Articles | Volume 14, issue 6
https://doi.org/10.5194/gmd-14-3185-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-3185-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
03 Jun 2021
Development and technical paper |
| 03 Jun 2021
| 03 Jun 2021
A nested multi-scale system implemented in the large-eddy simulation model PALM model system 6.0
Antti Hellsten
CORRESPONDING AUTHOR
Finnish Meteorological Institute, P.O. Box 503, 00101, Helsinki, Finland
Klaus Ketelsen
Software Consultant, Beethovenstr. 29A, 12247 Berlin, Germany
Matthias Sühring
Leibniz University Hannover, Institute of Meteorology and Climatology, Herrenhäuser Strasse 2, 30419 Hanover, Germany
Finnish Meteorological Institute, P.O. Box 503, 00101, Helsinki, Finland
Björn Maronga
Leibniz University Hannover, Institute of Meteorology and Climatology, Herrenhäuser Strasse 2, 30419 Hanover, Germany
University of Bergen, Geophysical Institute, Postboks 7803, 5020 Bergen, Norway
Christoph Knigge
Deutscher Wetterdienst, Frankfurter Straße 135, 63067 Offenbach, Germany
Fotios Barmpas
Aristotle University Thessaloniki, P.O. Box 483, 54124, Thessaloniki, Greece
Georgios Tsegas
Aristotle University Thessaloniki, P.O. Box 483, 54124, Thessaloniki, Greece
Nicolas Moussiopoulos
Aristotle University Thessaloniki, P.O. Box 483, 54124, Thessaloniki, Greece
Siegfried Raasch
Leibniz University Hannover, Institute of Meteorology and Climatology, Herrenhäuser Strasse 2, 30419 Hanover, Germany
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Sebastian Schubert, Julian Anders, Tobias Gronemeier, Björn Maronga, and Mohamed Salim
EGUsphere, https://doi.org/10.5194/egusphere-2026-355, https://doi.org/10.5194/egusphere-2026-355, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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We present palm_csd version 25.10, the current default preprocessing tool for generating the static input data for the building-resolving large-eddy simulation model PALM. This paper focuses on the processing of buildings, vegetation, pavement, water bodies, terrain height and land cover. We demonstrate the application of palm_csd using publicly available geodata for the city of Berlin (Germany). Common data inconsistencies and sources of uncertainty in urban geodata are discussed.
Sasu Karttunen, Matthias Sühring, Ewan O'Connor, and Leena Järvi
Geosci. Model Dev., 18, 5725–5757, https://doi.org/10.5194/gmd-18-5725-2025, https://doi.org/10.5194/gmd-18-5725-2025, 2025
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This paper presents PALM-SLUrb, a single-layer urban canopy model for the PALM model system, designed to simulate urban–atmosphere interactions without resolving flow around individual buildings. The model is described in detail and evaluated against grid-resolved urban canopy simulations, demonstrating its ability to model urban surfaces accurately. By bridging the gap between computational efficiency and physical detail, PALM-SLUrb broadens PALM's potential for urban climate research.
Jaroslav Resler, Petra Bauerová, Michal Belda, Martin Bureš, Kryštof Eben, Vladimír Fuka, Jan Geletič, Radek Jareš, Jan Karel, Josef Keder, Pavel Krč, William Patiño, Jelena Radović, Hynek Řezníček, Matthias Sühring, Adriana Šindelářová, and Ondřej Vlček
Geosci. Model Dev., 17, 7513–7537, https://doi.org/10.5194/gmd-17-7513-2024, https://doi.org/10.5194/gmd-17-7513-2024, 2024
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Detailed modeling of urban air quality in stable conditions is a challenge. We show the unprecedented sensitivity of a large eddy simulation (LES) model to meteorological boundary conditions and model parameters in an urban environment under stable conditions. We demonstrate the crucial role of boundary conditions for the comparability of results with observations. The study reveals a strong sensitivity of the results to model parameters and model numerical instabilities during such conditions.
