T
Tiziano Tirabassi
Researcher at National Research Council
Publications - 68
Citations - 1284
Tiziano Tirabassi is an academic researcher from National Research Council. The author has contributed to research in topics: Convection–diffusion equation & Planetary boundary layer. The author has an hindex of 20, co-authored 66 publications receiving 1219 citations. Previous affiliations of Tiziano Tirabassi include Universidade Federal do Rio Grande do Sul.
Papers
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Journal ArticleDOI
Turbulence parameterisation for PBL dispersion models in all stability conditions
Gervásio Annes Degrazia,Domenico Anfossi,Jonas C. Carvalho,Cristina Mangia,Tiziano Tirabassi,H. F. Campos Velho +5 more
TL;DR: In this article, a new set of turbulence parameterization to be used is such models has been derived, that is, expressions for the vertical profiles of the velocity standard deviations σ i, Lagrangian length scale l Li, time scale T Li and diffusion coefficient K i, where i = 1, 2, 3.
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The state-of-art of the GILTT method to simulate pollutant dispersion in the atmosphere
TL;DR: A review of the GILTT solutions for the one and two-dimensional, time-dependent, advection-diffusion equations focusing the application to pollutant dispersion simulation in atmosphere, assuming both Fickian and counter-gradient models for a wide class of problems is presented in this article.
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Semi-analytical solution of the steady three-dimensional advection-diffusion equation in the planetary boundary layer
TL;DR: In this paper, a three-dimensional solution of the steady-state advection-diffusion equation considering a vertically inhomogeneous planetary boundary layer (PBL) was presented.
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Near-source atmospheric pollutant dispersion using the new GILTT method
TL;DR: In this paper, the GILTT method for the solution of the advection-diffusion equation utilizing an eddy diffusivity depending on source distance is presented, where no approximation is made along the solution derivation so that it is an exact solution except for the round-off error due to the stepwise approximation of the eddy diffusion in the x variable.
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Air dispersion model and neural network: A new perspective for integrated models in the simulation of complex situations
TL;DR: It is demonstrated that the use of NN in order to correct the air dispersion model could be the reasonable model combination when the air pollution model gives some systematic error with respect to experimental data.