Advancements in satellite technology yield environmental data with ever improving spatial coverage and temporal resolution. This necessitates the development of techniques to discern actionable information from large amounts of such data. We explore the potential of dynamic mode decomposition (DMD) to discover the dynamics of spatially correlated structures present in global-scale data, specifically in observations of total water storage anomalies provided by GRACE satellite missions. Our results demonstrate that DMD enables data compression and extrapolation from a reduced set of dominant spatiotemporal structures. The accuracy of its predictions of global system dynamics is preserved in its reconstruction of local time series. These findings suggest potential uses of DMD in analysis of remote-sensing data for hydrologic applications.

中文翻译：

---

GRACE 卫星数据的动态模态分解


卫星技术的进步产生了环境数据，空间覆盖范围和时间分辨率不断提高。这需要开发技术，从大量此类数据中识别可操作的信息。我们探索了动态模态分解 （DMD） 的潜力，以发现全球尺度数据中存在的空间相关结构的动力学，特别是在对 GRACE 卫星任务提供的总储水异常的观测中。我们的结果表明，DMD 能够从一组减少的主导时空结构中进行数据压缩和推断。它对全球系统动力学的预测的准确性在其对局部时间序列的重建中得以保留。这些发现表明 DMD 在水文应用遥感数据分析中的潜在用途。