Articles | Volume 14, issue 9
https://doi.org/10.5194/gmd-14-5435-2021
© 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-14-5435-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Mesoscale nesting interface of the PALM model system 6.0
Eckhard Kadasch
CORRESPONDING AUTHOR
Deutscher Wetterdienst, Offenbach, Germany
Matthias Sühring
Institute of Meteorology and Climatology, Leibniz University Hannover, Hanover, Germany
Tobias Gronemeier
Institute of Meteorology and Climatology, Leibniz University Hannover, Hanover, Germany
Siegfried Raasch
Institute of Meteorology and Climatology, Leibniz University Hannover, Hanover, Germany
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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.
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.
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.
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.
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.
Antti Hellsten, Klaus Ketelsen, Matthias Sühring, Mikko Auvinen, Björn Maronga, Christoph Knigge, Fotios Barmpas, Georgios Tsegas, Nicolas Moussiopoulos, and Siegfried Raasch
Geosci. Model Dev., 14, 3185–3214, https://doi.org/10.5194/gmd-14-3185-2021, https://doi.org/10.5194/gmd-14-3185-2021, 2021
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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.
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.
Basit Khan, Sabine Banzhaf, Edward C. Chan, Renate Forkel, Farah Kanani-Sühring, Klaus Ketelsen, Mona Kurppa, Björn Maronga, Matthias Mauder, Siegfried Raasch, Emmanuele Russo, Martijn Schaap, and Matthias Sühring
Geosci. Model Dev., 14, 1171–1193, https://doi.org/10.5194/gmd-14-1171-2021, https://doi.org/10.5194/gmd-14-1171-2021, 2021
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An atmospheric chemistry model has been implemented in the microscale PALM model system 6.0. This article provides a detailed description of the model, its structure, input requirements, various features and limitations. Several pre-compiled ready-to-use chemical mechanisms are included in the chemistry model code; however, users can also easily implement other mechanisms. A case study is presented to demonstrate the application of the new chemistry model in the urban environment.
Wieke Heldens, Cornelia Burmeister, Farah Kanani-Sühring, Björn Maronga, Dirk Pavlik, Matthias Sühring, Julian Zeidler, and Thomas Esch
Geosci. Model Dev., 13, 5833–5873, https://doi.org/10.5194/gmd-13-5833-2020, https://doi.org/10.5194/gmd-13-5833-2020, 2020
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For realistic microclimate simulations in urban areas with PALM 6.0, detailed description of surface types, buildings and vegetation is required. This paper shows how such input data sets can be derived with the example of three German cities. Various data sources are used, including remote sensing, municipal data collections and open data such as OpenStreetMap. The collection and preparation of input data sets is tedious. Future research aims therefore at semi-automated tools to support users.
Cited articles
André, J. C., De Moor, G., Lacarrère, P., and du Vachat, R.: Modeling the
24-Hour Evolution of the Mean and Turbulent Structures of the Planetary
Boundary Layer, J. Atmos. Sci., 35, 1861–1883,
https://doi.org/10.1175/1520-0469(1978)035<1861:MTHEOT>2.0.CO;2, 1978. a
Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., and
Reinhardt, T.: Operational Convective-Scale Numerical Weather Prediction with
the COSMO Model: Description and Sensitivities, Mon. Weather Rev., 139,
3887–3905, https://doi.org/10.1175/MWR-D-10-05013.1, 2011. a, b, c, d
Baldauf, M., Förstner, J., Klink, S., Reinhardt, T., Schraff, C., Seifert, A., and Stephan, K.: Kurze Beschreibung des Lokal-Modells Kürzestfrist COSMO-DE (LMK) und seiner Datenbanken auf dem Datenserver des DWD, Tech. rep., Deutscher Wetterdienst, available at:
https://www.dwd.de/SharedDocs/downloads/DE/modelldokumentationen/nwv/cosmo_de/cosmo_de_dbbeschr_version_2_3_201406.pdf?__blob=publicationFile&v=5 (last access: 13 August 2021),