Gina C. Jozef, Robert Klingel, John J. Cassano, Björn Maronga, Gijs de Boer, Sandro Dahlke, and Christopher J. Cox
Earth Syst. Sci. Data, 15, 4983–4995, https://doi.org/10.5194/essd-15-4983-2023, https://doi.org/10.5194/essd-15-4983-2023, 2023
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Observations from the MOSAiC expedition relating to lower-atmospheric temperature, wind, stability, moisture, and surface radiation budget from radiosondes, a meteorological tower, radiation station, and ceilometer were compiled to create a dataset which describes the thermodynamic and kinematic state of the central Arctic lower atmosphere between October 2019 and September 2020. This paper describes the methods used to develop this lower-atmospheric properties dataset.
Sreenath Paleri, Luise Wanner, Matthias Sühring, Ankur Desai, and Matthias Mauder
EGUsphere, https://doi.org/10.5194/egusphere-2023-1721, https://doi.org/10.5194/egusphere-2023-1721, 2023
Preprint archived
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We present a description and evaluation of numerical simulations of field experiment days during the CHEESEHEAD19 field campaign, conducted over a heterogeneous forested domain in Northern Wisconsin, USA. Diurnal simulations, informed and constrained by field measurements for two days during the summer and autumn were performed. The model could simulate near surface time series and profiles of atmospheric state variables and fluxes that matched relatively well with observations.
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
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Lidars can measure the wind profile in the lower part of the atmosphere, provided that the wind field is horizontally uniform and does not change during the time of the measurement. These requirements are mostly not fulfilled in reality, and the lidar wind measurement will thus hold a certain error. We investigate different strategies for lidar wind profiling using a lidar simulator implemented in a numerical simulation of the wind field. Our findings can help to improve wind measurements.
Ranjeet S. Sokhi, Nicolas Moussiopoulos, Alexander Baklanov, John Bartzis, Isabelle Coll, Sandro Finardi, Rainer Friedrich, Camilla Geels, Tiia Grönholm, Tomas Halenka, Matthias Ketzel, Androniki Maragkidou, Volker Matthias, Jana Moldanova, Leonidas Ntziachristos, Klaus Schäfer, Peter Suppan, George Tsegas, Greg Carmichael, Vicente Franco, Steve Hanna, Jukka-Pekka Jalkanen, Guus J. M. Velders, and Jaakko Kukkonen
Atmos. Chem. Phys., 22, 4615–4703, https://doi.org/10.5194/acp-22-4615-2022, https://doi.org/10.5194/acp-22-4615-2022, 2022
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This review of air quality research focuses on developments over the past decade. The article considers current and future challenges that are important from air quality research and policy perspectives and highlights emerging prominent gaps of knowledge. The review also examines how air pollution management needs to adapt to new challenges and makes recommendations to guide the direction for future air quality research within the wider community and to provide support for policy.
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
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In the future there will be very large wind farm clusters in the German Bight. This study investigates how the wind field is affected by these very large wind farms and how much energy can be extracted by the wind turbines. Very large wind farms do not only reduce the wind speed but can also cause a change in wind direction or temperature. The extractable energy per wind turbine is much smaller for large wind farms than for small wind farms due to the reduced wind speed inside the wind farms.
Mohamed H. Salim, Sebastian Schubert, Jaroslav Resler, Pavel Krč, Björn Maronga, Farah Kanani-Sühring, Matthias Sühring, and Christoph Schneider
Geosci. Model Dev., 15, 145–171, https://doi.org/10.5194/gmd-15-145-2022, https://doi.org/10.5194/gmd-15-145-2022, 2022
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Radiative transfer processes are the main energy transport mechanism in urban areas which influence the surface energy budget and drive local convection. We show here the importance of each process to help modellers decide on how much detail they should include in their models to parameterize radiative transfer in urban areas. We showed how the flow field may change in response to these processes and the essential processes needed to assure acceptable quality of the numerical simulations.
