Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 14
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

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Cited articles

Alla, A. and Kutz, J. N.: Nonlinear Model Order Reduction via Dynamic Mode Decomposition, SIAM J. Sci. Comput., 39, B778–B796, https://doi.org/10.1137/16M1059308, 2017. a
Allen-Zhu, Z. and Li, Y.: Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning, arXiv [preprint], https://doi.org/10.48550/arXiv.2012.09816, 2020. a
Antoulas, A. C.: Approximation of Large-Scale Dynamical Systems, Society for Industrial and Applied Mathematics, https://doi.org/10.1137/1.9780898718713, 2005. a
Askham, T. and Kutz, J. N.: Variable projection methods for an optimized dynamic mode decomposition, SIAM J. Appl. Dyn. Syst., 17, 380–416, 2018. a, b, c, d
Bagheri, S.: Effects of weak noise on oscillating flows: Linking quality factor, Floquet modes, and Koopman spectrum, Phys. Fluids, 26, 094104, https://doi.org/10.1063/1.4895898, 2014. a
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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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