Articles | Volume 33, issue 1
https://doi.org/10.5194/npg-33-85-2026
https://doi.org/10.5194/npg-33-85-2026
Research article
 | 
12 Feb 2026
Research article |  | 12 Feb 2026

Dynamic mode decomposition of extreme events

Maša Ann, Jörn Behrens, and Jana Sillmann

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Cited articles

Alcayaga, L., Larsen, G. C., Kelly, M., and Mann, J.: Identification of large-scale atmospheric structures under different stability conditions using Dynamic Mode Decomposition, J. Phys. Conf. Ser., 2265, 022006, https://doi.org/10.1088/1742-6596/2265/2/022006, 2022. a
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Berkooz, G., Holmes, P., and Lumley, J. L.: The proper orthogonal decomposition in the analysis of turbulent flows, Annu. Rev. Fluid Mech., 25, 539–575, https://doi.org/10.1146/annurev.fl.25.010193.002543, 1993. a
Brunton, S. L., Proctor, J. L., and Kutz, J. N.: Discovering governing equations from data by sparse identification of nonlinear dynamical systems, P. Natl. Acad. Sci. USA, 113, 3932–3937, https://doi.org/10.1073/pnas.1517384113, 2016. a
Brunton, S. L., Budišić, M., Kaiser, E., and Kutz, J. N.: Modern Koopman Theory for Dynamical Systems, arXiv [preprint], https://doi.org/10.48550/arXiv.2102.12086, 2021. a
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Short summary
We present a new framework based on Dynamic Mode Decomposition (DMD) to better detect outliers and model extremes. Unlike standard DMD, which focuses on average system behaviour, our approach targets rare, exceptional dynamics. Applied to climate data, it improves extreme event approximation and reveals meaningful spatiotemporal patterns. The method may generalise to other types of extremes.
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