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: this preprint is currently under review for the journal GMD.

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.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Meghana Velagar, Christoph Keller, and J. Nathan Kutz

Status: open (until 26 Nov 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Meghana Velagar, Christoph Keller, and J. Nathan Kutz
Meghana Velagar, Christoph Keller, and J. Nathan Kutz

Viewed

Total article views: 48 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
42 4 2 48 0 0
  • HTML: 42
  • PDF: 4
  • XML: 2
  • Total: 48
  • BibTeX: 0
  • EndNote: 0
Views and downloads (calculated since 01 Oct 2024)
Cumulative views and downloads (calculated since 01 Oct 2024)

Viewed (geographical distribution)

Total article views: 48 (including HTML, PDF, and XML) Thereof 48 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 04 Oct 2024
Download
Short summary
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 forecasting of atmospheric chemistry in a computationally tractable manner.