用端到端模型从气候数据中识别极端事件的时空驱动因素。
Identifying Spatio-Temporal Drivers of Extreme Events
- 联合预测极端事件与驱动因子的时空分布
- 在三个合成基准上准确识别出相关驱动因子
- 适合气候建模与极端事件分析的研究者使用
极端事件及其驱动因素在气候数据中的时空关系尚未被充分理解，亟需机器学习方法从数据中挖掘此类关系。然而，该任务极具挑战性，因为极端事件与其驱动因素之间存在时间延迟，且驱动因素的空间响应具有非均质性。本文提出首个针对该挑战的方法并构建了基准测试。所提方法采用端到端训练，联合预测物理输入变量中的时空极端事件与时空驱动因子。通过强制网络仅基于已识别驱动因子的时空二值掩码来预测极端事件，模型成功捕捉到与极端事件相关的驱动因素。我们在三个新创建的合成基准上评估该方法，其中两个基于遥感或再分析气候数据，另在两个真实世界再分析数据集上进行验证。源代码与数据集已在项目页面公开：https://hakamshams.github.io/IDE。
原文摘要 · Abstract (English)
The spatio-temporal relations of impacts of extreme events and their drivers in climate data are not fully understood and there is a need of machine learning approaches to identify such spatio-temporal relations from data. The task, however, is very challenging since there are time delays between extremes and their drivers, and the spatial response of such drivers is inhomogeneous. In this work, we propose a first approach and benchmarks to tackle this challenge. Our approach is trained end-to-end to predict spatio-temporally extremes and spatio-temporally drivers in the physical input variables jointly. By enforcing the network to predict extremes from spatio-temporal binary masks of identified drivers, the network successfully identifies drivers that are correlated with extremes. We evaluate our approach on three newly created synthetic benchmarks, where two of them are based on remote sensing or reanalysis climate data, and on two real-world reanalysis datasets. The source code and datasets are publicly available at the project page https://hakamshams.github.io/IDE.
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