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GMD | Articles | Volume 12, issue 10
Geosci. Model Dev., 12, 4245–4259, 2019
https://doi.org/10.5194/gmd-12-4245-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue: The Lagrangian particle dispersion model FLEXPART

Geosci. Model Dev., 12, 4245–4259, 2019
https://doi.org/10.5194/gmd-12-4245-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Model description paper 09 Oct 2019

Model description paper | 09 Oct 2019

Development of turbulent scheme in the FLEXPART-AROME v1.2.1 Lagrangian particle dispersion model

Bert Verreyken et al.

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Akritidis, D., Zanis, P., Pytharoulis, I., Mavrakis, A., and Karacostas, T.: A deep stratospheric intrusion event down to the Earth's surface of the megacity of Athens, Meteorol. Atmos. Phys., 109, 9–18, https://doi.org/10.1007/s00703-010-0096-6, 2012. a
Alam, J. M. and Lin, J. C.: Toward a Fully Lagrangian Atmospheric Modeling System, Mon. Weather Rev., 136, 4653–4667, https://doi.org/10.1175/2008MWR2515.1, 2008. a
Baray, J.-L., Courcoux, Y., Keckhut, P., Portafaix, T., Tulet, P., Cammas, J.-P., Hauchecorne, A., Godin Beekmann, S., De Mazière, M., Hermans, C., Desmet, F., Sellegri, K., Colomb, A., Ramonet, M., Sciare, J., Vuillemin, C., Hoareau, C., Dionisi, D., Duflot, V., Vérèmes, H., Porteneuve, J., Gabarrot, F., Gaudo, T., Metzger, J.-M., Payen, G., Leclair de Bellevue, J., Barthe, C., Posny, F., Ricaud, P., Abchiche, A., and Delmas, R.: Maïdo observatory: a new high-altitude station facility at Reunion Island (21 S, 55 E) for long-term atmospheric remote sensing and in situ measurements, Atmos. Meas. Tech., 6, 2865–2877, https://doi.org/10.5194/amt-6-2865-2013, 2013. a
Bertò, A., Buzzi, A., and Zardi, D.: Back-tracking water vapour contributing to a precipitation event over Trentino: a case study, Meteorol. Z., 13, 189–200, https://doi.org/10.1127/0941-2948/2004/0013-0189, 2004. a
Bougeault, P. and Lacarrère, P.: Parameterization of Orography-Induced Turbulence in a Mesobeta–Scale Model, Mon. Weather Rev., 117, 1872–1890, https://doi.org/10.1175/1520-0493(1989)117<1872:POOITI>2.0.CO;2, 1989. a
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The Lagrangian particle dispersion model FLEXPART-AROME was built to study air mass transport around La Réunion, a volcanic island in the southwest Indian Ocean. To harmonize turbulent transport between the numerical weather prediction model and the transport model, turbulent kinetic energy from AROME is directly used in FLEXPART-AROME using discrete interfaces between different turbulent regions. An adaptive time step was implemented to satisfy physical constraints on turbulent transport.
The Lagrangian particle dispersion model FLEXPART-AROME was built to study air mass transport...
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