Severe weather, including tornadoes, thunderstorms, wind, and hail annually cause significant loss of life and property. We are developing spatiotemporal machine learning techniques that will enable meteorologists to improve the prediction of these events by improving their understanding of the fundamental causes of the phenomena and by building skillful empirical predictive models. In this paper, we present significant enhancements of our Spatiotemporal Relational Probability Trees that enable autonomous discovery of spatiotemporal relationships as well as learning with arbitrary shapes. We focus our evaluation on two real-world case studies using our technique: predicting tornadoes in Oklahoma and predicting aircraft turbulence in the United States. We also discuss how to evaluate success for a machine learning algorithm in the severe weather domain, which will enable new methods such as ours to transfer from research to operations, provide a set of lessons learned for embedded machine learning applications, and discuss how to field our technique.

中文翻译：

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通过时空关系学习增强对恶劣天气的理解和改进预测

包括龙卷风、雷暴、风和冰雹在内的恶劣天气每年都会造成重大的生命和财产损失。我们正在开发时空机器学习技术，使气象学家能够通过提高对现象根本原因的理解和建立熟练的经验预测模型来改进对这些事件的预测。在本文中，我们展示了我们的时空关系概率树的显着增强，它能够自主发现时空关系以及学习任意形状。我们使用我们的技术将评估重点放在两个真实世界的案例研究上：预测俄克拉荷马州的龙卷风和预测美国的飞机湍流。