Long-window tandem variational data assimilation methods for chaotic climate models tested with the Lorenz 63 system

Kennedy, Philip David; Banerjee, Abhirup; Köhl, Armin; Stammer, Detlef

doi:https://doi.org/10.5194/npg-32-353-2025

Articles | Volume 32, issue 3

https://doi.org/10.5194/npg-32-353-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/npg-32-353-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 32, issue 3

Research article

|

24 Sep 2025

Research article |

| 24 Sep 2025

Long-window tandem variational data assimilation methods for chaotic climate models tested with the Lorenz 63 system

Philip David Kennedy, Abhirup Banerjee, Armin Köhl, and Detlef Stammer

Model code and software

seaborn: statistical data visualization Waskom https://doi.org/10.21105/joss.03021

Pandas The pandas development team https://doi.org/10.5281/zenodo.3509134

JAX: composable trans- formations of Python+NumPy programs Bradbury et al. http://github.com/google/jax

scikit-hep/iminuit Dembinski et al. https://doi.org/10.5281/zenodo.3949207

Short summary

This work introduces and evaluates two new tandem data assimilation techniques. The first uses two synchronised forward model runs before a single adjoint model run to consistently increase the precision of the parameter estimation. The second uses a lower-resolution model with adjoint equations to drive a higher-resolution target model through data assimilation with no loss in precision compared to data assimilation without tandem methods.