Articles | Volume 14, issue 4
https://doi.org/10.5194/gmd-14-2205-2021
https://doi.org/10.5194/gmd-14-2205-2021
Model description paper
 | 
27 Apr 2021
Model description paper |  | 27 Apr 2021

A new Lagrangian in-time particle simulation module (Itpas v1) for atmospheric particle dispersion

Matthias Faust, Ralf Wolke, Steffen Münch, Roger Funk, and Kerstin Schepanski

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Cited articles

Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., and Reinhardt, T.: Operational Convective-Scale Numerical Weather Prediction with the COSMO Model: Description and Sensitivities, Mon. Weather Rev., 139, 3887–3905, https://doi.org/10.1175/MWR-D-10-05013.1, 2011. a
Borrelli, P., Lugato, E., Montanarella, L., and Panagos, P.: A New Assessment of Soil Loss Due to Wind Erosion in European Agricultural Soils Using a Quantitative Spatially Distributed Modelling Approach, Land Degrad. Dev., 28, 335–344, https://doi.org/10.1002/ldr.2588, 2016. a
Brioude, J., Arnold, D., Stohl, A., Cassiani, M., Morton, D., Seibert, P., Angevine, W., Evan, S., Dingwell, A., Fast, J. D., Easter, R. C., Pisso, I., Burkhart, J., and Wotawa, G.: The Lagrangian particle dispersion model FLEXPART-WRF version 3.1, Geosci. Model Dev., 6, 1889–1904, https://doi.org/10.5194/gmd-6-1889-2013, 2013. a
Cassiani, M., Stohl, A., and Brioude, J.: Lagrangian Stochastic Modelling of Dispersion in the Convective Boundary Layer with Skewed Turbulence Conditions and a Vertical Density Gradient: Formulation and Implementation in the FLEXPART Model, Bound.-Lay. Meteorol., 154, 367–390, https://doi.org/10.1007/s10546-014-9976-5, 2015. a
Doms, G., Förstner, J., Heise, E., Herzog, H.-J., Mironov, D., Raschendorfer, M., Reinhardt, T., Ritter, B., Schrodin, R., Schulz, J.-P., and Vogel, G.: A Description of the Nonhydrostatic Regional COSMO Model, Part II : Physical Parameterization, Tech. rep., Consortium for Small-Scale Modelling, Deutscher Wetterdienst, https://doi.org/10.5676/dwd_pub/nwv/cosmo-doc_5.00_II, 2011. a
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Short summary
Trajectory dispersion models are powerful and intuitive tools for tracing air pollution through the atmosphere. But the turbulent nature of the atmospheric boundary layer makes it challenging to provide accurate predictions near the surface. To overcome this, we propose an approach using wind and turbulence information at high temporal resolution. Finally, we demonstrate the strength of our approach in a case study on dust emissions from agriculture.