论文标题
天气和气候的机器学习应用需要更加专注于极端
Machine learning applications for weather and climate need greater focus on extremes
论文作者
论文摘要
现在，多项研究表明，机器学习（ML）可以提供改进的技能，以预测或模拟相当典型的天气事件，例如短期和季节性天气预测，将模拟降低模拟以提高分辨率，并效仿和加速昂贵的模型参数。其中许多使用的ML方法具有很高的参数，例如神经网络，这是这里讨论的重点。对于许多关键天气和气候预测应用的极端事件严重性，这些方法的性能并没有太多关注。这给这些方法的有用性留下了很多不确定性，特别是对于必须在极端情况下可靠执行的通用预测系统。由于通常很少有此类事件的样本，因此可能会预计ML模型会难以预测极端。但是，有些研究确实表明ML模型可以在极端天气方面具有合理的技能，并且在需要推断的情况下使用它们并非绝望。本文回顾了这些研究，并认为这是需要更多研究的领域。讨论了ML模型在预测极端天气事件中的表现如何，可以更好地了解。
Multiple studies have now demonstrated that machine learning (ML) can give improved skill for predicting or simulating fairly typical weather events, for tasks such as short-term and seasonal weather forecasting, downscaling simulations to higher resolution and emulating and speeding up expensive model parameterisations. Many of these used ML methods with very high numbers of parameters, such as neural networks, which are the focus of the discussion here. Not much attention has been given to the performance of these methods for extreme event severities of relevance for many critical weather and climate prediction applications, with return periods of more than a few years. This leaves a lot of uncertainty about the usefulness of these methods, particularly for general purpose prediction systems that must perform reliably in extreme situations. ML models may be expected to struggle to predict extremes due to there usually being few samples of such events. However, there are some studies that do indicate that ML models can have reasonable skill for extreme weather, and that it is not hopeless to use them in situations requiring extrapolation. This article reviews these studies and argues that this is an area that needs researching more. Ways to get a better understanding of how well ML models perform at predicting extreme weather events are discussed.