version 2.3, 2014. a, b, c
Baldauf, M., Gebhardt, C., Theis, S., Ritter, B., and Schraff, C.: Beschreibung des operationellen Kürzesfristvorhersagemodells COSMO-D2 und
COSMO-D2-EPS und seiner Ausgabe in die Datenbanken des DWD, Tech. rep., Deutscher Wetterdienst, available at:
https://www.dwd.de/SharedDocs/downloads/DE/modelldokumentationen/nwv/cosmo_d2/cosmo_d2_dbbeschr_version_1_0_201805.pdf?__blob=publicationFile&v=3 (last access: 13 August 2021),
version 1.0, 2018. a, b
Ching, J., Rotunno, R., LeMone, M., Martilli, A., Kosovic, B., Jimenez, P. A.,
and Dudhia, J.: Convectively Induced Secondary Circulations in Fine-Grid
Mesoscale Numerical Weather Prediction Models, Mon. Weather Rev., 142,
3284–3302, https://doi.org/10.1175/MWR-D-13-00318.1, 2014. a, b
Clough, S. A., Shephard, M. W., Mlawer, E. J., Delamere, J. S., Iacono, M. J.,
Cady-Pereira, K., Boukabara, S., and Brown, P. D.: Atmospheric radiative
transfer modeling: A summary of the AER codes, Short Communication, J. Quant. Spectrosc. Ra., 91, 233–244, 2005. a
Davies, H. C. and Turner, R. E.: Updating prediction models by dynamical
relaxation: an examination of the technique, Q. J. Roy. Meteor. Soc., 103, 225–245,
https://doi.org/10.1002/qj.49710343602, 1977. a
Emes, M. J., Arjomandi, M., Kelso, R. M., and Ghanadi, F.: Turbulence length
scales in a low-roughness near-neutral atmospheric surface layer, J. Turbul., 20, 545–562, https://doi.org/10.1080/14685248.2019.1677908, 2019. a
Flay, R. and Stevenson, D.: Integral length scales in strong winds below 20 m,
J. Wind Eng. Ind. Aerod., 28, 21–30,
https://doi.org/10.1016/0167-6105(88)90098-0, 1988. 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
Gronemeier, T., Inagaki, A., Gryschka, M., and Kanda, M.: Large-eddy simulation
of an urban canopy using a synthetic turbulence inflow generation method,
Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering),
71, I_43–I_48, https://doi.org/10.2208/jscejhe.71.I_43, 2015. a
Gronemeier, T., Raasch, S., and Ng, E.: Effects of Unstable Stratification on
Ventilation in Hong Kong, Atmosphere, 8, 168, https://doi.org/10.3390/atmos8090168, 2017. a
Gryning, S., Holtslag, A., Irwin, J., and Sivertsen, B.: Applied dispersion
modelling based on meteorological scaling parameters, Atmos. Environ., 21, 79–89, https://doi.org/10.1016/0004-6981(87)90273-3, 1987. a, b
Heinze, R., Moseley, C., Böske, L. N., Muppa, S. K., Maurer, V., Raasch, S., and Stevens, B.: Evaluation of large-eddy simulations forced with mesoscale model output for a multi-week period during a measurement campaign, Atmos. Chem. Phys., 17, 7083–7109, https://doi.org/10.5194/acp-17-7083-2017, 2017. a, b, c, d, e
Hellsten, A., Ketelsen, K., Sühring, M., Auvinen, M., Maronga, B., Knigge, C.,
Barmpas, F., Tsegas, G., Moussiopoulos, N., and Raasch, S.: A nested
multi-scale system implemented in the large-eddy simulation model PALM model
system 6.0, Geosci. Model Dev., 14, 3185–3214, https://doi.org/10.5194/gmd-14-3185-2021, 2021. a, b, c, d
Holtslag, A. A. M. and Boville, B. A.: Local Versus Nonlocal Boundary-Layer
Diffusion in a Global Climate Model, J. Climate, 6, 1825–1842,
https://doi.org/10.1175/1520-0442(1993)006<1825:LVNBLD>2.0.CO;2, 1993. a
Honnert, R., Masson, V., and Couvreux, F.: A Diagnostic for Evaluating the
Representation of Turbulence in Atmospheric Models at the Kilometric Scale,
J. Atmos. Sci., 68, 3112–3131,
https://doi.org/10.1175/JAS-D-11-061.1, 2011. a
Jähn, M., Muñoz-Esparza, D., Chouza, F., Reitebuch, O., Knoth, O., Haarig, M., and Ansmann, A.: Investigations of boundary layer structure, cloud characteristics and vertical mixing of aerosols at Barbados with large eddy simulations, Atmos. Chem. Phys., 16, 651–674, https://doi.org/10.5194/acp-16-651-2016, 2016. a
Jiang, P., Wen, Z., Sha, W., and Chen, G.: Interaction between turbulent flow
and sea breeze front over urban-like coast in large-eddy simulation, J. Geophys. Res.-Atmos., 122, 5298–5315,
https://doi.org/10.1002/2016JD026247, 2017. a
Kadasch, E.: INIFOR [code], available at: https://palm.muk.uni-hannover.de/trac/browser/palm/trunk/ UTIL/inifor, last access: 13 August 2021. a
