用数据驱动方法追踪火山喷发后气候影响的时空路径。
Spatio-temporal Multivariate Cluster Evolution Analysis for Detecting and Tracking Climate Impacts
- 结合异常检测与聚类，用NLP挖掘多变量气候集群演化模式。
- 成功识别1991年皮纳图博火山喷发后的已知气候影响事件。
- 适合研究气候因果链、极端事件追踪的研究者使用。
近年来，气候变化及其影响日益受到关注。尽管地球系统模型（ESMs）在研究气候变化影响方面具有重要价值，但其内部复杂的耦合过程、海量数据以及地球系统的高内部变异性，常使关键的源-影响关系难以识别。本文提出一种新颖且高效的无监督数据驱动方法，通过融合异常检测、聚类与自然语言处理（NLP）的思想，实现对气候中统计显著影响的探测及时空源-影响路径的追踪。以菲律宾1991年皮纳图博火山喷发为例，验证了该方法能有效识别喷发后的已知气候影响事件。此外，提出一种利用NLP高效挖掘频繁多变量集群演化的技术，可提取有意义的喷发后影响序列，用于确认或发现气候源与影响之间的物理过程链。
原文摘要 · Abstract (English)
Recent years have seen a growing concern about climate change and its impacts. While Earth System Models (ESMs) can be invaluable tools for studying the impacts of climate change, the complex coupling processes encoded in ESMs and the large amounts of data produced by these models, together with the high internal variability of the Earth system, can obscure important source-to-impact relationships. This paper presents a novel and efficient unsupervised data-driven approach for detecting statistically-significant impacts and tracing spatio-temporal source-impact pathways in the climate through a unique combination of ideas from anomaly detection, clustering and Natural Language Processing (NLP). Using as an exemplar the 1991 eruption of Mount Pinatubo in the Philippines, we demonstrate that the proposed approach is capable of detecting known post-eruption impacts/events. We additionally describe a methodology for extracting meaningful sequences of post-eruption impacts/events by using NLP to efficiently mine frequent multivariate cluster evolutions, which can be used to confirm or discover the chain of physical processes between a climate source and its impact(s).
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