Articles | Volume 17, issue 2
https://doi.org/10.5194/gmd-17-815-2024
https://doi.org/10.5194/gmd-17-815-2024
Development and technical paper
 | 
31 Jan 2024
Development and technical paper |  | 31 Jan 2024

GEO4PALM v1.1: an open-source geospatial data processing toolkit for the PALM model system

Dongqi Lin, Jiawei Zhang, Basit Khan, Marwan Katurji, and Laura E. Revell

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Belda, M., Resler, J., Geletič, J., Krč, P., Maronga, B., Sühring, M., Kurppa, M., Kanani-Sühring, F., Fuka, V., Eben, K., Benešová, N., and Auvinen, M.: Sensitivity analysis of the PALM model system 6.0 in the urban environment, Geosci. Model Dev., 14, 4443–4464, https://doi.org/10.5194/gmd-14-4443-2021, 2021. a
Boeing, G.: OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks, Computers, Environment and Urban Systems, 65, 126–139, Elsevier, ISBN 0198-9715 2017. a, b, c, d, e, f, g, h, i
Bou-Zeid, E., Meneveau, C., and Parlange, M. B.: Large-eddy simulation of neutral atmospheric boundary layer flow over heterogeneous surfaces: Blending height and effective surface roughness, Water Resour. Res., 40, W02505, https://doi.org/10.1029/2003WR002475, 2004. a
Chin, T. M., Vazquez-Cuervo, J., and Armstrong, E. M.: A multi-scale high-resolution analysis of global sea surface temperature, Remote Sens. Environ., 200, 154–169, https://doi.org/10.1016/j.rse.2017.07.029, 2017. a
Envirionment Canterbury Regional Council: Christchurch and Ashley River, Canterbury, New Zealand 2018, https://doi.org/10.5069/G91J97WQ, 2020. a, b, c, d
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Short summary
GEO4PALM is an open-source tool to generate static input for the Parallelized Large-Eddy Simulation (PALM) model system. Geospatial static input is essential for realistic PALM simulations. However, existing tools fail to generate PALM's geospatial static input for most regions. GEO4PALM is compatible with diverse geospatial data sources and provides access to free data sets. In addition, this paper presents two application examples, which show successful PALM simulations using GEO4PALM.
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