大气流动自适应网络的确定性混沌模型
Deterministic Chaos Model for Self-Organized Adaptive Networks in Atmospheric Flows
摘要：
从毫米到数千公里大小的波动的合作存在所产生的大气流动的复杂的时空模式被发现表现出长期的空间和时间的相关性表现为自相似的分形几何的全球云量模式和反幂律形式的大气涡旋能量谱。这种长距离的空间和时间相关性在扩展的自然动力系统中是普遍存在的，并且是以确定性混沌或自组织临界性为特征的奇异吸引子设计的签名。全球大气环流的统一网络类似于人脑的神经网络。
英文摘要：
The complex spatiotemporal patterns of atmospheric flows resulting from the cooperative existence of fluctuations ranging in size from millimeters to thousands of kilometers are found to exhibit long-range spatial and temporal correlations manifested as the selfsimilar fractal geometry to the global cloud cover pattern and the inverse power law form for the atmospheric eddy energy spectrum. Such long-range spatial and temporal correlations are ubiquitous to extended natural dynamical systems and is a signature of the strange attractor design characterizing deterministic chaos or self-organized criticality. The unified network of global atmospheric circulations is analogous to the neural networks of the human brain.
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