scholarly journals Linking a Lagrangian Particle Dispersion Model with Three-Dimensional Eulerian Wind Field Models

2008 ◽  
Vol 47 (9) ◽  
pp. 2463-2467 ◽  
Author(s):  
Jeffrey C. Weil

Abstract A slightly simplified form of Thomson’s Lagrangian stochastic model (LSM) is presented for dispersion applications in three-dimensional (3D) flow fields. It is found that the Lagrangian velocity of a particle in 3D inhomogeneous Gaussian turbulence can be decomposed into the local Eulerian mean velocity UEi at the particle position and a velocity perturbation u′i. The Eulerian mean wind can be predicted by 3D wind field models, whereas the u′i is obtained from Thomson’s model and depends on the turbulence field. The UEi, u′i decomposition was used earlier in a two-dimensional particle model for a canopy (by Flesch and Wilson) and in models with 3D mean winds but with u′i based on LSM forms differing from that of Thomson. This note shows that the UEi, u′i decomposition is consistent with Thomson’s LSM for general 3D flow fields and is a simpler solution that should lead to improved computational efficiency for dispersion applications.

2013 ◽  
Vol 21 (3) ◽  
pp. 466-473 ◽  
Author(s):  
Xingqin An ◽  
Bo Yao ◽  
Yan Li ◽  
Nan Li ◽  
Lingxi Zhou

2021 ◽  
Vol 244 ◽  
pp. 117791 ◽  
Author(s):  
Félix Gomez ◽  
Bruno Ribstein ◽  
Laurent Makké ◽  
Patrick Armand ◽  
Jacques Moussafir ◽  
...  

1997 ◽  
Vol 15 (4) ◽  
pp. 476-486 ◽  
Author(s):  
J. Camps ◽  
J. Massons ◽  
M. R. Soler ◽  
E. C. Nickerson

Abstract. A three-dimensional meteorological model and a Lagrangian particle dispersion model are used to study the effects of a uniform large-scale wind on the dispersion of a non-reactive pollutant in a coastal region with complex terrain. Simulations are carried out both with and without a background wind. A comparison between model results and measured data (wind and pollutant concentrations) indicates that the coupled model system provides a useful mechanism for analyzing pollutant dispersion in coastal regions.


2014 ◽  
Vol 14 (17) ◽  
pp. 9363-9378 ◽  
Author(s):  
T. Ziehn ◽  
A. Nickless ◽  
P. J. Rayner ◽  
R. M. Law ◽  
G. Roff ◽  
...  

Abstract. This paper describes the generation of optimal atmospheric measurement networks for determining carbon dioxide fluxes over Australia using inverse methods. A Lagrangian particle dispersion model is used in reverse mode together with a Bayesian inverse modelling framework to calculate the relationship between weekly surface fluxes, comprising contributions from the biosphere and fossil fuel combustion, and hourly concentration observations for the Australian continent. Meteorological driving fields are provided by the regional version of the Australian Community Climate and Earth System Simulator (ACCESS) at 12 km resolution at an hourly timescale. Prior uncertainties are derived on a weekly timescale for biosphere fluxes and fossil fuel emissions from high-resolution model runs using the Community Atmosphere Biosphere Land Exchange (CABLE) model and the Fossil Fuel Data Assimilation System (FFDAS) respectively. The influence from outside the modelled domain is investigated, but proves to be negligible for the network design. Existing ground-based measurement stations in Australia are assessed in terms of their ability to constrain local flux estimates from the land. We find that the six stations that are currently operational are already able to reduce the uncertainties on surface flux estimates by about 30%. A candidate list of 59 stations is generated based on logistic constraints and an incremental optimisation scheme is used to extend the network of existing stations. In order to achieve an uncertainty reduction of about 50%, we need to double the number of measurement stations in Australia. Assuming equal data uncertainties for all sites, new stations would be mainly located in the northern and eastern part of the continent.


2005 ◽  
Vol 24 (1/2/3/4) ◽  
pp. 114 ◽  
Author(s):  
Efstratios Davakis ◽  
Spyros Andronopoulos ◽  
George A. Sideridis ◽  
Eleftherios G. Kastrinakis ◽  
Stavros G. Nychas ◽  
...  

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