优化动态模式分解用于大气化学数据的重建与预测
Optimized Dynamic Mode Decomposition for Reconstruction and Forecasting of Atmospheric Chemistry Data
- University of Washington（华盛顿大学）
- Morgan State University（摩根州立大学）
- NASA Global Modelling and Assimilation Office, Goddard Space Flight Center（美国国家航空航天局戈达德航天中心全球建模与同化办公室）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出优化动态模式分解算法，构建高效降阶模型，用于全球大气化学数据的重建与预测，成功提取主要特征并适应非平稳数据。
AI中文摘要：
我们引入了优化动态模式分解算法，用于构建一个自适应且计算高效的降阶模型和全球大气化学动力学预测工具。通过利用低维的全球时空模式集合，可以计算出底层空间和时间尺度的可解释特征。预测也通过一个线性模型实现，该模型使用主导时空特征的线性叠加。DMD方法在三个月的全球化学动力学数据上进行了演示，展示了其在计算速度和可解释性方面的显著性能。我们表明，所提出的分解方法成功提取了大气化学的已知主要特征，如夏季地表污染和生物质燃烧活动。此外，DMD算法允许快速重建底层线性模型，该模型随后可以轻松适应非平稳数据和动力学变化。
英文摘要：
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.
