Nonlinear Processes in Geophysics
Nonlinear Processes in Geophysics
NPG
Articles | Volume 33, issue 1
Articles | Volume 33, issue 1
https://doi.org/10.5194/npg-33-85-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-33-85-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 33, issue 1
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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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
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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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