Ian Boutle, Wayne Angevine, Jian-Wen Bao, Thierry Bergot, Ritthik Bhattacharya, Andreas Bott, Leo Ducongé, Richard Forbes, Tobias Goecke, Evelyn Grell, Adrian Hill, Adele L. Igel, Innocent Kudzotsa, Christine Lac, Bjorn Maronga, Sami Romakkaniemi, Juerg Schmidli, Johannes Schwenkel, Gert-Jan Steeneveld, and Benoît Vié
Atmos. Chem. Phys., 22, 319–333, https://doi.org/10.5194/acp-22-319-2022, https://doi.org/10.5194/acp-22-319-2022, 2022
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Fog forecasting is one of the biggest problems for numerical weather prediction. By comparing many models used for fog forecasting with others used for fog research, we hoped to help guide forecast improvements. We show some key processes that, if improved, will help improve fog forecasting, such as how water is deposited on the ground. We also showed that research models were not themselves a suitable baseline for comparison, and we discuss what future observations are required to improve them.
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
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The key points are the following. (i) Integrative observing system design can multiply the information gain of surface–atmosphere field measurements. (ii) Catalyzing numerical simulations and first-principles machine learning open up observing system simulation experiments to novel applications. (iii) Use cases include natural climate solutions, emission inventory validation, urban air quality, and industry leak detection.
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
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In this paper, we provide a technical description of a newly developed interface for coupling the PALM model system 6.0 to the weather prediction model COSMO. The interface allows users of PALM to simulate the detailed atmospheric flow for relatively small regions of tens of kilometres under specific weather conditions, for instance, periods around observation campaigns or extreme weather situations. We demonstrate the interface using a benchmark simulation.
Katrin Frieda Gehrke, Matthias Sühring, and Björn Maronga
Geosci. Model Dev., 14, 5307–5329, https://doi.org/10.5194/gmd-14-5307-2021, https://doi.org/10.5194/gmd-14-5307-2021, 2021
Jaroslav Resler, Kryštof Eben, Jan Geletič, Pavel Krč, Martin Rosecký, Matthias Sühring, Michal Belda, Vladimír Fuka, Tomáš Halenka, Peter Huszár, Jan Karlický, Nina Benešová, Jana Ďoubalová, Kateřina Honzáková, Josef Keder, Šárka Nápravníková, and Ondřej Vlček
Geosci. Model Dev., 14, 4797–4842, https://doi.org/10.5194/gmd-14-4797-2021, https://doi.org/10.5194/gmd-14-4797-2021, 2021
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We describe validation of the PALM model v6.0 against measurements collected during two observational campaigns in Dejvice, Prague. The study focuses on the evaluation of the newly developed or improved radiative and energy balance modules in PALM related to urban modelling. In addition to the energy-related quantities, it also evaluates air flow and air quality under street canyon conditions.
Michal Belda, Jaroslav Resler, Jan Geletič, Pavel Krč, Björn Maronga, Matthias Sühring, Mona Kurppa, Farah Kanani-Sühring, Vladimír Fuka, Kryštof Eben, Nina Benešová, and Mikko Auvinen
Geosci. Model Dev., 14, 4443–4464, https://doi.org/10.5194/gmd-14-4443-2021, https://doi.org/10.5194/gmd-14-4443-2021, 2021
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The analysis summarizes how sensitive the modelling of urban environment is to changes in physical parameters describing the city (e.g. reflectivity of surfaces) and to several heat island mitigation scenarios in a city quarter in Prague, Czech Republic. We used the large-eddy simulation modelling system PALM 6.0. Surface parameters connected to radiation show the highest sensitivity in this configuration. For heat island mitigation, urban vegetation is shown to be the most effective measure.
Jens Pfafferott, Sascha Rißmann, Matthias Sühring, Farah Kanani-Sühring, and Björn Maronga
Geosci. Model Dev., 14, 3511–3519, https://doi.org/10.5194/gmd-14-3511-2021, https://doi.org/10.5194/gmd-14-3511-2021, 2021
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The building model is integrated via an urban surface model into the urban climate model.
There is a strong interaction between the built environment and the urban climate.
According to the building energy concept, the energy demand results in a waste heat; this is directly transferred to the urban environment.
The impact of buildings on the urban climate is defined by different physical building parameters with different technical facilities for ventilation, heating and cooling.
