Articles | Volume 14, issue 5
https://doi.org/10.5194/gmd-14-2503-2021
© 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-14-2503-2021
© 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
| 
06 May 2021
Development and technical paper |
| 06 May 2021
| 06 May 2021
WRF4PALM v1.0: a mesoscale dynamical driver for the microscale PALM model system 6.0
Dongqi Lin
CORRESPONDING AUTHOR
School of Physical and Chemical Sciences, University of Canterbury, Christchurch, New Zealand
School of Earth and Environment, University of Canterbury, Christchurch, New Zealand
Basit Khan
Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology (KIT), 82467 Garmisch-Partenkirchen, Germany
Marwan Katurji
School of Earth and Environment, University of Canterbury, Christchurch, New Zealand
Leroy Bird
Bodeker Scientific, Alexandra, New Zealand
Ricardo Faria
Oceanic Observatory of Madeira, Agência Regional para o Desenvolvimento da Investigação Tecnologia e Investigação, Madeira, Portugal
Laura E. Revell
School of Physical and Chemical Sciences, University of Canterbury, Christchurch, New Zealand
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Brian Nathan, Stefanie Kremser, Sara Mikaloff-Fletcher, Greg Bodeker, Leroy Bird, Ethan Dale, Dongqi Lin, Gustavo Olivares, and Elizabeth Somervell
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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.
Cited articles
Arakawa, A. and Lamb, V. R.: Computational design of the basic dynamical
processes of the UCLA general circulation model, General circulation models
of the atmosphere, 17, 173–265, 1977. a
Baldauf, M., Stephan, K., Klink, S., Schraff, C., Seifert, A., Förstner, J.,
Reinhardt, T., and Lenz, C.: The new very short range forecast model COSMO-LMK for the
convection-resolving scale, WGNE Blue Book, Research activities in atmospheric and oceanic
modelling, CAS/JSC Working Group on Numerical Experimentation, Report No. 37, WMO,
Geneva, Switzerland, 1–2, 2007. a
Bengtsson, L., Andrae, U., Aspelien, T., Batrak, Y., Calvo, J., de Rooy,
W., Gleeson, E., Hansen-Sass, B., Homleid, M., Hortal, M., Ivarsson, K., Lenderink, G.,
Niemelä, S., Nielsen, K. P., Onvlee, J., Rontu, L., Samuelsson, P., Muñoz, D. S., Subias, A.,
Tijm, S., Toll, V., Yang, X., and Køltzow, M. Ø.: The HARMONIE–AROME model configuration
in the ALADIN–HIRLAM NWP system, Mon. Weather Rev., 145, 1919–1935,
https://doi.org/10.1175/MWR-D-16-0417.1, 2017. a
Bergot, T., Escobar, J., and Masson, V.: Effect of small-scale surface
heterogeneities and buildings on radiation fog: Large-eddy simulation study
at Paris–Charles de Gaulle airport, Q. J. Roy.
Meteor. Soc., 141, 285–298, 2015. a
Bhati, S. and Mohan, M.: WRF-urban canopy model evaluation for the
assessment of heat island and thermal comfort over an urban airshed in India under varying
land use/land cover conditions, Geosci. Lett., 5, 27, https://doi.org/10.1186/s40562-018-0126-7, 2018. a
Chai, T. and Draxler, R. R.: Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature, Geosci. Model Dev., 7, 1247–1250, https://doi.org/10.5194/gmd-7-1247-2014, 2014. a
Envirionment Canterbury Regional Council: Christchurch and Ashley River,
Canterbury, New Zealand 2018, Open Topography, https://doi.org/10.5069/G91J97WQ, 2020. a
Faria, R.: WRF2PALM,
available at: https://github.com/ricardo88faria/WRF2PALM (last access: 23 April 2021), 2019. a
Gousseau, P., Blocken, B., Stathopoulos, T., and Van Heijst, G.: CFD simulation
of near-field pollutant dispersion on a high-resolution grid: a case study by
LES and RANS for a building group in downtown Montreal, Atmos.
