Articles | Volume 10, issue 8
Geosci. Model Dev., 10, 3145–3165, 2017
Geosci. Model Dev., 10, 3145–3165, 2017
Model description paper
28 Aug 2017
Model description paper | 28 Aug 2017

MicroHH 1.0: a computational fluid dynamics code for direct numerical simulation and large-eddy simulation of atmospheric boundary layer flows

Chiel C. van Heerwaarden et al.

Related authors

A new accurate low-cost instrument for fast synchronized spatial measurements of light spectra
Bert G. Heusinkveld, Wouter B. Mol, and Chiel C. van Heerwaarden
EGUsphere,,, 2022
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Evaluation of two common source estimation measurement strategies using large-eddy simulation of plume dispersion under neutral atmospheric conditions
Anja Ražnjević, Chiel van Heerwaarden, and Maarten Krol
Atmos. Meas. Tech., 15, 3611–3628,,, 2022
Short summary
Technical note: Interpretation of field observations of point-source methane plume using observation-driven large-eddy simulations
Anja Ražnjević, Chiel van Heerwaarden, Bart van Stratum, Arjan Hensen, Ilona Velzeboer, Pim van den Bulk, and Maarten Krol
Atmos. Chem. Phys., 22, 6489–6505,,, 2022
Short summary
Development of a large-eddy simulation subgrid model based on artificial neural networks: a case study of turbulent channel flow
Robin Stoffer, Caspar M. van Leeuwen, Damian Podareanu, Valeriu Codreanu, Menno A. Veerman, Martin Janssens, Oscar K. Hartogensis, and Chiel C. van Heerwaarden
Geosci. Model Dev., 14, 3769–3788,,, 2021
Short summary
Understanding wind-driven melt of patchy snow cover
Luuk D. van der Valk, Adriaan J. Teuling, Luc Girod, Norbert Pirk, Robin Stoffer, and Chiel C. van Heerwaarden
The Cryosphere Discuss.,,, 2021
Revised manuscript accepted for TC
Short summary

Related subject area

Atmospheric sciences
Downscaling atmospheric chemistry simulations with physically consistent deep learning
Andrew Geiss, Sam J. Silva, and Joseph C. Hardin
Geosci. Model Dev., 15, 6677–6694,,, 2022
Short summary
A machine learning methodology for the generation of a parameterization of the hydroxyl radical
Daniel C. Anderson, Melanie B. Follette-Cook, Sarah A. Strode, Julie M. Nicely, Junhua Liu, Peter D. Ivatt, and Bryan N. Duncan
Geosci. Model Dev., 15, 6341–6358,,, 2022
Short summary
Large-eddy simulations with ClimateMachine v0.2.0: a new open-source code for atmospheric simulations on GPUs and CPUs
Akshay Sridhar, Yassine Tissaoui, Simone Marras, Zhaoyi Shen, Charles Kawczynski, Simon Byrne, Kiran Pamnany, Maciej Waruszewski, Thomas H. Gibson, Jeremy E. Kozdon, Valentin Churavy, Lucas C. Wilcox, Francis X. Giraldo, and Tapio Schneider
Geosci. Model Dev., 15, 6259–6284,,, 2022
Short summary
Hybrid ensemble-variational data assimilation in ABC-DA within a tropical framework
Joshua Chun Kwang Lee, Javier Amezcua, and Ross Noel Bannister
Geosci. Model Dev., 15, 6197–6219,,, 2022
Short summary
OpenIFS/AC: atmospheric chemistry and aerosol in OpenIFS 43r3
Vincent Huijnen, Philippe Le Sager, Marcus O. Köhler, Glenn Carver, Samuel Rémy, Johannes Flemming, Simon Chabrillat, Quentin Errera, and Twan van Noije
Geosci. Model Dev., 15, 6221–6241,,, 2022
Short summary

Cited articles

Bannon, P. R.: On the anelastic approximation for a compressible atmosphere, J. Atmos. Sci., 53, 3618–3628, 1996.
Beare, R. J., Macvean, M. K., Holtslag, A. A., Cuxart, J., Esau, I., Golaz, J. C., Jimenez, M. A., Khairoutdinov, M., Kosovic, B., Lewellen, D., and Lund, T. S.: An intercomparison of large-eddy simulations of the stable boundary layer, Bound.-Lay. Meteorol., 118, 247–272, 2006.
Betts, A. K.: Non-precipitating cumulus convection and its parameterization, Q. J. Roy. Meteor. Soc., 99, 178–196, 1973.
Boing, S. J.: The interaction of deep convective clouds and their environment, TU Delft, Delft University of Technology, the Netherlands, 2014.
Bou-Zeid, E., Meneveau, C., and Parlange, M.: A scale-dependent Lagrangian dynamic model for large eddy simulation of complex turbulent flows, Phys. Fluids, 17, 025105,, 2005.
Short summary
MicroHH ( is a new and open-source computational fluid dynamics code for the simulation of turbulent flows in the atmosphere. It is made to simulate atmospheric flows up to the finest detail levels at very high resolution. It has been designed from scratch in C++ in order to use a modern design that allows the code to run on more than 10 000 cores, as well as on a graphical processing unit.