Abstract. Recent advances in statistical and machine learning have opened the possibility to forecast the behavior 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 on 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 grain 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的性能

摘要。统计和机器学习的最新进展为使用递归神经网络预测混沌系统的行为提供了可能性。在本文中，我们研究了这种框架对地球物理流的适用性，已知该结构涉及长度，时间和能量的多个尺度，并且具有间歇性。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提出了严重的限制，无论是短期预测还是潜在吸引子的重建都如此。我们建议，克服此类局限性的可能策略应基于从间歇性/小规模特征中分离出平滑的大规模动力学。我们在过去40年的全球海平面压力数据中测试了这些想法，大气环流动力学的代表。通过对空间粗粒和时间滤波的最佳选择，可以获得对海平面压力数据的更好的短期和长期预测。