Tobias Gronemeier, Kerstin Surm, Frank Harms, Bernd Leitl, Björn Maronga, and Siegfried Raasch
Geosci. Model Dev., 14, 3317–3333, https://doi.org/10.5194/gmd-14-3317-2021, https://doi.org/10.5194/gmd-14-3317-2021, 2021
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We demonstrate the capability of the PALM model system version 6.0 to simulate urban boundary layers. The studied situation includes a real-world building setup of the HafenCity area in Hamburg, Germany. We evaluate the simulation results against wind-tunnel measurements utilizing PALM's virtual measurement module. The comparison reveals an overall high agreement between simulation results and wind-tunnel measurements including mean wind speed and direction as well as turbulence statistics.
Pavel Krč, Jaroslav Resler, Matthias Sühring, Sebastian Schubert, Mohamed H. Salim, and Vladimír Fuka
Geosci. Model Dev., 14, 3095–3120, https://doi.org/10.5194/gmd-14-3095-2021, https://doi.org/10.5194/gmd-14-3095-2021, 2021
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The adverse effects of an urban environment, e.g. heat stress and air pollution, pose a risk to health and well-being. Precise modelling of the urban climate is crucial to mitigate these effects. Conventional atmospheric models are inadequate for modelling the complex structures of the urban environment; in particular, they lack a 3-D model of radiation and its interaction with surfaces and the plant canopy. The new RTM simulates these processes within the PALM-4U urban climate model.
Cited articles
Ahmad, N. H., Inagaki, A., Kanda, M., Onodera, N., and Aoki, T.: Large-eddy simulation of the gust index in an urban area using the lattice Boltzmann method, Bound.-Lay. Meteorol., 163, 447–467, 2017. a
Aidun, C. K. and Clausen, J. R.: Lattice-Boltzmann method for complex flows, Annu. Rev. Fluid Mech., 42, 439–472, 2010. a
Auvinen, M., Boi, S., Hellsten, A., Tanhuanpää, T., and Järvi, L.: Study of Realistic Urban Boundary Layer Turbulence with High-Resolution Large-Eddy Simulation, Atmosphere, 11, 201, https://doi.org/10.3390/atmos11020201, 2020a. a, b, c
Auvinen, M., Karttunen, S., and Kurppa, M.: P4UL: Pre- and Post-Processing Python Library for Urban LES Simulations, Zenodo, https://doi.org/10.5281/zenodo.4005687, 2020b. a
Bou-Zeid, E., Overney, J., Rogers, B. D., and Parlange, M. B.: The Effects of Building Representation and Clustering in Large-Eddy Simulations of Flows in Urban Canopies, Bound.-Lay. Meteorol., 132, 415–436, https://doi.org/10.1007/s10546-009-9410-6, 2009. a
Britter, R. E. and Hanna, S. R.: Flow and Dispersion in Urban Areas, Annu. Rev. Fluid Mech., 35, 469–496, https://doi.org/10.1146/annurev.fluid.35.101101.161147, 2003. a, b
Buccolieri, R. and Hang, J.: Recent Advances in Urban Ventilation Assessment and Flow Modelling, Atmosphere, 10, 144, https://doi.org/10.3390/atmos10030144, 2019. a
Chung, D. and McKeon, B. J.: Large-eddy simulation of large-scale structures in long channel flow, J. Fluid Mech., 661, 341–364, https://doi.org/10.1017/S0022112010002995, 2010. a
Daniels, M., Lundquist, K., Mirocha, J., Wiersema, D., and Chow, F.: A new vertical grid nesting capability in the Weather Research and Forecasting WRF Model, Mon. Weather Rev., 144, 3725–3747, 2016. a
de Roode, S. R., Duynkerke, P. G., and Jonker, H. J.: Large-eddy simulation: How large is large enough?, J. Atmos. Sci, 61, 403–421, 2004. a