Environ., 45, 428–438, 2011. a
Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock,
W. C., and Eder, B.: Fully coupled “online” chemistry within the WRF
model, Atmos. Environ., 39, 6957–6975, 2005. a
Gronemeier, T., Inagaki, A., Gryschka, M., and Kanda, M.: Large-eddy simulation
of a 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, b
Harlow, F. H. and Welch, J. E.: Numerical calculation of time-dependent viscous
incompressible flow of fluid with free surface, Phys. Fluids, 8,
2182–2189, 1965. a
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
Heldens, W., Burmeister, C., Kanani-Sühring, F., Maronga, B., Pavlik, D., Sühring, M., Zeidler, J., and Esch, T.: Geospatial input data for the PALM model system 6.0: model requirements, data sources and processing, Geosci. Model Dev., 13, 5833–5873, https://doi.org/10.5194/gmd-13-5833-2020, 2020. a, b, c, d
Hersbach, H., Bell, W., Berrisford, P., Horányi, A., Sabater, J. M., Nicolas, J.,
Radu, R., Schepers, D., Simmons, A., Soci, C., and Dee, D.: Global reanalysis: goodbye ERA-Interim,
hello ERA5, ECMWF newsletter, 259, 17-24, https://doi.org/10.21957/vf291hehd7, 2019. a
Hoyer, S. and Hamman, J.: xarray: ND labeled arrays and datasets in
Python, J. Open Res. Softw., 5, 10, https://doi.org/10.5334/jors.148, 2017. a
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough,
S. A., and Collins, W. D.: Radiative forcing by long lived greenhouse gases: Calculations with
the AER radiative transfer models, J. Geophys. Res.-Atmos., 113, D13103,
https://doi.org/10.1029/2008JD009944, 2008. a
Indasi, V., Lynch, M., McGann, B., and Sutton, J.: WIND RESOURCE ASSESSMENT
USING WRF MODEL IN COMPLEX TERRAIN, International Journal of Latest
Engineering Research and Applications, 02, 2455–7137, 2017. a
Janjić, Z. I.: The step-mountain eta coordinate model: Further developments
of the convection, viscous sublayer, and turbulence closure schemes, Mon.
Weather Rev., 122, 927–945, 1994. a
Janjić, Z. I.: The Mellor-Yamada level 2.5 turbulence closure scheme in the
NCEP Eta Model, World Meteorological Organization-Publications-WMO TD, pp.
4–14, 1996. a
Janjić, Z. I.: Nonsingular implementation of the Mellor-Yamada level 2.5
scheme in the NCEP Meso model, Mon. Weather Rev., 2001. a
Kadasch, E., Sühring, M., Gronemeier, T., and Raasch, S.: Mesoscale nesting interface of the PALM model system 6.0, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2020-285, in review, 2020. a, b, c, d
Kurppa, M., Hellsten, A., Auvinen, M., Raasch, S., Vesala, T., and Järvi,
L.: Ventilation and air Quality in city blocks using large eddy simulation – urban planning
perspective, Atmosphere, 9, 65, https://doi.org/10.3390/atmos9020065, 2018. 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, b, c
Ladwig, W.: wrf-python (version 1.3. 0)[Software], Tech. rep., UCAR/NCAR, Boulder,
Colorado, https://doi.org/10.5065/D6W094P1, 2019. a, b, c, d
Landcare Research: LCDB v5.0 – Land Cover Database version 5.0, Mainland New
Zealand,
available at: https://lris.scinfo.org.nz/layer/104400-lcdb-v50-land-cover-database-version-50-mainland-new-zealand/ (last access: 23 April 2021),
2020. a
Land Information New Zealand: New Zealand Building Outlines (All Sources),
available at: https://data.linz.govt.nz/layer/101292-nz-building-outlines-all-sources/ (last access: 23 April 2021),
2020a. a
Land Information New Zealand: New Zealand Parcels,
available at: https://data.linz.govt.nz/layer/51571-nz-parcels/ (last access: 23 April 2021),
2020b. a
Lin, D.: PALM model system 6.0 source code, revision r4550 [Data set], Zenodo, https://doi.org/10.5281/zenodo.4713316, 2020a. a
Lin, D.: WRF4PALM_release_v1.0 (Version WRF4PALM_v1.0), Zenodo,
https://doi.org/10.5281/zenodo.4017005, 2020b. a
Liu, C., Ikeda, K., Rasmussen, R., Barlage, M., Newman, A. J.,
Prein, A. F., Chen, F., Chen, L., Clark, M., Dai, A., Dudhia, J., Eidhammer, T., Gochis, D.,
Gutmann, E., Kurkute, S., Li, Y., Thompson, G., and Yates, D.: Continental-scale convectionpermitting
modeling of the current and future climate of North America, Clim. Dynam., 49, 71–95, https://doi.org/10.1007/s00382-016-3327-9, 2017. a
Macara, G. R.: The climate and weather of Canterbury, NIWA, Taihoro Nukurangi,
2016. 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, f
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, h
Monin, A. S. and Obukhov, A. M.: Basic laws of
turbulent mixing in the surface layer of the atmosphere, Contrib. Geophys. Inst. Acad. Sci.
