Abstract
In the last few years Lagrangian Monte Carlo models have been proposed as a new atmospheric dispersion modelling technique overcoming the limitations and inaccuracies inherent in Gaussian models and in models based on the advection-diffusion equation. In the present paper it is shown that these models are based on the use of stochastic differential equations (SDE) describing particle movements in a random velocity field. Some cases of 2-D inhomogeneous turbulent flows are examined in order to point out the relationships between parameters appearing in the differential equations and the statistics of particle velocity distributions. The influence of the initial distribution statistics as well as that of the driving noise are also investigated. In particular the effects produced by using a random term different from the usual white noise are shown. Finally the problem of relating Eulerian and Lagrangian quantities is examined and it is shown that SDE can represent correlated Eulerian random fields as well as Lagrangian particle evolution. The possibility of using the Eulerian field as the input for the Lagrangian equation is finally considered.
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© 1985 Plenum Press, New York
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Melli, P., Spirito, A. (1985). Atmospheric Diffusion Modelling By Stochastic Differential Equations. In: De Wispelaere, C. (eds) Air Pollution Modeling and Its Application IV. Nato — Challenges of Modern Society, vol 7. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-2455-3_42
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DOI: https://doi.org/10.1007/978-1-4613-2455-3_42
Publisher Name: Springer, Boston, MA
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