S2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts
S2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts
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DOI：
10.1002/wcc.567
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发表时间：
2019-03-01
影响因子：
9.2
通讯作者：
Tziperman, Eli
中科院分区：
文献类型：
--
作者：
Cohen, Judah;Coumou, Dim;Tziperman, Eli
季节性气候预测的学科始于简单的统计技术。然而，今天的大型政府预报中心几乎完全依赖于复杂的完全耦合的动力学预报系统，其亚季节到季节（S2 S）的预测，而统计技术大多被忽视，这些技术仍然在使用几十年没有更新。在这篇观点文章中，我们认为，新的统计技术主要是在气候科学领域之外开发的，统称为机器学习，可以被气候预报员采用，以提高S2 S预测的准确性。我们提出了一个例子，其中无监督学习在季节预测中表现出比最先进的动态系统更高的准确性。我们还总结了一些最适用于气候预测的相关机器学习方法。最后，我们通过将实时动力模式预报与2017/2018年冬季的观测结果进行比较，表明动力模式预报对极涡（PV）变率及其对可感天气的影响几乎完全不敏感。相反，统计预报更准确地预测了由此产生的明智的天气从仲冬光伏中断比动态预报。动力学预报不佳的重要意义是，如果北极变化通过PV变率影响中纬度天气，那么动力学模型证明这种路径存在的能力就会受到损害。我们的结论是，S2 S预测将是最有益的公众通过结合混合或混合的动态预测和更新的统计技术，如机器学习。这篇文章被分类下：气候模型和建模>知识生成与模型
The discipline of seasonal climate prediction began as an exercise in simple statistical techniques. However, today the large government forecast centers almost exclusively rely on complex fully coupled dynamical forecast systems for their subseasonal to seasonal (S2S) predictions while statistical techniques are mostly neglected and those techniques still in use have not been updated in decades. In this Opinion Article, we argue that new statistical techniques mostly developed outside the field of climate science, collectively referred to as machine learning, can be adopted by climate forecasters to increase the accuracy of S2S predictions. We present an example of where unsupervised learning demonstrates higher accuracy in a seasonal prediction than the state-of-the-art dynamical systems. We also summarize some relevant machine learning methods that are most applicable to climate prediction. Finally, we show by comparing real-time dynamical model forecasts with observations from winter 2017/2018 that dynamical model forecasts are almost entirely insensitive to polar vortex (PV) variability and the impact on sensible weather. Instead, statistical forecasts more accurately predicted the resultant sensible weather from a mid-winter PV disruption than the dynamical forecasts. The important implication from the poor dynamical forecasts is that if Arctic change influences mid-latitude weather through PV variability, then the ability of dynamical models to demonstrate the existence of such a pathway is compromised. We conclude by suggesting that S2S prediction will be most beneficial to the public by incorporating mixed or a hybrid of dynamical forecasts and updated statistical techniques such as machine learning. This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models