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.

优化的动态模态分解算法用于构建自适应且具有高计算效率的全球大气化学动力学的降阶模型和预测工具，通过利用低维度的全球时空模态，可以计算出对应的可解释的空间和时间尺度特征，并通过线性模型实现预测。该方法在三个月的全球化学动力学数据上展示了其在计算速度和可解释性方面的显著性能，成功提取了大气化学的知名主要特征，如夏季地表污染和生物质燃烧活动，并且动态模态分解算法可以迅速重构基础线性模型，以适应非平稳数据和动态变化。