Intrinsic non-stationarity of carbon dioxide turbulent fluxes in the urban boundary layer

Abstract. Stationarity is a critical assumption in the eddy-covariance method that is widely used to calculate turbulent fluxes. Many methods have been proposed to diagnose non-stationarity attributed to external non-turbulent flows. In this paper, we focus on intrinsic non-stationarity (IN) attributed to turbulence randomness. The detrended fluctuation analysis is used to quantify IN of CO2 turbulent fluxes in the downtown of Beijing. Results show that the IN is widespread in CO2 turbulent fluxes and is a small-scale phenomenon related to the inertial sub-range turbulence. The small-scale IN of CO2 turbulent fluxes can be simulated by the Ornstein-Uhlenbeck (OU) process as a first approximation. Basing on the simulation results, we find that the average time should be greater than 27 s to avoid the effects of IN. Besides, the non-stationarity diagnosis methods that do not take into account IN would possibly make a wrong diagnosis with some parameters.

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Multifractal analysis of velocity and temperature fluctuations in the atmospheric surface layer

10.5194/egusphere-egu2020-7220 ◽

2020 ◽

Author(s):

Ganapati Sahoo ◽

Soumak Bhattacharjee ◽

Timo Vesala ◽

Rahul Pandit

Keyword(s):

Time Series ◽

Turbulent Flows ◽

Detrended Fluctuation Analysis ◽

Multifractal Analysis ◽

Temperature Time Series ◽

Temperature Fields ◽

Fluctuation Analysis ◽

Fluid Turbulence ◽

Detrended Fluctuation ◽

Temperature Time

The characterization of the structure of non-stationary, noisy fluctuations in a time series, e.g., the time series of the velocity components or temperature in turbulent flows, is a problem of central importance in fluid dynamics, nonequilibrium statistical mechanics, atmospheric physics and climate science. Over the past few decades, a variety of statistical techniques, like detrended fluctuation analysis (DFA), have been used to reveal intricate, multiscaling properties of such time series. We present an analysis of velocity and temperature time series, which have been obtained by measurements over the canopy of Hyyti&#228;l&#228; Forest in Finland. In our study we use DFA, its generalization, namely, multifractal detrended fluctuation analysis (MFDFA), and the recently developed multiscale multifractal analysis (MMA), which is an extension of MFDFA. These methods allow us to characterize the rich hierarchy or multi- fractality of the dynamics of the time series of the velocity components and the temperature. In particular, we can clearly distinguish these time series from white noise and the signals that display simple, monofractal, scaling with a single exponent (also called the Hurst exponent). It is useful to recall that monofractal scaling is predicted for fluid turbulence at the level of the Kolmogorov&#8217;s phenomenological approach of 1941 (K41); experiments and direct numerical simulations suggest that three-dimensional (3D) fluid turbulence must be characterised by a hierarchy of exponents for it is truly multifractal.We present an analysis of multifractality of velocity and temperature fields that have been measured, at different heights, over the canopy of Hyyti&#228;l&#228; Forest in Finland. In particular, we carry out a detailed study of velocity and temperature time series by using MFDFA and MMA. Results from both these methods are consistent, as they must be; but, of course, the MMA results contain more information because they account for the dependence of the multifractality on the time intervals.

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