Estimation Of The Lagrangian Structure Function Constant C0 From Surface-Layer Wind Data

Anfossi, D.; Degrazia, Gervásio Annes; Ferrero, E.; Gryning, Sven‐Erik; Morselli, M. G.; Castelli, Silvia

doi:10.1023/a:1002697221093

Cited by 26 publications

(17 citation statements)

References 37 publications

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“…where T is the characteristic time-scale, which is often estimated considering the value for which R i (τ ) is equal to 1/e (Hanna, 1981;Kaimal and Finnigan, 1994;Anfossi et al, 2000). Equation 2 can be proved true for the vertical component but it is a less general result for the two horizontal components .…”

Section: Eulerian Autocorrelation Functionsmentioning

confidence: 99%

Low‐frequency processes and turbulence structure in a perturbed boundary layer

Mortarini

Ferrero

Falabino

et al. 2012

Quart J Royal Meteoro Soc

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An analysis of the turbulence structure in a perturbed boundary layer and in low-wind regimes is presented. The study is based on 15 months of continuous wind and turbulence measurements gathered, within the framework of the Urban Turbulence Project, at three levels (5, 9 and 25 m) on a mast located in the outskirts of the city of Turin (Italy). The aim of the work is to investigate low-frequency processes in a perturbed boundary-layer. In fact, the urban canopy and the heat island, together with frequent low-wind conditions, interact with and modify the turbulence structure. In order to investigate this modification, the velocity Eulerian autocorrelation functions together with both the Eulerian and Lagrangian timescales are shown and compared with the classical theory. The comparisons show that in low-wind cases the velocity autocorrelation functions are not simply exponential but present an oscillating behaviour. A method of normalization is proposed together with an analysis on the applicability of this function. The estimated Lagrangian timescales are compared with two widely used parametrizations. It is found that the presence of the urban fabric influences the turbulence time-scales and suggests the development of new parametrizations. Finally, higher-order statistics are evaluated and the relationship between higher-order and lower-order moments are analysed, pointing out the effects due to the urban environment.

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Section: Eulerian Autocorrelation Functionsmentioning

confidence: 99%

Low‐frequency processes and turbulence structure in a perturbed boundary layer

Mortarini

Ferrero

Falabino

et al. 2012

Quart J Royal Meteoro Soc

Self Cite

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“…In all, 27 days of measurements (from April 22 to May 17) were considered in the present analysis, and are the same data previously examined by Anfossi et al (2000). As in that paper, the full data record was broken into 1-hour segments each containing 72,000 raw data of wind (u, v, w) and temperature (T) observations.…”

Section: Tisby Datasetmentioning

confidence: 99%

An Analysis Of Sonic Anemometer Observations In Low Wind Speed Conditions

Anfossi

Oettl

Degrazia

et al. 2005

Boundary-Layer Meteorology

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184

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When the wind speed decreases below a certain value (1-2 m s À1 ) meandering (low frequency horizontal wind oscillations) starts to prevail. In these conditions it becomes difficult to define a precise mean wind direction and to estimate the airborne dispersion. To study the wind and turbulence characteristics during meandering, two sonic anemometer datasets, containing hourly wind observations, were analysed: the first one, lasting 1 year, was recorded in complex terrain (Graz, Austria) and the second one, lasting about 1 month, was recorded in a rather flat area (Tisby, Sweden). It was found that meandering seems to exist under all meteorological conditions regardless of the stability or wind speed and it was confirmed that meandering sets a lower limit for the horizontal wind component variances. Further, it was found that the autocorrelation functions of the horizontal wind components, computed for the low wind cases, show an oscillating behaviour with the presence of large negative lobes. Two different relationships from the literature, and relevant to these oscillatory aspects, were fitted to the data. They contain two parameters: one associated and relevant to the classical integral time scale and the second with meandering occurrence. Based on these relationships, expressions for the mean square displacement of particles r 2 y ðtÞ were also derived.

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“…Wilson and Sawford, 1996;Degrazia and Anfossi, 1998;Anfossi et al, 2000), eðzÞ is the ensemble-average rate of dissipation of turbulent kinetic energy, DW is a random number with zero mean, a variance equal to Dt v , and a Gaussian p.d.f., and the time-step Dt v is given by…”

Section: Vertical Dispersionmentioning

confidence: 99%

Dispersion modelling of air pollution caused by road traffic using a Markov Chain-Monte Carlo model

Oettl¹,

Almbauer²,

Sturm³

et al. 2003

Stochastic Environmental Research and Risk Assessment (SERRA)

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Although the strict legislation regarding vehicle emissions in Europe (EURO 4, EURO 5) will lead to a remarkable reduction of emissions in the near future, traffic related air pollution still can be problematic due to a large increase of traffic in certain areas. Many dispersion models for line-sources have been developed to assess the impact of traffic on the air pollution levels near roads, which are in most cases based on the Gaussian equation. Previous studies gave evidence, that such kind of models tend to overestimate concentrations in low wind speed conditions or when the wind direction is almost parallel to the street orientation. This is of particular interest, since such conditions lead generally to the highest observed concentrations in the vicinity of streets. As many air quality directives impose limits on high percentiles of concentrations, it is important to have good estimates of these quantities in environmental assessment studies. The objective of this study is to evaluate a methodology for the computation of especially those high percentiles required by e.g. the EU daughter directive 99/30/EC (for instance the 99.8 percentile for NO 2 ). The model used in this investigation is a Markov Chain -Monte Carlo model to predict pollutant concentrations, which performs well in low wind conditions as is shown here. While usual Lagrangian models use deterministic time steps for the calculation of the turbulent velocities, the model presented here, uses random time steps from a Monte Carlo simulation and a Markov Chain simulation for the sequence of the turbulent velocities. This results in a physically better approach when modelling the dispersion in low wind speed conditions. When Lagrangian dispersion models are used for regulatory purposes, a meteorological pre-processor is necessary to obtain required input quantities like MoninObukhov length and friction velocity from routinely observed data. The model and the meteorological pre-processor applied here, were tested against field data taken near a major motorway south of Vienna. The methodology used is based on input parameters, which are also available in usual environmental assess-

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Estimation Of The Lagrangian Structure Function Constant C0 From Surface-Layer Wind Data

Cited by 26 publications

References 37 publications

Low‐frequency processes and turbulence structure in a perturbed boundary layer

Low‐frequency processes and turbulence structure in a perturbed boundary layer

An Analysis Of Sonic Anemometer Observations In Low Wind Speed Conditions

Dispersion modelling of air pollution caused by road traffic using a Markov Chain-Monte Carlo model

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