论文标题
通过无监督的机器学习了解极端降水的变化
Understanding Extreme Precipitation Changes through Unsupervised Machine Learning
论文作者
论文摘要
尽管重要的是量化极端降水的空间模式如何随着变暖的变化而变化，但我们缺乏客观地分析现代气候模型的风暴规模产量的工具。为了解决这一差距，我们开发了一个无监督的机器学习框架，以量化暴风雨动态如何影响极端降水的变化，而无需牺牲空间信息。对于上降水量的分位数（高于80％的），我们发现极端降水变化的空间模式由风暴动力学的空间变化而不是这些风暴制度产生降水的方式的变化所占据主导地位。我们的研究表明，无监督的机器学习如何与领域知识配对，可以使我们能够更好地了解大气的物理学，并预测与温暖世界相关的变化。
Despite the importance of quantifying how the spatial patterns of extreme precipitation will change with warming, we lack tools to objectively analyze the storm-scale outputs of modern climate models. To address this gap, we develop an unsupervised machine learning framework to quantify how storm dynamics affect changes in precipitation extremes, without sacrificing spatial information. For the upper precipitation quantiles (above the 80th percentile), we find that the spatial patterns of extreme precipitation changes are dominated by spatial shifts in storm dynamical regimes rather than changes in how these storm regimes produce precipitation. Our study shows how unsupervised machine learning, paired with domain knowledge, may allow us to better understand the physics of the atmosphere and anticipate the changes associated with a warming world.
