Yongxiang Huang

Second-order structure function in fully developed turbulence.

We relate the second-order structure function of a time series with the power spectrum of the original variable, taking an assumption of statistical stationarity. With this approach, we find that the structure function is strongly influenced by the large scales. The large-scale contribution and the contribution range are, respectively, 79% and 1.4 decades for a Kolmogorov -5/3 power spectrum. We show numerically that a single scale influence range, over smaller scales is about 2 decades. We argue that the structure function is not a good method to extract the scaling exponents when the data possess large energetic scales. An alternative methodology, the arbitrary order Hilbert spectral analysis which may constrain this influence within 0.3 decade, is proposed to characterize the scaling property directly in an amplitude-frequency space. An analysis of passive scalar (temperature) turbulence time series is presented to show the influence of large-scale structures in real turbulence and the efficiency of the Hilbert-based methodology. The corresponding scaling exponents ζ(θ)(q) provided by the Hilbert-based approach indicate that the passive scalar turbulence field may be less intermittent than what was previously believed.

Scaling of maximum probability density functions of velocity and temperature increments in turbulent systems

In this paper, we introduce a new way to estimate the scaling parameter of a self-similar process by considering the maximum probability density function (pdf) of its increments. We prove this for H-self-similar processes in general and experimentally investigate it for turbulent velocity and temperature increments. We consider turbulent velocity database from an experimental homogeneous and nearly isotropic turbulent channel flow, and temperature data set obtained near the sidewall of a Rayleigh-Benard convection cell, where the turbulent flow is driven by buoyancy. For the former database, it is found that the maximum value of increment pdf pmax(τ) is in a good agreement with lognormal distribution. We also obtain a scaling exponent α≃0.37, which is consistent with the scaling exponent for the first-order structure function reported in other studies. For the latter one, we obtain a scaling exponent αθ≃0.33. This index value is consistent with the Kolmogorov-Obukhov-Corrsin scaling for passive scalar tur...

Analysis of Nonlinear Biophysical Time Series in Aquatic Environments: Scaling Properties and Empirical Mode Decomposition

Aquatic environmental time series often display large fluctuations at many time scales, possessing stochastic properties, as well as deterministic forcing coming from seasonal or annual meteorological and climatic cycles. In this work we are interested in the characterization of these properties, using different statistical tools, borrowed from the field of turbulence, or of nonlinear time series analysis. We first present the analysis of a long (30 years) time series of daily river flow data, recorded in the Seine River (France). We consider the scale dependence and scale invariance of river flow data, using structure function analysis; we also apply a decomposition method called Empirical Mode Decomposition (EMD). We then consider the statistical properties, and the nonlinear dynamics behaviour of a long-term copepod (small crustaceans) time series sampled every week in the Meditarranean sea from 1967 to 1992. We first consider its high variability and characterize its properties, including extreme evens obeying power law tail pdf. We then consider their scale dependence, using Fourier power spectra together with an EMD approach.

Scaling and variability of biophysical time series in aquatic environments

Preface.- Introducing Networks in Climate Studies.- Two Paradigms in Landscape Dynamics: Self-Similar Processes and Emergence.- Effects of Systematic and Random Errors on the Spatial Scaling Properties in Radar-Estimated Rainfall.- Nonlinear Dynamics in the Earths Magnetosphere.- Microseism Activity and Equilibrium Fluctuations.- An Exponential Langevin-type Model for Rainfall Exhibiting Spatial and Temporal Scaling.- Storm Tracking and Ensemble Prediction .- Towards a Nonlinear Geophysical Theory of Floods in River Networks: An Overview of 20 Years of Progress.- Investigations of Wave-induced Nonlinear Response of Minor Species with the KBM Averaging Method.- ENSO Signal Propagation Detected by Wavelet Coherence and Mean Phase Coherence Methods.- Twenty-Five Years of Nonlinearity in Oceanography from the Lagrangian Perspective.- Self-Scaling of the Statistical Properties of a Minimal Model of the Atmospheric Circulation .- Hindcast AGCM Experiments on the Predictability of Stratospheric Sudden Warming.- Self Organized Criticality and/or Low Dimensional Chaos in Second Earthquake Processes: Theory and Practice in Hellenic Region.- Analysis of Nonlinear Biophysical Time Series in Aquatic Environments: Scaling Properties and Empirical Mode Decomposition.- The Arctic Ocean as a Coupled Oscillating System to the Forced 18.6 Year Lunar Gravity Cycle.- Dynamical Synchronization of Truth and Model as an Approach to Data Assimilation, Parameter Estimation, and Model Learning.- Scale, Scaling and Multifractals in Geosciences: Twenty Years On.- Statistics of Return Intervals and Extreme Events in Long-Term Correlated Time Series.- Statistical Properties of Mid-Latitude Atmospheric Variability.- On the Spatiotemporal Variability of the Temperature Anomaly Field.- Time Evolution of the Fractal Dimension of Electric Self-Potential Time Series.- Diffusion Entropy Analysis in Seismicity.- Snow Avalanches as a Non-Critical, Punctuated Equilibrium System.- Evidence fromWavelet Lag Coherence for Negligible Solar Forcing of Climate at Multi-year and Decadal Periods .- From Diversity to Volatility: Probability of Daily Precipitation Extremes.- Stochastic Linear Models of Nonlinear Geosystems.- Reducing Forecast Uncertainty to Understand Atmospheric Flow Transitions.- The Role of El Nino-Southern Oscillation in Regulating its Background State.- Nonlinear Dynamics of Natural Hazards .- Predicting the Multifractal Geomagnetic Field.- Index.