Two extreme CO2 concentration events were detected in February 2004 at the mountain station of Plateau Rosa in the Italian Alps. The meteorological model WRF was used to study the evolution of the meteorological fields and to identify the deterministic trajectories of the polluted air masses. Here, the case study is examined applying two Lagrangian particle dispersion models in backward mode, in order to trace the provenience of the CO2 mass. FLEXPART-WRF model was used at the regional scale, MILORD model was applied on long-range. The deterministic trajectories are verified against the models’ results. Despite the differences in scales and parameterizations, the two models provide very similar patterns of the particle clouds.

Detection of CO2 source areas using two lagrangian particle dispersion models, at regional scale and long range

S. Ferrarese;S. Trini Castelli
2019

Abstract

Two extreme CO2 concentration events were detected in February 2004 at the mountain station of Plateau Rosa in the Italian Alps. The meteorological model WRF was used to study the evolution of the meteorological fields and to identify the deterministic trajectories of the polluted air masses. Here, the case study is examined applying two Lagrangian particle dispersion models in backward mode, in order to trace the provenience of the CO2 mass. FLEXPART-WRF model was used at the regional scale, MILORD model was applied on long-range. The deterministic trajectories are verified against the models’ results. Despite the differences in scales and parameterizations, the two models provide very similar patterns of the particle clouds.
19th International Conference On Harmonisation Within Atmospheric Dispersion Modelling For Regulatory Purposes, Harmo 2019
Bruges, Belgium
3-6 June 2019
Proceedings
1
5
http://www.harmo.org/Conferences/Proceedings/_Bruges/publishedSections/H19-038 Silvia Trini Castelli.pdf
S. Ferrarese, S. Trini Castelli
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1762213
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