Articles | Volume 10, issue 8
https://doi.org/10.5194/gmd-10-3145-2017
https://doi.org/10.5194/gmd-10-3145-2017
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, Bart J. H. van Stratum, Thijs Heus, Jeremy A. Gibbs, Evgeni Fedorovich, and Juan Pedro Mellado

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
Atmos. Meas. Tech., 16, 3767–3785, https://doi.org/10.5194/amt-16-3767-2023,https://doi.org/10.5194/amt-16-3767-2023, 2023
Short summary
Ten years of 1 Hz solar irradiance observations at Cabauw, the Netherlands, with cloud observations, variability classifications, and statistics
Wouter B. Mol, Wouter H. Knap, and Chiel C. van Heerwaarden
Earth Syst. Sci. Data, 15, 2139–2151, https://doi.org/10.5194/essd-15-2139-2023,https://doi.org/10.5194/essd-15-2139-2023, 2023
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, 16, 4319–4341, https://doi.org/10.5194/tc-16-4319-2022,https://doi.org/10.5194/tc-16-4319-2022, 2022
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, https://doi.org/10.5194/amt-15-3611-2022,https://doi.org/10.5194/amt-15-3611-2022, 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, https://doi.org/10.5194/acp-22-6489-2022,https://doi.org/10.5194/acp-22-6489-2022, 2022
Short summary

Related subject area

Atmospheric sciences
MEXPLORER 1.0.0 – a mechanism explorer for analysis and visualization of chemical reaction pathways based on graph theory
Rolf Sander
Geosci. Model Dev., 17, 2419–2425, https://doi.org/10.5194/gmd-17-2419-2024,https://doi.org/10.5194/gmd-17-2419-2024, 2024
Short summary
Advances and prospects of deep learning for medium-range extreme weather forecasting
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 2347–2358, https://doi.org/10.5194/gmd-17-2347-2024,https://doi.org/10.5194/gmd-17-2347-2024, 2024
Short summary
An overview of the Western United States Dynamically Downscaled Dataset (WUS-D3)
Stefan Rahimi, Lei Huang, Jesse Norris, Alex Hall, Naomi Goldenson, Will Krantz, Benjamin Bass, Chad Thackeray, Henry Lin, Di Chen, Eli Dennis, Ethan Collins, Zachary J. Lebo, Emily Slinskey, Sara Graves, Surabhi Biyani, Bowen Wang, Stephen Cropper, and the UCLA Center for Climate Science Team
Geosci. Model Dev., 17, 2265–2286, https://doi.org/10.5194/gmd-17-2265-2024,https://doi.org/10.5194/gmd-17-2265-2024, 2024
Short summary
cloudbandPy 1.0: an automated algorithm for the detection of tropical–extratropical cloud bands
Romain Pilon and Daniela I. V. Domeisen
Geosci. Model Dev., 17, 2247–2264, https://doi.org/10.5194/gmd-17-2247-2024,https://doi.org/10.5194/gmd-17-2247-2024, 2024
Short summary
PyRTlib: an educational Python-based library for non-scattering atmospheric microwave radiative transfer computations
Salvatore Larosa, Domenico Cimini, Donatello Gallucci, Saverio Teodosio Nilo, and Filomena Romano
Geosci. Model Dev., 17, 2053–2076, https://doi.org/10.5194/gmd-17-2053-2024,https://doi.org/10.5194/gmd-17-2053-2024, 2024
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, https://doi.org/10.1063/1.1839152, 2005.
Download
Short summary
MicroHH (www.microhh.org) 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.