Deardorff, J.: Stratoculumus-capped mixed layers derived from a three-dimensional model, Bound.-Lay. Meteorol., 18, 495–527, 1980. a
Diebold, M., Higgins, C., Fang, J., Bechmann, A., and Parlange, M. B.: Flow over Hills: A Large-Eddy Simulation of the Bolund Case, Bound.-Lay. Meteorol., 148, 177–194, https://doi.org/10.1007/s10546-013-9807-0, 2013. a
Fishpool, G. M., Lardeau, S., and Leschziner, M. A.: Persistent Non-Homogeneous Features in Periodic Channel-Flow Simulations, Flow Turbul. Combust., 83, 323–342, https://doi.org/10.1007/s10494-009-9209-z, 2009. a
Gehrke, K. F., Sühring, M., and Maronga, B.: Modeling of land-surface interactions in the PALM model system 6.0: Land surface model description, first evaluation, and sensitivity to model parameters, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2020-197, in review, 2020. a
Giometto, M., Christen, A., Meneveau, C., Fang, J., Krafczyk, M., and Parlange, M.: Spatial Characteristics of Roughness Sublayer Mean Flow and Turbulence Over a Realistic Urban Surface, Bound.-Lay. Meteorol., 160, 425–452, 2016. a
Gropp, W., Lusk, E., and Skjellum, A.: Using MPI: Portable parallel programming with the Message Passing Interface, 2nd edition, MIT Press, Cambridge, MA, 1999. a
Hackbusch, W.: Multigrid methods and applications, Springer, Berlin, Heidelberg, New York, 378 pp., 1985. a
Heinze, R., Dipankar, A., Henken, C. C., Moseley, C., Sourdeval, O., Trömel, S., Xie, X., Adamidis, P., Ament, F., Baars, H., Barthlott, C., Behrendt, A., Blahak, U., Bley, S., Brdar, S., Brueck, M., Crewell, S., Deneke, H., Di Girolamo, P., Evaristo, R., Fischer, J., Frank, C., Friederichs, P., Göcke, T., Gorges, K., Hande, L., Hanke, M., Hansen, A., Hege, H.-C., Hoose, C., Jahns, T., Kalthoff, N., Klocke, D., Kneifel, S., Knippertz, P., Kuhn, A., van Laar, T., Macke, A., Maurer, V., Mayer, B., Meyer, C. I., Muppa, S. K., Neggers, R. A. J., Orlandi, E., Pantillon, F., Pospichal, B., Röber, N., Scheck, L., Seifert, A., Seifert, P., Senf, F., Siligam, P., Simmer, C., Steinke, S., Stevens, B., Wapler, K., Weniger, M., Wulfmeyer, V., Zängl, G., Zhang, D., and Quaas, J.: Large-eddy simulations over Germany using ICON: a comprehensive evaluation, Q. J. Roy. Meteor. Soc., 143, 69–100, https://doi.org/10.1002/qj.2947, 2017. a
Hellsten, A. and Zilitinkevich, S.: Role of convective structures and background turbulence in the dry convective boundary layer, Bound.-Lay. Meteorol., 149, 323–353, 2013. a
Hellsten, A., Ketelsen, K., Sühring, M., Auvinen, M., Maronga, B., Knigge, C., Barmpas, F., Tsegas, G., Moussiopoulos, N., and Raasch, S.: Dataset: A Nested Multi-Scale System Implemented in the Large-Eddy Simulation Model PALM model system 6.0, PALM input files and source code, Leibniz Universität Hannover, https://doi.org/10.25835/0090593, 2020. a
Heus, T., van Heerwaarden, C. C., Jonker, H. J. J., Pier Siebesma, A., Axelsen, S., van den Dries, K., Geoffroy, O., Moene, A. F., Pino, D., de Roode, S. R., and Vilà-Guerau de Arellano, J.: Formulation of the Dutch Atmospheric Large-Eddy Simulation (DALES) and overview of its applications, Geosci. Model Dev., 3, 415–444, https://doi.org/10.5194/gmd-3-415-2010, 2010. a
Hutchins, N. and Marusic, I.: Evidence of very long meandering features in the logarithmic region of turbulent boundary layers, J. Fluid Mech., 579, 1–28, 2007. a
