Evaluation of the Lagrangian particle dispersion model DIPCOT against data from wind tunnel simulations of quasi two-dimensional turbulent flow

2005 ◽  
Vol 24 (1/2/3/4) ◽  
pp. 114 ◽  
Author(s):  
Efstratios Davakis ◽  
Spyros Andronopoulos ◽  
George A. Sideridis ◽  
Eleftherios G. Kastrinakis ◽  
Stavros G. Nychas ◽  
...  
Keyword(s):  
Wind Tunnel ◽  
Turbulent Flow ◽  
Dispersion Model ◽  
Particle Dispersion ◽  
Two Dimensional ◽  
Lagrangian Particle

Validation of a Lagrangian particle dispersion model with wind tunnel and field experiments in urban environment

2018 ◽  
Vol 193 ◽  
pp. 273-289 ◽  
Author(s):  
S. Trini Castelli ◽  
P. Armand ◽  
G. Tinarelli ◽  
C. Duchenne ◽  
M. Nibart
Keyword(s):  
Wind Tunnel ◽  
Urban Environment ◽  
Field Experiments ◽  
Dispersion Model ◽  
Particle Dispersion ◽  
Lagrangian Particle

Tracking source area of Shangdianzi station using Lagrangian particle dispersion model of FLEXPART

2013 ◽  
Vol 21 (3) ◽  
pp. 466-473 ◽  
Author(s):  
Xingqin An ◽  
Bo Yao ◽  
Yan Li ◽  
Nan Li ◽  
Lingxi Zhou
Keyword(s):  
Dispersion Model ◽  
Source Area ◽  
Particle Dispersion ◽  
Lagrangian Particle

Simulation of a dense gas chlorine release with a Lagrangian particle dispersion model (LPDM)

2021 ◽  
Vol 244 ◽  
pp. 117791 ◽  
Author(s):  
Félix Gomez ◽  
Bruno Ribstein ◽  
Laurent Makké ◽  
Patrick Armand ◽  
Jacques Moussafir ◽  
...  
Keyword(s):  
Dispersion Model ◽  
Particle Dispersion ◽  
Dense Gas ◽  
Lagrangian Particle

scholarly journals Greenhouse gas network design using backward Lagrangian particle dispersion modelling − Part 1: Methodology and Australian test case

2014 ◽  
Vol 14 (17) ◽  
pp. 9363-9378 ◽  
Author(s):  
T. Ziehn ◽  
A. Nickless ◽  
P. J. Rayner ◽  
R. M. Law ◽  
G. Roff ◽  
...  
Keyword(s):  
Network Design ◽  
Fossil Fuel ◽  
Dispersion Model ◽  
Surface Flux ◽  
Particle Dispersion ◽  
Surface Fluxes ◽  
Test Case ◽  
Lagrangian Particle ◽  
Gas Network ◽  
Australian Continent

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.

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