Articles | Volume 12, issue 6
Geosci. Model Dev., 12, 2523–2538, 2019
https://doi.org/10.5194/gmd-12-2523-2019
Geosci. Model Dev., 12, 2523–2538, 2019
https://doi.org/10.5194/gmd-12-2523-2019
Development and technical paper
28 Jun 2019
Development and technical paper | 28 Jun 2019

Vertically nested LES for high-resolution simulation of the surface layer in PALM (version 5.0)

Sadiq Huq et al.

Related authors

Scan strategies for wind profiling with Doppler lidar – an large-eddy simulation (LES)-based evaluation
Charlotte Rahlves, Frank Beyrich, and Siegfried Raasch
Atmos. Meas. Tech., 15, 2839–2856, https://doi.org/10.5194/amt-15-2839-2022,https://doi.org/10.5194/amt-15-2839-2022, 2022
Short summary
Wake properties and power output of very large wind farms for different meteorological conditions and turbine spacings: a large-eddy simulation case study for the German Bight
Oliver Maas and Siegfried Raasch
Wind Energ. Sci., 7, 715–739, https://doi.org/10.5194/wes-7-715-2022,https://doi.org/10.5194/wes-7-715-2022, 2022
Short summary
Options to correct local turbulent flux measurements for large-scale fluxes using an approach based on large-eddy simulation
Matthias Mauder, Andreas Ibrom, Luise Wanner, Frederik De Roo, Peter Brugger, Ralf Kiese, and Kim Pilegaard
Atmos. Meas. Tech., 14, 7835–7850, https://doi.org/10.5194/amt-14-7835-2021,https://doi.org/10.5194/amt-14-7835-2021, 2021
Short summary
Novel approach to observing system simulation experiments improves information gain of surface–atmosphere field measurements
Stefan Metzger, David Durden, Sreenath Paleri, Matthias Sühring, Brian J. Butterworth, Christopher Florian, Matthias Mauder, David M. Plummer, Luise Wanner, Ke Xu, and Ankur R. Desai
Atmos. Meas. Tech., 14, 6929–6954, https://doi.org/10.5194/amt-14-6929-2021,https://doi.org/10.5194/amt-14-6929-2021, 2021
Short summary
Mesoscale nesting interface of the PALM model system 6.0
Eckhard Kadasch, Matthias Sühring, Tobias Gronemeier, and Siegfried Raasch
Geosci. Model Dev., 14, 5435–5465, https://doi.org/10.5194/gmd-14-5435-2021,https://doi.org/10.5194/gmd-14-5435-2021, 2021
Short summary

Related subject area

Atmospheric sciences
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, https://doi.org/10.5194/gmd-15-6341-2022,https://doi.org/10.5194/gmd-15-6341-2022, 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, https://doi.org/10.5194/gmd-15-6259-2022,https://doi.org/10.5194/gmd-15-6259-2022, 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, https://doi.org/10.5194/gmd-15-6197-2022,https://doi.org/10.5194/gmd-15-6197-2022, 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, https://doi.org/10.5194/gmd-15-6221-2022,https://doi.org/10.5194/gmd-15-6221-2022, 2022
Short summary
Simulations of aerosol pH in China using WRF-Chem (v4.0): sensitivities of aerosol pH and its temporal variations during haze episodes
Xueyin Ruan, Chun Zhao, Rahul A. Zaveri, Pengzhen He, Xinming Wang, Jingyuan Shao, and Lei Geng
Geosci. Model Dev., 15, 6143–6164, https://doi.org/10.5194/gmd-15-6143-2022,https://doi.org/10.5194/gmd-15-6143-2022, 2022
Short summary

Cited articles

Anastopoulos, N., Nikunen, P., and Weinberg, V.: Best Practice Guide – SuperMUC v1.0. PRACE – Partnership for Advanced Computing in Europe 2013, available at: http://www.prace-ri.eu/best-practice-guide-supermuc-html (last access: 24 June 2019), 2013. a
Basu, S. and Lacser, A.: A Cautionary Note on the Use of Monin–Obukhov Similarity Theory in Very High-Resolution Large-Eddy Simulations, Bound.-Lay. Meteorol., 163, 351–355, https://doi.org/10.1007/s10546-016-0225-y, 2017. a
Boersma, B. J., Kooper, M. N., Nieuwstadt, F. T. M., and Wesseling, P.: Local grid refinement in large-eddy simulations, J. Eng. Math., 32, 161–175, https://doi.org/10.1023/A:1004283921077, 1997. a
Clark, T. L. and Farley, R. D.: Severe downslope windstorm calculations in two and three spatial dimensions using anelastic interactive grid nesting: A possible mechanism for gustiness, J. Atmos. Sci., 41, 329–350, https://doi.org/10.1175/1520-0469(1984)041<0329:SDWCIT>2.0.CO;2, 1984. a, b
Clark, T. L. and Hall, W. D.: Multi-domain simulations of the time dependent Navier Stokes equation: Benchmark error analyses of nesting procedures, J. Comput. Phys., 92, 456–481, https://doi.org/10.1016/0021-9991(91)90218-A, 1991. a, b, c
Download
Short summary
To study turbulence in heterogeneous terrain, high-resolution LES is desired. However, the desired resolution is often restricted by computational constraints. We present a two-way interactive vertical grid nesting technique that enables high-resolution LES of the surface layer. By employing a finer grid only close to the surface layer, the total computational memory requirement is reduced. We demonstrate the accuracy and performance of the method for a convective boundary layer simulation.