Zappala Dario A, Barreiro Marcelo, Masoller Cristina
Departament de Fisica, Universitat Politecnica de Catalunya, St. Nebridi 22, 08222 Terrassa, Barcelona, Spain.
Instituto de Fisica, Facultad de Ciencias, Universidad de la Republica, Igua 4225, Montevideo 11400, Uruguay.
Chaos. 2020 Jan;30(1):011103. doi: 10.1063/1.5140620.
In the analysis of empirical signals, detecting correlations that capture genuine interactions between the elements of a complex system is a challenging task with applications across disciplines. Here, we analyze a global dataset of surface air temperature (SAT) with daily resolution. Hilbert analysis is used to obtain phase, instantaneous frequency, and amplitude information of SAT seasonal cycles in different geographical zones. The analysis of the phase dynamics reveals large regions with coherent seasonality. The analysis of the instantaneous frequencies uncovers clean wave patterns formed by alternating regions of negative and positive correlations. In contrast, the analysis of the amplitude dynamics uncovers wave patterns with additional large-scale structures. These structures are interpreted as due to the fact that the amplitude dynamics is affected by processes that act in long and short time scales, while the dynamics of the instantaneous frequency is mainly governed by fast processes. Therefore, Hilbert analysis allows us to disentangle climatic processes and to track planetary atmospheric waves. Our results are relevant for the analysis of complex oscillatory signals because they offer a general strategy for uncovering interactions that act at different time scales.
在实证信号分析中，检测能够捕捉复杂系统各要素之间真实相互作用的相关性是一项具有挑战性的任务，其应用涵盖多个学科。在此，我们分析了一个具有日分辨率的全球地表气温（SAT）数据集。希尔伯特分析用于获取不同地理区域SAT季节周期的相位、瞬时频率和振幅信息。相位动力学分析揭示了具有连贯季节性的大片区域。瞬时频率分析揭示了由正负相关交替区域形成的清晰波动模式。相比之下，振幅动力学分析揭示了具有额外大规模结构的波动模式。这些结构被解释为是由于振幅动力学受到在长时间和短时间尺度上起作用的过程的影响，而瞬时频率的动力学主要由快速过程主导。因此，希尔伯特分析使我们能够区分气候过程并追踪行星大气波。我们的结果与复杂振荡信号的分析相关，因为它们提供了一种揭示在不同时间尺度上起作用的相互作用的通用策略。
