Abstract
The effect of using in dispersion modeling different parameterizations for the wind velocity fluctuations standard deviations is investigated for low-wind conditions.
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Questioner Name: Jeff Weil
Q: (1) For the unstable case, have you looked at the number of independent eddies that pass your wind sensor over the averaging time? Since the large eddy scale ∼zi and the wind speed is U, the number of eddies that pass the sensor in the averaging time Tav is n ≅TavU/zi (e.g. see Briggs 1993 JAM). The lower the number, the greater is the uncertainty in the σu,v,w as your data seem to suggest.
(2) For the stable case, have you looked at the surface temperature in homogeneity as a source of horizontal wind fluctuations? For example, a velocity scale due to temperature fluctuations σθ could be given by vθ = (gLsσθ/θ)1/2, where Ls is the relevant length scale (TBD).
A: We appreciate the questions and related suggestions and we will take care of them in future research activity. However, our experience in low wind turbulence tells us that in such conditions there is not a clear distinction between unstable and stable conditions and that the mixing height zi is not a proper scaling parameter. A thorough analysis of this specific dataset confirmed that low-wind conditions are critical for the definition of the atmospheric stability, since the boundaries between stable and unstable stratification are less defined than for windy cases.
Questioner Name: Bertrand Carissimo
Q: If the measurement tower was close to the building you would not use the homogeneous terrain formula as you did. What do you think is the limit for using this formula?
A: Surely we can expect that in complex geometry the formula should be corrected to account for the presence of buildings and obstacles, for instance through morphometric parameters. We are investigating this aspect trying to estimate the possible limits of it. Up to now, a thorough analysis of the data showed that in our case the low-wind regime dominates in determining the flow dynamics and the stratification conditions with respect to the urban morphology. We found a good agreement between the observed and the predicted standard deviations. Moreover, the obstacles closest to the mast are at a distance of 70Â m and about 4Â m high. In the simulation presented here we are looking at the microscale effect on the dispersion using the observed data as input. It will be different when considering larger scales, for which the effects of the surrounding buildings can play a major role.
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Castelli, S.T., Falabino, S., Tinarelli, G., Anfossi, D. (2014). Effect of the Turbulence Parameterizations on the Simulation of Pollutant Dispersion with the RMS Modelling System. In: Steyn, D., Builtjes, P., Timmermans, R. (eds) Air Pollution Modeling and its Application XXII. NATO Science for Peace and Security Series C: Environmental Security. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5577-2_89
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DOI: https://doi.org/10.1007/978-94-007-5577-2_89
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