Overview of the PALM model system 6.0
-
Published:2020-03-20
Issue:3
Volume:13
Page:1335-1372
-
ISSN:1991-9603
-
Container-title:Geoscientific Model Development
-
language:en
-
Short-container-title:Geosci. Model Dev.
Author:
Maronga Björn, Banzhaf Sabine, Burmeister Cornelia, Esch Thomas, Forkel RenateORCID, Fröhlich DominikORCID, Fuka Vladimir, Gehrke Katrin FriedaORCID, Geletič JanORCID, Giersch SebastianORCID, Gronemeier TobiasORCID, Groß Günter, Heldens WiekeORCID, Hellsten Antti, Hoffmann FabianORCID, Inagaki Atsushi, Kadasch Eckhard, Kanani-Sühring Farah, Ketelsen Klaus, Khan Basit AliORCID, Knigge Christoph, Knoop HelgeORCID, Krč PavelORCID, Kurppa MonaORCID, Maamari Halim, Matzarakis AndreasORCID, Mauder MatthiasORCID, Pallasch Matthias, Pavlik Dirk, Pfafferott Jens, Resler JaroslavORCID, Rissmann Sascha, Russo EmmanueleORCID, Salim MohamedORCID, Schrempf Michael, Schwenkel JohannesORCID, Seckmeyer Gunther, Schubert SebastianORCID, Sühring Matthias, von Tils Robert, Vollmer Lukas, Ward Simon, Witha Björn, Wurps Hauke, Zeidler JulianORCID, Raasch Siegfried
Abstract
Abstract. In this paper, we describe the PALM model system 6.0. PALM (formerly an abbreviation for Parallelized Large-eddy Simulation Model and now an independent name) is a Fortran-based code and has been applied for studying a variety of atmospheric and oceanic boundary layers for about 20 years. The model is optimized for use on massively parallel computer architectures. This is a follow-up paper to the PALM 4.0 model description in Maronga et al. (2015). During the last years, PALM has been significantly improved and now offers a variety of new components. In particular, much effort was made to enhance the model with components needed for applications in urban environments, like fully interactive land surface and radiation schemes, chemistry, and an indoor model. This paper serves as an overview paper of the PALM 6.0 model system and we describe its current model core. The individual components for urban applications, case studies, validation runs, and issues with suitable input data are presented and discussed in a series of companion papers in this special issue.
Publisher
Copernicus GmbH
Reference133 articles.
1. Andersen, S. J., Witha, B., Breton, S.-P., Sørensen, J. N., Mikkelsen,
R. F., and Ivanell, S.: Quantifying variability of Large Eddy
Simulations of very large wind farms, J. Phys. Conf. Ser., 625, 012027, https://doi.org/10.1088/1742-6596/625/1/012027, 2015. a 2. Andrejczuk, M., Reisner, J. M., Henson, B., Dubey, M. K., and Jeffery, C. A.:
The potential impacts of pollution on a nondrizzling stratus deck: Does
aerosol number matter more than type?, J. Geophys. Res., 113, D19204, https://doi.org/10.1029/2007JD009445, 2008. a 3. Arakawa, A. and Lamb, V. R.: Computational design of the basic dynamical
processes of the UCLA general circulation model, in: General circulation models of the atmosphere, Methods in computational physics, edited by: Chang, J., Elsevier, 17, 173–265, https://doi.org/10.1016/B978-0-12-460817-7.50009-4, 1977. a 4. Auvinen, M., Järvi, L., Hellsten, A., Rannik, Ü., and Vesala, T.: Numerical framework for the computation of urban flux footprints employing large-eddy simulation and Lagrangian stochastic modeling, Geosci. Model Dev., 10, 4187–4205, https://doi.org/10.5194/gmd-10-4187-2017, 2017. a 5. 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
Cited by
207 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
|
|