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

• URL: http://arxiv.org/abs/2404.12396v1
• Date: Sat, 13 Apr 2024 15:44:12 GMT
• Title: Optimized Dynamic Mode Decomposition for Reconstruction and Forecasting of Atmospheric Chemistry Data
• Authors: Meghana Velegar, Christoph Keller, J. Nathan Kutz,
• Abstract summary: We introduce the optimized dynamic mode decomposition for constructing an adaptive and efficient reduced order model and forecasting tool. We show that the DMD algorithm successfully extracts known features of atmospheric chemistry, such as summertime surface pollution and biomass burning activities.
• Score: 3.1484174280822845
• License: http://creativecommons.org/licenses/by/4.0/
• 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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