用自监督方法将气温数据聚类为有意义的气候状态，便于分析和预测。
Deep Clustering for Climate: Analyzing Teleconnections through Learned Categorical States
- 用掩码孪生网络将日温数据离散化为语义丰富的聚类。
- 聚类结果与厄尔尼诺事件有统计关联，反映真实气候特征。
- 适合气候建模、极端天气分析的研究者使用。
理解与表征复杂气候变异性对科学分析和预测建模至关重要。然而，从原始变量中识别有意义的气候态具有挑战性，因其存在高噪声和非线性依赖。本文探索使用掩码孪生网络将气候时间序列离散化为语义丰富的聚类。以日最低温和最高温为例，结果表明：(i) 聚类反映在模型假设下的有意义气候状态，为下游应用提供简化表示；(ii) 支持特定气候情景的采样与分析；(iii) 与厄尔尼诺事件存在统计关联，凸显其科学意义。研究揭示了自监督离散化在气候数据分析中的潜力，并为未来引入更丰富气候指标开辟路径。
原文摘要 · Abstract (English)
Understanding and representing complex climate variability is essential for both scientific analysis and predictive modeling. However, identifying meaningful climate regimes from raw variables is challenging, as they exhibit high noise and nonlinear dependencies. In this work, we explore the use of Masked Siamese Networks to discretize climate time series into semantically rich clusters. Focusing on daily minimum and maximum temperature, we show that the resulting representations: (i) yield clusters that reflect meaningful climate states under our modeling assumptions, offering a simplified representation for downstream use; (ii) enable sampling and analysis of specific climate scenarios; and (iii) exhibit statistical associations with El Niño events, underscoring their scientific relevance. Our findings highlight the potential of self-supervised discretization as a tool for climate data analysis and open avenues for incorporating richer climate indicators in future work.
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