Integrating the Urban Canopy Layer in a Lagrangian Particle Dispersion Model

Author(s):  
Stefan Stöckl ◽  
Mathias W. Rotach ◽  
Natascha Kljun

<p>Traditional Lagrangian particle dispersion models reflect particles at the zero-plane displacement height and therefore cannot properly take near-ground effects into account. In this study, we investigate whether including the urban canopy layer improves the performance of such a Lagrangian particle dispersion model. Here, spatially averaged flow and turbulence profiles throughout the urban canopy are constructed based on data from the literature (mostly from wind tunnel and numerical modeling studies).</p><p>We apply a first-order approach to test to what degree the explicit inclusion of the urban canopy changes the simulated concentration distributions. In a comprehensive sensitivity study, we show that most of the parameters introduced to describe the turbulence and flow profiles in the canopy have a relatively minor impact on the dispersion (and hence concentration distribution) – despite their inherent uncertainty. In particular, concentration fields are more sensitive to previously existing parameters of the model. One exception is a parameter describing the mean canopy wind speed profile, to which the model is sensitive.</p><p>When compared to data from the BUBBLE tracer experiment, the results show that the inclusion of the urban canopy layer slightly improves the modelled concentration values. The improvement is minor and might likely differ when comparing with other field experiments. However, the key point here is that the increased complexity and added capability of near-ground concentration simulation did not fundamentally change the model performance.</p><p>Ultimately, inclusion of the urban canopy layer will allow the model to be used as the dispersion core for an urban footprint model with footprint estimates near the ground.</p>

2018 ◽  
Vol 193 ◽  
pp. 273-289 ◽  
Author(s):  
S. Trini Castelli ◽  
P. Armand ◽  
G. Tinarelli ◽  
C. Duchenne ◽  
M. Nibart

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 ◽  
...  

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.


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