Articles | Volume 12, issue 6
Geosci. Model Dev., 12, 2587–2606, 2019
https://doi.org/10.5194/gmd-12-2587-2019

Special issue: Particle-based methods for simulating atmospheric aerosol...

Geosci. Model Dev., 12, 2587–2606, 2019
https://doi.org/10.5194/gmd-12-2587-2019

Model description paper 01 Jul 2019

Model description paper | 01 Jul 2019

University of Warsaw Lagrangian Cloud Model (UWLCM) 1.0: a modern large-eddy simulation tool for warm cloud modeling with Lagrangian microphysics

Piotr Dziekan et al.

Related authors

libcloudph++ 2.0: aqueous-phase chemistry extension of the particle-based cloud microphysics scheme
Anna Jaruga and Hanna Pawlowska
Geosci. Model Dev., 11, 3623–3645, https://doi.org/10.5194/gmd-11-3623-2018,https://doi.org/10.5194/gmd-11-3623-2018, 2018
Short summary
Lagrangian condensation microphysics with Twomey CCN activation
Wojciech W. Grabowski, Piotr Dziekan, and Hanna Pawlowska
Geosci. Model Dev., 11, 103–120, https://doi.org/10.5194/gmd-11-103-2018,https://doi.org/10.5194/gmd-11-103-2018, 2018
Short summary
Stochastic coalescence in Lagrangian cloud microphysics
Piotr Dziekan and Hanna Pawlowska
Atmos. Chem. Phys., 17, 13509–13520, https://doi.org/10.5194/acp-17-13509-2017,https://doi.org/10.5194/acp-17-13509-2017, 2017
Short summary
libcloudph++ 1.0: a single-moment bulk, double-moment bulk, and particle-based warm-rain microphysics library in C++
S. Arabas, A. Jaruga, H. Pawlowska, and W. W. Grabowski
Geosci. Model Dev., 8, 1677–1707, https://doi.org/10.5194/gmd-8-1677-2015,https://doi.org/10.5194/gmd-8-1677-2015, 2015
Short summary
libmpdata++ 1.0: a library of parallel MPDATA solvers for systems of generalised transport equations
A. Jaruga, S. Arabas, D. Jarecka, H. Pawlowska, P. K. Smolarkiewicz, and M. Waruszewski
Geosci. Model Dev., 8, 1005–1032, https://doi.org/10.5194/gmd-8-1005-2015,https://doi.org/10.5194/gmd-8-1005-2015, 2015
Short summary

Related subject area

Atmospheric sciences
Novel estimation of aerosol processes with particle size distribution measurements: a case study with the TOMAS algorithm v1.0.0
Dana L. McGuffin, Yuanlong Huang, Richard C. Flagan, Tuukka Petäjä, B. Erik Ydstie, and Peter J. Adams
Geosci. Model Dev., 14, 1821–1839, https://doi.org/10.5194/gmd-14-1821-2021,https://doi.org/10.5194/gmd-14-1821-2021, 2021
Short summary
Evaluation of ECMWF IFS-AER (CAMS) operational forecasts during cycle 41r1–46r1 with calibrated ceilometer profiles over Germany
Harald Flentje, Ina Mattis, Zak Kipling, Samuel Rémy, and Werner Thomas
Geosci. Model Dev., 14, 1721–1751, https://doi.org/10.5194/gmd-14-1721-2021,https://doi.org/10.5194/gmd-14-1721-2021, 2021
Short summary
Influence of biomass burning vapor wall loss correction on modeling organic aerosols in Europe by CAMx v6.50
Jianhui Jiang, Imad El Haddad, Sebnem Aksoyoglu, Giulia Stefenelli, Amelie Bertrand, Nicolas Marchand, Francesco Canonaco, Jean-Eudes Petit, Olivier Favez, Stefania Gilardoni, Urs Baltensperger, and André S. H. Prévôt
Geosci. Model Dev., 14, 1681–1697, https://doi.org/10.5194/gmd-14-1681-2021,https://doi.org/10.5194/gmd-14-1681-2021, 2021
Short summary
Seasonal and diurnal performance of daily forecasts with WRF V3.8.1 over the United Arab Emirates
Oliver Branch, Thomas Schwitalla, Marouane Temimi, Ricardo Fonseca, Narendra Nelli, Michael Weston, Josipa Milovac, and Volker Wulfmeyer
Geosci. Model Dev., 14, 1615–1637, https://doi.org/10.5194/gmd-14-1615-2021,https://doi.org/10.5194/gmd-14-1615-2021, 2021
Short summary
MLAir (v1.0) – a tool to enable fast and flexible machine learning on air data time series
Lukas Hubert Leufen, Felix Kleinert, and Martin G. Schultz
Geosci. Model Dev., 14, 1553–1574, https://doi.org/10.5194/gmd-14-1553-2021,https://doi.org/10.5194/gmd-14-1553-2021, 2021
Short summary

Cited articles

Ackerman, A. S., Hobbs, P. V., and Toon, O. B.: A model for particle microphysics, turbulent mixing, and radiative transfer in the stratocumulus-topped marine boundary layer and comparisons with measurements, J. Atmos. Sci., 52, 1204–1236, 1995. a
Ackerman, A. S., vanZanten, M. C., Stevens, B., Savic-Jovcic, V., Bretherton, C. S., Chlond, A., Golaz, J.-C., Jiang, H., Khairoutdinov, M., Krueger, S. K., Lewellen, D. C., Lock, A., Moeng, C.-H., Nakamura, K., Petters, M. D., Snider, J. R., Weinbrecht, S., and Zulauf, M.: Large-Eddy Simulations of a Drizzling, Stratocumulus-Topped Marine Boundary Layer, Mon. Weather Rev., 137, 1083–1110, https://doi.org/10.1175/2008MWR2582.1, 2009. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r
Andrejczuk, M., Reisner, J., Henson, B., Dubey, M., and Jeffery, C.: The potential impacts of pollution on a nondrizzling stratus deck: Does aerosol number matter more than type?, J. Geophys. Res.-Atmos., 113, D19204, https://doi.org/10.1029/2007JD009445, 2008. a, b
Andrejczuk, M., Grabowski, W., Reisner, J., and Gadian, A.: Cloud-aerosol interactions for boundary layer stratocumulus in the Lagrangian Cloud Model, J. Geophys. Res.-Atmos., 115, D22214, https://doi.org/10.1029/2010JD014248, 2010. a
Arabas, S. and Pawlowska, H.: Adaptive method of lines for multi-component aerosol condensational growth and CCN activation, Geosci. Model Dev., 4, 15–31, https://doi.org/10.5194/gmd-4-15-2011, 2011. a
Download
Short summary
A new numerical model for clouds is presented. It is designed for detailed studies of the small-scale behavior of cloud droplets within a domain large enough to model cloud field. To achieve this, droplets are modeled in a Lagrangian manner and calculations are done on GPU accelerators. Comparison with models that use Eulerian descriptions of droplets reveals discrepancies in the amount of precipitation. This suggests that some effects important for rain production are missing in current models.