Optimized dynamic mode decomposition for reconstruction and forecasting of atmospheric chemistry data

Abstract

We introduce the optimized dynamic mode decomposition (DMD) 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 3 months of global chemistry dynamics data, showing its significant performance in terms of computational speed and interpretability. We show that the presented decomposition method successfully extracts and forecasts chemical patterns for leading chemical indicators, including nitric oxide, ozone, nitrogen dioxide, hydroxyl radical, isoprene, and carbon monoxide. 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.