Optimized dynamic mode decomposition for reconstruction  and forecasting of atmospheric chemistry data

Velagar, Meghana; Keller, Christoph; Kutz, J. Nathan

doi:https://doi.org/10.5194/gmd-18-4667-2025

Articles | Volume 18, issue 14

https://doi.org/10.5194/gmd-18-4667-2025

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

https://doi.org/10.5194/gmd-18-4667-2025

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

Articles | Volume 18, issue 14

Development and technical paper

|

30 Jul 2025

Development and technical paper |

| 30 Jul 2025

Optimized dynamic mode decomposition for reconstruction and forecasting of atmospheric chemistry data

Meghana Velagar, Christoph Keller, and J. Nathan Kutz

Data sets

Dynamic Mode Decomposition Data and Code for Atmospheric Chemistry M. Velaghar and J. N. Kutz https://doi.org/10.5281/zenodo.12754943

Model code and software

Dynamic Mode Decomposition Data and Code for Atmospheric Chemistry M. Velaghar and J. N. Kutz https://github.com/mvelegar/DMDPaper

Short summary

We develop the data-driven method of dynamic mode decomposition for producing a robust and stable surrogate reduced-order model of atmospheric chemistry dynamics. The model is computationally efficient, provides interpretable patterns of activity, and produces uncertainty quantification metrics. It is ideal for the forecasting of atmospheric chemistry in a computationally tractable manner.