Published July 12, 2026 | Version 1.0.0
Software Open

VVMex v1.0.0: GPU-capable refactoring of the Vector Vorticity cloud-resolving Model

Authors/Creators

Contributors

Researcher:

Description

VVMex is a GPU-capable, object-oriented C++ refactoring of the Vector Vorticity cloud-resolving Model for large-eddy and cloud-resolving simulations on heterogeneous high-performance computing systems. This release contains the source code, default experiment configurations, selected input files, lookup tables, radiation coefficient files, and regression-test baselines used for the VVMex v1.0 software release.

Files

Aaron-Hsieh-0129/VVMex-v1.0.0.zip

Files (60.6 MB)

Name Size Download all
md5:ec9693fb1360be4e0d46493342cf1a6d
60.6 MB Preview Download

Additional details

Related works

Is supplement to
Software: https://github.com/Aaron-Hsieh-0129/VVMex/tree/v1.0.0 (URL)

Software

Repository URL
https://github.com/Aaron-Hsieh-0129/VVMex
Programming language
C++ , Fortran
Development Status
Active

References

  • Jung, J.-H., and Arakawa, A. (2008). A three-dimensional anelastic model based on the vorticity equation. Monthly Weather Review, 136, 276–294. https://doi.org/10.1175/2007MWR2095.1
  • Wu, C.-M., and Arakawa, A. (2011). Inclusion of surface topography into the vector vorticity equation model (VVM). Journal of Advances in Modeling Earth Systems, 3, M06001. https://doi.org/10.1029/2011MS000061
  • E3SM Project (2024). Energy Exascale Earth System Model (E3SM). Computer Software. https://doi.org/10.11578/E3SM/dc.20240301.3
  • Donahue, A. S., et al. (2024). To exascale and beyond: The Simple Cloud-Resolving E3SM Atmosphere Model. Journal of Advances in Modeling Earth Systems. https://doi.org/10.1029/2024MS004314
  • Trott, C. R., Lebrun-Grandié, D., Arndt, D., Ciesko, J., Dang, V., Ellingwood, N., Gayatri, R., Harvey, E., Hollman, D. S., Ibanez, D., Liber, N., Madsen, J., Miles, J., Poliakoff, D., Powell, A., Rajamanickam, S., Simberg, M., Sunderland, D., Turcksin, B., and Wilke, J. (2022). Kokkos 3: Programming model extensions for the exascale era. IEEE Transactions on Parallel and Distributed Systems, 33, 805–817. https://doi.org/10.1109/TPDS.2021.3097283
  • Godoy, W. F., Podhorszki, N., Wang, R., et al. (2020). ADIOS 2: The Adaptable Input Output System. A framework for high-performance data management. SoftwareX, 12, 100561. https://doi.org/10.1016/j.softx.2020.100561
  • Chen, F., and Dudhia, J. (2001). Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 Modeling System. Part I: Model implementation and sensitivity. Monthly Weather Review, 129, 569–585. https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2
  • Morrison, H., and Milbrandt, J. A. (2015). Parameterization of cloud microphysics based on the prediction of bulk ice particle properties. Part I: Scheme description and idealized tests. Journal of the Atmospheric Sciences, 72, 287–311. https://doi.org/10.1175/JAS-D-14-0065.1
  • Pincus, R., Mlawer, E. J., and Delamere, J. S. (2019). Balancing accuracy, efficiency, and flexibility in radiation calculations for dynamical models. Journal of Advances in Modeling Earth Systems, 11, 3074–3089. https://doi.org/10.1029/2019MS001621