Articles | Volume 8, issue 6
https://doi.org/10.5194/gmd-8-1677-2015
https://doi.org/10.5194/gmd-8-1677-2015
Model description paper
 | 
09 Jun 2015
Model description paper |  | 09 Jun 2015

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

Related authors

Breakups are complicated: an efficient representation of collisional breakup in the superdroplet method
Emily de Jong, John Ben Mackay, Oleksii Bulenok, Anna Jaruga, and Sylwester Arabas
Geosci. Model Dev., 16, 4193–4211, https://doi.org/10.5194/gmd-16-4193-2023,https://doi.org/10.5194/gmd-16-4193-2023, 2023
Short summary
On numerical broadening of particle-size spectra: a condensational growth study using PyMPDATA 1.0
Michael A. Olesik, Jakub Banaśkiewicz, Piotr Bartman, Manuel Baumgartner, Simon Unterstrasser, and Sylwester Arabas
Geosci. Model Dev., 15, 3879–3899, https://doi.org/10.5194/gmd-15-3879-2022,https://doi.org/10.5194/gmd-15-3879-2022, 2022
Short summary
On the CCN (de)activation nonlinearities
Sylwester Arabas and Shin-ichiro Shima
Nonlin. Processes Geophys., 24, 535–542, https://doi.org/10.5194/npg-24-535-2017,https://doi.org/10.5194/npg-24-535-2017, 2017
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
Accounting for effects of coagulation and model uncertainties in particle number concentration estimates based on measurements from sampling lines – a Bayesian inversion approach with SLIC v1.0
Matti Niskanen, Aku Seppänen, Henri Oikarinen, Miska Olin, Panu Karjalainen, Santtu Mikkonen, and Kari Lehtinen
Geosci. Model Dev., 18, 2983–3001, https://doi.org/10.5194/gmd-18-2983-2025,https://doi.org/10.5194/gmd-18-2983-2025, 2025
Short summary
Top-down CO emission estimates using TROPOMI CO data in the TM5-4DVAR (r1258) inverse modeling suit
Johann Rasmus Nüß, Nikos Daskalakis, Fabian Günther Piwowarczyk, Angelos Gkouvousis, Oliver Schneising, Michael Buchwitz, Maria Kanakidou, Maarten C. Krol, and Mihalis Vrekoussis
Geosci. Model Dev., 18, 2861–2890, https://doi.org/10.5194/gmd-18-2861-2025,https://doi.org/10.5194/gmd-18-2861-2025, 2025
Short summary
The Multi-Compartment Hg Modeling and Analysis Project (MCHgMAP): mercury modeling to support international environmental policy
Ashu Dastoor, Hélène Angot, Johannes Bieser, Flora Brocza, Brock Edwards, Aryeh Feinberg, Xinbin Feng, Benjamin Geyman, Charikleia Gournia, Yipeng He, Ian M. Hedgecock, Ilia Ilyin, Jane Kirk, Che-Jen Lin, Igor Lehnherr, Robert Mason, David McLagan, Marilena Muntean, Peter Rafaj, Eric M. Roy, Andrei Ryjkov, Noelle E. Selin, Francesco De Simone, Anne L. Soerensen, Frits Steenhuisen, Oleg Travnikov, Shuxiao Wang, Xun Wang, Simon Wilson, Rosa Wu, Qingru Wu, Yanxu Zhang, Jun Zhou, Wei Zhu, and Scott Zolkos
Geosci. Model Dev., 18, 2747–2860, https://doi.org/10.5194/gmd-18-2747-2025,https://doi.org/10.5194/gmd-18-2747-2025, 2025
Short summary
Similarity-based analysis of atmospheric organic compounds for machine learning applications
Hilda Sandström and Patrick Rinke
Geosci. Model Dev., 18, 2701–2724, https://doi.org/10.5194/gmd-18-2701-2025,https://doi.org/10.5194/gmd-18-2701-2025, 2025
Short summary
Porting the Meso-NH atmospheric model on different GPU architectures for the next generation of supercomputers (version MESONH-v55-OpenACC)
Juan Escobar, Philippe Wautelet, Joris Pianezze, Florian Pantillon, Thibaut Dauhut, Christelle Barthe, and Jean-Pierre Chaboureau
Geosci. Model Dev., 18, 2679–2700, https://doi.org/10.5194/gmd-18-2679-2025,https://doi.org/10.5194/gmd-18-2679-2025, 2025
Short summary

Cited articles

Ahnert, K. and Mulansky, M.: Boost.Numeric.Odeint: solving ordinary differential equations, in: Boost Library Documentation, available at: http://www.boost.org/doc/libs/ (last access: 15 November 2014), 2013.
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., 113, D19204, https://doi.org/10.1029/2007JD009445, 2008.
Andrejczuk, M., Grabowski, W., Reisner, J., and Gadian, A.: Cloud-aerosol interactions for boundary layer stratocumulus in the Lagrangian Cloud Model, J. Geophys. Res., 115, D22214, https://doi.org/10.1029/2010JD014248, 2010.
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.
Download
Short summary
This paper introduces a free and open-source C++ library of algorithms for representing cloud microphysics in numerical models. In the current release, the library covers three warm-rain schemes: the single- and double-moment bulk schemes, and the particle-based scheme with Monte Carlo coalescence. The three schemes are intended for modelling frameworks of different dimensionalities and complexities ranging from parcel models to multi-dimensional cloud-resolving (e.g. large-eddy) simulations.
Share