Hutchins, N., Chauhan, K., Marusic, I., Monty, J., and Klewicki, J.: Towards reconciling the large-scale structure of turbulent boundary layers in the atmosphere and laboratory, Bound.-Lay. Meteorol., 145, 273–306, 2012. a
Ishihara, T., Hibi, K., and Oikawa, S.: A wind tunnel study of turbulent flow over a three-dimensional steep hill, J. Wind Eng. Ind. Aerod., 83, 95–107, https://doi.org/10.1016/S0167-6105(99)00064-1, 1999. a, b
Karttunen, S., Kurppa, M., Auvinen, M., Hellsten, A., and Järvi, L.: Large-eddy simulation of the optimal street-tree layout for pedestrian-level aerosol particle concentrations – A case study from a city-boulevard, Atmos. Environ X, 6, 100 073, https://doi.org/10.1016/j.aeaoa.2020.100073, 2020. a
Kataoka, H. and Mizuno, M.: Numerical flow computation around aeroelastic 3D square cylinder using inflow turbulence, Wind and Structures, 5, 379–392, 2002. a
Khan, B., Banzhaf, S., Chan, E. C., Forkel, R., Kanani-Sühring, F., Ketelsen, K., Kurppa, M., Maronga, B., Mauder, M., Raasch, S., Russo, E., Schaap, M., and Sühring, M.: Development of an atmospheric chemistry model coupled to the PALM model system 6.0: implementation and first applications, Geosci. Model Dev., 14, 1171–1193, https://doi.org/10.5194/gmd-14-1171-2021, 2021. a
Krč, P., Resler, J., Sühring, M., Schubert, S., Salim, M. H., and Fuka, V.: Radiative Transfer Model 3.0 integrated into the PALM model system 6.0, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2020-168, in review, 2020. a
Kurppa, M., Hellsten, A., Roldin, P., Kokkola, H., Tonttila, J., Auvinen, M., Kent, C., Kumar, P., Maronga, B., and Järvi, L.: Implementation of the sectional aerosol module SALSA2.0 into the PALM model system 6.0: model development and first evaluation, Geosci. Model Dev., 12, 1403–1422, https://doi.org/10.5194/gmd-12-1403-2019, 2019. a, b
Kurppa, M., Roldin, P., Strömberg, J., Balling, A., Karttunen, S., Kuuluvainen, H., Niemi, J. V., Pirjola, L., Rönkkö, T., Timonen, H., Hellsten, A., and Järvi, L.: Sensitivity of spatial aerosol particle distributions to the boundary conditions in the PALM model system 6.0, Geosci. Model Dev., 13, 5663–5685, https://doi.org/10.5194/gmd-13-5663-2020, 2020. a
Maronga, B. and Raasch, S.: Large-Eddy Simulations of Surface Heterogeneity Effects on the Convective Boundary Layer During the LITFASS-2003 Experiment, Bound.-Lay. Meteorol., 146, 17–44, https://doi.org/10.1007/s10546-012-9748-z, 2013. a
Maronga, B., Gryschka, M., Heinze, R., Hoffmann, F., Kanani-Sühring, F., Keck, M., Ketelsen, K., Letzel, M. O., Sühring, M., and Raasch, S.: The Parallelized Large-Eddy Simulation Model (PALM) version 4.0 for atmospheric and oceanic flows: model formulation, recent developments, and future perspectives, Geosci. Model Dev., 8, 2515–2551, https://doi.org/10.5194/gmd-8-2515-2015, 2015. a, b, c, d, e
Maronga, B., Gross, G., Raasch, S., Banzhaf, S., Forkel, R., Heldens, W., Kanani-Sühring, F., Matzarakis, A., Mauder, M., Pavlik, D., Pfafferott, J., Schubert, S., Seckmeyer, G., Sieker, H., and Winderlich, K.: Development of a new urban climate model based on the model PALM – Project overview, planned work, and first achievements, Meteorol. Z., 28, 105–119, https://doi.org/10.1127/metz/2019/0909, 2019. a
