提出新方法识别热浪短期驱动因子，提升极端气候事件预测能力。
Identifying Key Drivers of Heatwaves: A Novel Spatio-Temporal Framework for Extreme Event Detection
- 结合聚类与进化算法，构建时空联合分析框架
- 在意大利阿达河盆地识别出影响热浪的关键变量及时间滞后
- 适合气候建模、灾害预警领域研究人员参考
热浪是造成重大社会与环境影响的极端大气事件。传统统计和动力模型难以捕捉其与大尺度气候变量间的复杂交互关系，导致预测困难。本文提出一种通用的极端气候事件驱动因子识别方法，构建了新型时空框架STCO-FS，通过聚类算法对空间数据降维，将相似地理节点合并，并结合集成进化算法，在时空域中筛选关键预测变量，识别变量与热浪发生间最优时间滞后。该方法应用于意大利阿达河盆地的热浪分析，有效识别出该区域影响热浪的关键变量，有助于深化对热浪驱动机制的理解并提升可预测性。
原文摘要 · Abstract (English)
Heatwaves (HWs) are extreme atmospheric events that produce significant societal and environmental impacts. Predicting these extreme events remains challenging, as their complex interactions with large-scale atmospheric and climatic variables are difficult to capture with traditional statistical and dynamical models. This work presents a general method for driver identification in extreme climate events. A novel framework (STCO-FS) is proposed to identify key immediate (short-term) HW drivers by combining clustering algorithms with an ensemble evolutionary algorithm. The framework analyzes spatio-temporal data, reduces dimensionality by grouping similar geographical nodes for each variable, and develops driver selection in spatial and temporal domains, identifying the best time lags between predictive variables and HW occurrences. The proposed method has been applied to analyze HWs in the Adda river basin in Italy. The approach effectively identifies significant variables influencing HWs in this region. This research can potentially enhance our understanding of HW drivers and predictability.
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