluid Dynamics Laboratory, NOAA, Princeton, NJ, USA
Rhea C. George
Vulcan Inc., Seattle, WA, USA
Lucas M. Harris
Geophysical Fluid Dynamics Laboratory, NOAA, Princeton, NJ, USA
Brian Henn
Vulcan Inc., Seattle, WA, USA
Anna Kwa
Vulcan Inc., Seattle, WA, USA
W. Andre Perkins
Vulcan Inc., Seattle, WA, USA
Oliver Watt-Meyer
Vulcan Inc., Seattle, WA, USA
Tobias F. Wicky
Vulcan Inc., Seattle, WA, USA
Christopher S. Bretherton
Vulcan Inc., Seattle, WA, USA
Department of Atmospheric Sciences, University of Washington, Seattle, WA, USA
Oliver Fuhrer
Vulcan Inc., Seattle, WA, USA
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Total article views: 4,742 (including HTML, PDF, and XML)
Cumulative views and downloads
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Total article views: 1,715 (including HTML, PDF, and XML)
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Total article views: 6,457 (including HTML, PDF, and XML)
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Total article views: 4,742 (including HTML, PDF, and XML)
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Total article views: 1,715 (including HTML, PDF, and XML)
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Cited
9 citations as recorded by crossref.
- Correcting a 200 km Resolution Climate Model in Multiple Climates by Machine Learning From 25 km Resolution Simulations S. Clark et al. 10.1029/2022MS003219
- The Common Community Physics Package (CCPP) Framework v6 D. Heinzeller et al. 10.5194/gmd-16-2235-2023
- Pace v0.2: a Python-based performance-portable atmospheric model J. Dahm et al. 10.5194/gmd-16-2719-2023
- Stochastic Parameterization of Moist Physics Using Probabilistic Diffusion Model L. Wang et al. 10.3390/atmos15101219
- Correcting Coarse‐Grid Weather and Climate Models by Machine Learning From Global Storm‐Resolving Simulations C. Bretherton et al. 10.1029/2021MS002794
- A review of solar forecasting, its dependence on atmospheric sciences and implications for grid integration: Towards carbon neutrality D. Yang et al. 10.1016/j.rser.2022.112348
- Correcting Weather and Climate Models by Machine Learning Nudged Historical Simulations O. Watt‐Meyer et al. 10.1029/2021GL092555
- Exploring a high-level programming model for the NWP domain using ECMWF microphysics schemes S. Ubbiali et al. 10.5194/gmd-18-529-2025
- Machine‐Learned Climate Model Corrections From a Global Storm‐Resolving Model: Performance Across the Annual Cycle A. Kwa et al. 10.1029/2022MS003400
9 citations as recorded by crossref.
- Correcting a 200 km Resolution Climate Model in Multiple Climates by Machine Learning From 25 km Resolution Simulations S. Clark et al. 10.1029/2022MS003219
- The Common Community Physics Package (CCPP) Framework v6 D. Heinzeller et al. 10.5194/gmd-16-2235-2023
- Pace v0.2: a Python-based performance-portable atmospheric model J. Dahm et al. 10.5194/gmd-16-2719-2023
- Stochastic Parameterization of Moist Physics Using Probabilistic Diffusion Model L. Wang et al. 10.3390/atmos15101219
- Correcting Coarse‐Grid Weather and Climate Models by Machine Learning From Global Storm‐Resolving Simulations C. Bretherton et al. 10.1029/2021MS002794
- A review of solar forecasting, its dependence on atmospheric sciences and implications for grid integration: Towards carbon neutrality D. Yang et al. 10.1016/j.rser.2022.112348
- Correcting Weather and Climate Models by Machine Learning Nudged Historical Simulations O. Watt‐Meyer et al. 10.1029/2021GL092555
- Exploring a high-level programming model for the NWP domain using ECMWF microphysics schemes S. Ubbiali et al. 10.5194/gmd-18-529-2025
- Machine‐Learned Climate Model Corrections From a Global Storm‐Resolving Model: Performance Across the Annual Cycle A. Kwa et al. 10.1029/2022MS003400
Latest update: 03 May 2025
Short summary
FV3GFS is a weather and climate model written in Fortran. It uses Fortran so that it can run fast, but this makes it hard to add features if you do not (or even if you do) know Fortran. We have written a Python interface to FV3GFS that lets you import the Fortran model as a Python package. We show examples of how this is used to write
modelscripts, which reproduce or build on what the Fortran model can do. You could do this same wrapping for any compiled model, not just FV3GFS.
FV3GFS is a weather and climate model written in Fortran. It uses Fortran so that it can run...
