Recent advances in statistical and machine learning have opened the possibility of forecasting the behaviour of chaotic systems using recurrent neural networks. In this article we investigate the applicability of such a framework to geophysical flows, known to involve multiple scales in length, time and energy and to feature intermittency. We show that both multiscale dynamics and intermittency introduce severe limitations to the applicability of recurrent neural networks, both for short-term forecasts as well as for the reconstruction of the underlying attractor. We suggest that possible strategies to overcome such limitations should be based on separating the smooth large-scale dynamics from the intermittent/small-scale features. We test these ideas on global sea-level pressure data for the past 40 years, a proxy of the atmospheric circulation dynamics. Better short- and long-term forecasts of sea-level pressure data can be obtained with an optimal choice of spatial coarse graining and time filtering.

中文翻译：

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通过尺度分离提高地球物理流机器学习性能

统计和机器学习的最新进展开启了使用循环神经网络预测混沌系统行为的可能性。在本文中，我们研究了这样一个框架对地球物理流的适用性，众所周知，地球物理流涉及长度、时间和能量的多个尺度，并具有间歇性。我们表明，多尺度动力学和间歇性都对循环神经网络的适用性造成了严重的限制，无论是短期预测还是潜在吸引子的重建。我们建议克服这些限制的可能策略应该基于将平滑的大规模动态与间歇/小规模特征分开。我们在过去 40 年的全球海平面压力数据上测试了这些想法，大气环流动力学的代表。通过空间粗粒度和时间过滤的最佳选择，可以获得更好的海平面压力数据的短期和长期预测。