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Preprints
https://doi.org/10.5194/gmd-2024-77
https://doi.org/10.5194/gmd-2024-77
Submitted as: development and technical paper
 | 
01 Oct 2024
Submitted as: development and technical paper |  | 01 Oct 2024
Status: a revised version of this preprint was accepted for the journal GMD and is expected to appear here in due course.

Optimized Dynamic Mode Decomposition for Reconstruction and Forecasting of Atmospheric Chemistry Data

Meghana Velagar, Christoph Keller, and J. Nathan Kutz

Abstract. We introduce the optimized dynamic mode decomposition algorithm for constructing an adaptive and computationally efficient reduced order model and forecasting tool for global atmospheric chemistry dynamics. By exploiting a low-dimensional set of global spatio-temporal modes, interpretable characterizations of the underlying spatial and temporal scales can be computed. Forecasting is also achieved with a linear model that uses a linear superposition of the dominant spatio-temporal features. The DMD method is demonstrated on three months of global chemistry dynamics data, showing its significant performance in computational speed and interpretability. We show that the presented decomposition method successfully extracts known major features of atmospheric chemistry, such as summertime surface pollution and biomass burning activities. Moreover, the DMD algorithm allows for rapid reconstruction of the underlying linear model, which can then easily accommodate non-stationary data and changes in the dynamics.

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We develop the data-driven method of dynamic mode decomposition for producing a robust and...
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