Maronga, B., Banzhaf, S., Burmeister, C., Esch, T., Forkel, R., Fröhlich, D., Fuka, V., Gehrke, K. F., Geletič, J., Giersch, S., Gronemeier, T., Groß, G., Heldens, W., Hellsten, A., Hoffmann, F., Inagaki, A., Kadasch, E., Kanani-Sühring, F., Ketelsen, K., Khan, B. A., Knigge, C., Knoop, H., Krč, P., Kurppa, M., Maamari, H., Matzarakis, A., Mauder, M., Pallasch, M., Pavlik, D., Pfafferott, J., Resler, J., Rissmann, S., Russo, E., Salim, M., Schrempf, M., Schwenkel, J., Seckmeyer, G., Schubert, S., Sühring, M., von Tils, R., Vollmer, L., Ward, S., Witha, B., Wurps, H., Zeidler, J., and Raasch, S.: Overview of the PALM model system 6.0, Geosci. Model Dev., 13, 1335–1372, https://doi.org/10.5194/gmd-13-1335-2020, 2020. a, b, c, d
Mellor, G. L. and Yamada, T.: A Hierarchy of Turbulence Closure Models for Planetary Boundary Layers, J. Atmos. Sci, 31, 1791–1806, https://doi.org/10.1175/1520-0469(1974)031<1791:AHOTCM>2.0.CO;2, 1974. a
Mellor, G. L. and Yamada, T.: Development of a turbulence closure model for geophysical fluid problems, Rev. Geophys., 20, 851–875, https://doi.org/10.1029/RG020i004p00851, 1982. a
Miller, M. J. and Thorpe, A. J.: Radiation conditions for the lateral boundaries of limited-area numerical models, Q. J. Roy. Meteor. Soc., 107, 615–628, https://doi.org/10.1002/qj.49710745310, 1981. a
Mirocha, J., Kirkil, G., Bou-Zeid, E., Chow, F. K., and Kosović, B.: Transition and equilibration of neutral atmospheric boundary layer flow in one-way nested large-eddy simulations using the Weather Research and Forecasting model, Mon. Weather Rev., 141, 918–940, https://doi.org/10.1175/MWR-D-11-00263.1, 2013. a
Muñoz-Esparza, D., Kosović, B., García-Sánchez, C., and van Beeck, J.: Nesting turbulence in an offshore convective boundary layer using large-eddy simulations, Bound.-Lay. Meteorol., 151, 453–478, 2014. a
Muñoz-Esparza, D., Lundquist, J. K., Sauer, J. A., Kosović, B., and Linn, R. R.: Coupled mesoscale-LES modeling of a diurnal cycle during the CWEX-13 field campaign: From weather to boundary-layer eddies, J. Adv. Model. Earth Syst., 9, 1572–1594, https://doi.org/10.1002/2017MS000960, 2017. a, b
Munters, W., Meneveau, C., and Meyers, J.: Shifted periodic boundary conditions for simulations of wall-bounded turbulent flows, Phys. Fluids, 28, 025 112, https://doi.org/10.1063/1.4941912, 2016. a
Nakayama, H., Takemi, T., and Nagai, H.: Development of LOcal-scale High-resolution atmospheric DIspersion Model using Large-Eddy Simulation. Part 5: detailed simulation of turbulent flows and plume dispersion in an actual urban area under real meteorological conditions., J. Nucl. Sci. Technol., 53, 887–908, https://doi.org/10.1080/00223131.2015.1078262, 2016. a
Nunalee, C. G., Kosović, B., and Bieringer, P. E.: Development of LOcal-scale High-resolution atmospheric DIspersion Model using Large-Eddy Simulation. Part 5: detailed simulation of turbulent flows and plume dispersion in an actual urban area under real meteorological conditions., Atmos. Environ., 99, 571–581, https://doi.org/10.1016/j.atmosenv.2014.09.070, 2014. a
Patrinos, A. N. A. and Kistler, A. L.: A numerical study of the Chicago lake breeze, Bound.-Lay. Meteorol., 12, 93–123, 1977. a
Resler, J., Krč, P., Belda, M., Juruš, P., Benešová, N., Lopata, J., Vlček, O., Damašková, D., Eben, K., Derbek, P., Maronga, B., and Kanani-Sühring, F.: PALM-USM v1.0: A new urban surface model integrated into the PALM large-eddy simulation model, Geosci. Model Dev., 10, 3635–3659, https://doi.org/10.5194/gmd-10-3635-2017, 2017. a
