GPU-accelerated three-dimensional variational data assimilation with automatic differentiation for WRF-Chem

Ye, Hancheng; Zang, Zengliang; You, Wei; Hu, Yiwen; Liu, Ning; Li, Yi

doi:10.5194/gmd-19-10263-2026

Articles | Volume 19, issue 20

https://doi.org/10.5194/gmd-19-10263-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-19-10263-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 20

Development and technical paper

|

28 Oct 2026

Development and technical paper |

| 28 Oct 2026

GPU-accelerated three-dimensional variational data assimilation with automatic differentiation for WRF-Chem

Hancheng Ye, Zengliang Zang, Wei You, Yiwen Hu, Ning Liu, and Yi Li

Data sets

Geosci_Model_Dev_egusphere-2026-394_PY3DVar_and_FT3DVar_code_and_observation_data Hancheng Ye https://doi.org/10.5281/zenodo.23077072

Model code and software

Geosci_Model_Dev_egusphere-2026-394_PY3DVar_and_FT3DVar_code_and_observation_data Hancheng Ye https://doi.org/10.5281/zenodo.23077072

Short summary

Our study pioneers a PyTorch-based atmospheric pollutant assimilation system that leverages automatic differentiation and Graphics Processing Unit (GPU) acceleration to achieve order-of-magnitude speedups, while maintaining analysis accuracy consistent with the traditional Fortran-based system.