Kadasch, E. and Sühring, M.: Supplementary material to “Mesoscale nesting interface of the PALM model system 6.0”, Leibniz Universität Hannover [data set], https://doi.org/10.25835/0084787, 2020. a
Kataoka, H. and Mizuno, M.: Numerical flow computation around aeroelastic 3D
square cylinder using inflow turbulence, Wind Struct., 5, 379–392,
https://doi.org/10.12989/WAS.2002.5.2_3_4.379, 2002. a, b
Kim, Y., Castro, I. P., and Xie, Z.-T.: Divergence-free turbulence inflow
conditions for large-eddy simulations with incompressible flow solvers,
Comput. Fluids, 84, 56–68,
https://doi.org/10.1016/j.compfluid.2013.06.001, 2013. a
Klein, M., Sadiki, A., and Janicka, J.: A digital filter based generation of
inflow data for spatially developing direct numerical or large eddy
simulations, J. Comput. Phys., 186, 652–665,
https://doi.org/10.1016/S0021-9991(03)00090-1, 2003. a
Lee, G.-J., Muñoz-Esparza, D., Yi, C., and Choe, H. J.: Application of the
Cell Perturbation Method to Large-Eddy Simulations of a Real Urban Area,
J. Appl. Meteorol. Clim., 58, 1125–1139,
https://doi.org/10.1175/JAMC-D-18-0185.1, 2019. a, b
Letzel, M. O., Helmke, C., Ng, E., An, X., Lai, A., and Raasch, S.: LES case
study on pedestrian level ventilation in two neighbourhoods in Hong Kong,
Meteorol. Z. 21, 575–589, https://doi.org/10.1127/0941-2948/2012/0356,
2012. a
Li, S., Hu, Z., Chan, P., and Hu, G.: A study on the profile of the turbulence
length scale in the near-neutral atmospheric boundary for sea (homogeneous)
and hilly land (inhomogeneous) fetches, J. Wind Eng. Ind. Aerod., 168, 200–210,
https://doi.org/10.1016/j.jweia.2017.06.008, 2017. a
Lund, T. S., Wu, X., and Squires, K. D.: Generation of Turbulent Inflow Data
for Spatially-Developing Boundary Layer Simulations, J. Comput. Phys., 140, 233–258, https://doi.org/10.1006/jcph.1998.5882, 1998. 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
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, e, f, g
Mazzaro, L. J., Koo, E., Muñoz-Esparza, D., Lundquist, J. K., and Linn, R. R.:
Random Force Perturbations: A New Extension of the Cell Perturbation Method
for Turbulence Generation in Multiscale Atmospheric Boundary Layer
Simulations, J. Adv. Model. Earth Sy., 11, 2311–2329,
https://doi.org/10.1029/2019MS001608, 2019. a, b, c
Mirocha, J., Kosović, B., and Kirkil, G.: Resolved Turbulence Characteristics
in Large-Eddy Simulations Nested within Mesoscale Simulations Using the
Weather Research and Forecasting Model, Mon. Weather Rev., 142,
806–831, https://doi.org/10.1175/MWR-D-13-00064.1, 2014. a, b, c, d
Moeng, C.-H. and Rotunno, R.: Vertical-Velocity Skewness in the Buoyancy-Driven
Boundary Layer, J. Atmos. Sci., 47, 1149–1162,
https://doi.org/10.1175/1520-0469(1990)047<1149:VVSITB>2.0.CO;2, 1990. a
Mordant, N., Metz, P., Michel, O., and Pinton, J.-F.: Measurement of Lagrangian
Velocity in Fully Developed Turbulence, Phys. Rev. Lett., 87, 214501,
https://doi.org/10.1103/PhysRevLett.87.214501, 2001. a
Munters, W., Meneveau, C., and Meyers, J.: Shifted periodic boundary conditions
for simulations of wall-bounded turbulent flows, Phys. Fluids, 28,
025112, https://doi.org/10.1063/1.4941912, 2016. a, b
Muñoz-Esparza, D., Kosović, B., Mirocha, J., and van Beeck, J.: Bridging
the Transition from Mesoscale to Microscale Turbulence in Numerical Weather
Prediction Models, Bound.-Lay. Meteorol., 153, 409–440,
https://doi.org/10.1007/s10546-014-9956-9, 2014. a
Muñoz-Esparza, D., Kosović, B., van Beeck, J., and Mirocha, J.: A stochastic
perturbation method to generate inflow turbulence in large-eddy simulation
models: Application to neutrally stratified atmospheric boundary layers,
Phys. Fluids, 27, 035102, https://doi.org/10.1063/1.4913572, 2015. a, b
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 Sy., 9, 1572–1594, https://doi.org/10.1002/2017MS000960, 2017. a, b, c
PALM: The PALM model system web pages [code], available at: http://palm-model.org, last access: 13 August 2021. a
Park, S.-B., Baik, J.-J., and Lee, S.-H.: Impacts of Mesoscale Wind on