Saiki, E. M., Moeng, C.-H., and Sullivan, P. P.: Large-eddy simulation of the stably stratified planetary boundary layer, Bound.-Lay. Meteorol., 95, 1–30, 2000. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda, M. G., Huang, X.-Y., Wang, W., and Powers, J. G.: G.: A description of the Advanced Research WRF version 3, Tech. Rep., NCAR/TN-475+ STR, NCAR, 2008. a
Sühring, M., Maronga, B., Herbort, F., and Raasch, S.: On the Effect of Surface Heat-Flux Heterogeneities on the Mixed-Layer-Top Entrainment, Bound.-Lay. Meteorol., 151, 531–556, https://doi.org/10.1007/s10546-014-9913-7, 2014. a
Sullivan, P. and Patton, E.: The effect of mesh resolution on convective boundary layer statistics and structures generated by large-eddy simulation, J. Atmos. Sci, 68, 2395–2415, 2011. a
Tominaga, Y. and Stathopoulos, T.: CFD simulation of near-field pollutant dispersion in the urban environment: A review of current modeling techniques, Atmos. Environ., 79, 716–730, https://doi.org/10.1016/j.atmosenv.2013.07.028, 2013. a
Tseng, Y.-H., Meneveau, C., and Parlange, M. B.: Modeling flow around bluff bodies and predicting urban dispersion using large eddy simulation, Environ. Sci. Technol., 40, 2653–2662, 2006. a
Vonlanthen, M., Allegrini, J., and Carmeliet, J.: Assessment of a one-way nesting procedure for obstacle resolved large eddy simulation of the ABL, Comput. Fluids, 140, 136–147, https://doi.org/10.1016/j.compfluid.2016.09.016, 2016. a
Vonlanthen, M., Allegrini, J., and Carmeliet, J.: Multiscale interaction between a cluster of buildings and the abl developing over a real terrain, Urban Climate, 20, 1–19, https://doi.org/10.1016/j.uclim.2017.02.009, 2017. a
Weil, J., Sullivan, P., and Moeng, C.: The use of large-eddy simulations in Lagrangian particle dispersion models, J. Atmos. Sci, 61, 2877–2887, 2004. a
Wiersema, D. J., Lundquist, K. A., and Chow, F. K.: Mesoscale to microscale simulations over complex terrain with the immersed boundary method in the Weather Research and Forecasting Model, Mon. Weather Rev., 148, 577–595, 2020. a
Williamson, J. H.: Low-storage Runge-Kutta schemes, J Comput. Phys., 35, 48–56, 1980. a
Xie, Z. and Castro, I.: LES and RANS for turbulent flows over arrays of wall-mounted obstacles, Flow Turbul. Combust., 76, 291–312, 2006. a
Zängl, G., Reinert, D., Rípodas, P., and Baldauf, M.: The ICON (ICOsahedral Non-hydrostatic) modelling framework of DWD and MPI-M: Description of the non-hydrostatic dynamical core, Q. J. Roy. Meteor. Soc., 141, 563–579, https://doi.org/10.1002/qj.2378, 2015. a
Zhu, P., Albrecht, B. A., Ghate, V. P., and Zhu, Z.: Multiple-scale simulations of stratocumulus clouds, J. Geophys. Res.-Atmos., 115, D23201, https://doi.org/10.1029/2010JD014400, 2010. a
Short summary
Large-eddy simulation (LES) of the urban atmospheric boundary layer involves a large separation of turbulent scales, leading to prohibitive computational costs. An online LES–LES nesting scheme is implemented into the PALM model system 6.0 to overcome this problem. Test results show that the accuracy within the high-resolution nest domains approach the non-nested high-resolution reference results. The nesting can reduce the CPU by time up to 80 % compared to the fine-resolution reference runs.
Large-eddy simulation (LES) of the urban atmospheric boundary layer involves a large separation...
