Articles | Volume 18, issue 14
https://doi.org/10.5194/gmd-18-4667-2025
https://doi.org/10.5194/gmd-18-4667-2025
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

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Latest update: 02 Oct 2025
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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.
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