Turbulent Flow and Ventilation in a Densely Built-up Urban Area, J. Appl. Meteorol. Clim., 54, 811–824,
https://doi.org/10.1175/JAMC-D-14-0044.1, 2015. a
Reinert, D., Prill, F., Frank, H., Denhard, M., Baldauf, M., Schraff, C., Gebhardt, C., Marsigli, C., and Zängl, G.: DWD Database Reference for the
Global and Regional ICON and ICON-EPS Forecasting System, Tech. rep., Deutscher Wetterdienst, available at:
https://www.dwd.de/DWD/forschung/nwv/fepub/icon_database_main.pdf,
version 2.1.1, last access: 6 June 2020. a, b
Salesky, S. T., Katul, G. G., and Chamecki, M.: Buoyancy effects on the
integral lengthscales and mean velocity profile in atmospheric surface layer
flows, Phys. Fluids, 25, 105101, https://doi.org/10.1063/1.4823747, 2013. a
Schalkwijk, J., Jonker, H. J. J., Siebesma, A. P., and Van Meijgaard, E.: Weather Forecasting Using GPU-Based Large-Eddy Simulations, B. Am. Meteorol. Soc., 96, 715–723,
https://doi.org/10.1175/BAMS-D-14-00114.1, 2015. a, b
Scherer, D., Antretter, F., Bender, S., Cortekar, J., Emeis, S.,
Fehrenbach, U., Gross, G., Halbig, G., Hasse, J., Maronga, B., Raasch, S., and
Scherber, K.: Urban Climate Under Change [UC]2 – A National Research Programme for
Developing a Building-Resolving Atmospheric Model for Entire City Regions,
Meteorol. Z. 28, 95–104, https://doi.org/10.1127/metz/2019/0913, 2019. a
Shin, H. H. and Dudhia, J.: Evaluation of PBL Parameterizations in WRF at
Subkilometer Grid Spacings: Turbulence Statistics in the Dry Convective
Boundary Layer, Mon. Weather Rev., 144, 1161–1177,
https://doi.org/10.1175/MWR-D-15-0208.1, 2016. a, b
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D., Duda, M. G., Huang, X., Wang, W., and Powers, J. G.: A Description of the Advanced
Research WRF Version 3 (No. NCAR/TN-475+STR), Tech. rep., University
Corporation for Atmospheric Research, https://doi.org/10.5065/D68S4MVH, 2008. a, b
Tennekes, H. and Lumley, J.: A first course in turbulence, The MIT Press, Cambridge, Mass.,
1972. a
Troen, I. B. and Mahrt, L.: A simple model of the atmospheric boundary layer;
sensitivity to surface evaporation, Bound.-Lay. Meteorol., 37, 129–148,
https://doi.org/10.1007/BF00122760, 1986. a
Wan, H., Giorgetta, M. A., Zängl, G., Restelli, M., Majewski, D., Bonaventura, L., Fröhlich, K., Reinert, D., Rípodas, P., Kornblueh, L., and Förstner, J.: The ICON-1.2 hydrostatic atmospheric dynamical core on triangular grids Part 1: Formulation and performance of the baseline version, Geosci. Model Dev., 6, 735–763, https://doi.org/10.5194/gmd-6-735-2013, 2013. a
Wicker, L. J. and Skamarock, W. C.: Time-Splitting Methods for Elastic Models
Using Forward Time Schemes, Mon. Weather Rev., 130, 2088–2097,
https://doi.org/10.1175/1520-0493(2002)130<2088:TSMFEM>2.0.CO;2,
2002. a
Williamson, J. H.: Low-storage Runge-Kutta schemes, J. Comput. Phys., 35, 48–56, https://doi.org/10.1016/0021-9991(80)90033-9,
1980. a
Willis, G. E. and Deardorff, J. W.: A laboratory model of diffusion into the
convective planetary boundary layer, Q. J. Roy. Meteor. Soc., 102, 427–445, https://doi.org/10.1002/qj.49710243212, 1976. a
Wu, X.: Inflow Turbulence Generation Methods, Annu. Rev. Fluid Mech., 49, 23–49, https://doi.org/10.1146/annurev-fluid-010816-060322, 2017. a
Wyngaard, J. C.: Toward Numerical Modeling in the “Terra Incognita”,
J. Atmos. Sci., 61, 1816–1826,
https://doi.org/10.1175/1520-0469(2004)061<1816:TNMITT>2.0.CO;2, 2004. a
Zhong, J., Cai, X., and Xie, Z.-T.: Implementation of a synthetic inflow turbulence generator in idealised WRF v3.6.1 large eddy simulations under neutral atmospheric conditions, Geosci. Model Dev., 14, 323–336, https://doi.org/10.5194/gmd-14-323-2021, 2021. a
Zhou, B., Simon, J. S., and Chow, F. K.: The Convective Boundary Layer in the
Terra Incognita, J. Atmos. Sci., 71, 2545–2563,
https://doi.org/10.1175/JAS-D-13-0356.1, 2014.
a, b
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, b
Short summary
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.
In this paper, we provide a technical description of a newly developed interface for coupling...