采用新型机器学习方法揭示中纬度大气变率的重现型态
Revealing recurrent regimes of mid-latitude atmospheric variability using novel machine learning method
AI总结：
针对温带大气低频变率型态的数据驱动识别难题，提出结合重现状态网络划分、重现定量分析与核主成分分析的新方法，可检测大气重现型态并获取其嵌入动力学变量，在模型与再分析数据中验证有效，为大气环流型态建模与预报提供新方向。
摘要：
温带大气的低频变率涉及半球尺度的重现性、通常具有持续性的状态，被称为遥相关型或大气型态，这类状态对季节内及更长时间尺度的可预报性具有深远影响。然而，对此类状态进行可靠的数据驱动识别与动力学表征，仍是大气动力学建模领域的挑战性问题。我们提出了一种新方法，既能检测大气变率的重现型态，也能获取可作为这些型态嵌入表示的动力学变量。该方法结合了非线性数据分析领域的两种思路：对重现状态网络进行划分，同时通过重现定量分析与核主成分分析研究其性质。我们将该方法应用于两项研究：一是温带半球大气环流准地转模型中的遥相关型研究，二是1981年至今冬季北半球中纬度大气位势高度异常的再分析数据研究。结果表明，检测到的型态以及获取的动力学变量集合能够解释大尺度天气型，这些天气型尤其与欧亚大陆和北美地区的寒冬相关。本研究提出的方法为改进大尺度大气环流型态的经验建模与长期预报开辟了前景。
英文摘要：
The low frequency variability of the extratropical atmosphere involves hemispheric-scale recurring, often persistent, states known as teleconnection patterns or regimes, which can have profound impact on predictability on intra-seasonal and longer timescales. However, reliable data-driven identification and dynamical representation of such states are still challenging problems in modeling dynamics of the atmosphere. We present a new method, which allows both to detect recurring regimes of atmospheric variability, and to obtain dynamical variables serving as an embedding for these regimes. The method combines two approaches from nonlinear data analysis: partitioning a network of recurrent states with studying its properties by the recurrence quantification analysis and the kernel principal component analysis. We apply the method to study teleconnection patterns in a quasi-geostrophical model of atmospheric circulation over the extratropical hemisphere as well as to reanalysis data of geopotential height anomalies in the mid-latitudes of the Northern Hemisphere atmosphere in the winter seasons from 1981 to the present. It is shown that the detected regimes as well as the obtained set of dynamical variables explain large-scale weather patterns, which are associated, in particular, with severe winters over Eurasia and North America. The method presented opens prospects for improving empirical modeling and long-term forecasting of large-scale atmospheric circulation regimes.
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