USSR, 151, 163–187, 1954 (in Russian). a
Müller, M., Homleid, M., Ivarsson, K., Køltzow, M. A. Ø., Lindskog, M.,
Midtbø, K. H., Andrae, U., Aspelien, T., Berggren, L., Bjørge, D., Dahlgren, P., Kristiansen, J.,
Randriamampianina, R., Ridal, M., and Vignes, O.: AROME-MetCoOp: A Nordic convective-scale
operational weather prediction model, Weather Forecast., 32, 609–627,
https://doi.org/10.1175/WAF-D-16-0099.1, 2017. a
Sagaut, P.: Large eddy simulation for incompressible flows: an
introduction, Springer Science & Business Media, Berlin, Heidelberg, Germany,
https://doi.org/10.1007/b137536, 2006. a, b
Schalkwijk, J., Jonker, H. J., Siebesma, A. P., and Bosveld, F. C.: A year-long
large-eddy simulation of the weather over Cabauw: An overview, Mon.
Weather Rev., 143, 828–844, 2015. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner,
J., Wang, W., Powers, J. G., Duda, M. G., Barker, D. M., and Huang, X.-Y.: A Description of the
Advanced Research WRF Model Version 4, NCAR Technical Notes, No. NCAR/TN- 556+ STR,
National Center for Atmospheric Research, Boulder, Colorado, United States,
https://doi.org/10.5065/1dfh-6p97, 2019. a, b, c, d
Sukoriansky, S., Galperin, B., and Perov, V.: Application of a New Spectral
Theory of Stably Stratified Turbulence to the Atmospheric Boundary Layer over
Sea Ice, Bound.-Lay. Meteorol., 117, 231–257,
https://doi.org/10.1007/s10546-004-6848-4, 2005. a
Tewari, M., Wang, W., Dudhia, J., LeMone, M., Mitchell, K., Ek, M.,
Gayno, G., Wegiel, J., and Cuenca, R.: Implementation and verification of the united NOAH land
surface model in the WRF model, in: 20th Conference on Weather Analysis and
Forecasting/16th Conference on Numerical Weather Prediction, 10–15 January 2004, Seattle,
Washington, United States, 11–15, 2004. a
Thompson, G., Field, P. R., Rasmussen, R. M., and Hall, W. D.: Explicit
forecasts of winter precipitation using an improved bulk microphysics scheme.
Part II: Implementation of a new snow parameterization, Mon. Weather
Rev., 136, 5095–5115, 2008. a
Tiedtke, M.: A comprehensive mass flux scheme for cumulus parameterization in
large-scale models, Mon. Weather Rev., 117, 1779–1800, 1989. a
van Stratum, B. J. and Stevens, B.: The influence of misrepresenting the
nocturnal boundary layer on idealized daytime convection in large-eddy
simulation, J. Adv. Model. Earth Syst., 7, 423–436, 2015. a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T.,
Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett,
M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E.,
Carey, C. J., Polat, İ., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman,
R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van
Mulbregt, P., and SciPy 1.0 Contributors: SciPy 1.0: fundamental algorithms for scientific
computing in Python, Nature Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Vollmer, L., van Dooren, M., Trabucchi, D., Schneemann, J., Steinfeld,
G., Witha, B., Trujillo, J., and Kühn, M.: First comparison of LES of an offshore wind turbine
wake with dual-Doppler lidar measurements in a German offshore wind farm, in: Journal of
Physics: Conference Series, IOP Publishing, Wake Conference 2015, 9–11 June 2015, Visby,
Sweden, volume 625, https://doi.org/10.1088/1742-6596/625/1/012001, 2015. a, b
Willmott, C. J., Robeson, S. M., and Matsuura, K.: A refined index of model
performance, Int. J. Climatol., 32, 2088–2094, 2012. a
Wyszogrodzki, A. A., Miao, S., and Chen, F.: Evaluation of the coupling between
mesoscale-WRF and LES-EULAG models for simulating fine-scale urban
dispersion, Atmos. Res., 118, 324–345, 2012. a
Xie, Z.-T. and Castro, I. P.: Efficient generation of inflow conditions for
large eddy simulation of street-scale flows, Flow Turbul. Combust.,
81, 449–470, 2008. a
Zhang, C., Wang, Y., and Hamilton, K.: Improved representation of boundary
layer clouds over the southeast Pacific in ARW-WRF using a modified Tiedtke
cumulus parameterization scheme, Mon. Weather Rev., 139, 3489–3513,
2011. a
Short summary
We present an open-source toolbox WRF4PALM, which enables weather dynamics simulation within urban landscapes. WRF4PALM passes meteorological information from the popular Weather Research and Forecasting (WRF) model to the turbulence-resolving PALM model system 6.0. WRF4PALM can potentially extend the use of WRF and PALM with realistic boundary conditions to any part of the world. WRF4PALM will help study air pollution dispersion, wind energy prospecting, and high-impact wind forecasting.
We present an open-source toolbox WRF4PALM, which enables weather dynamics simulation within...
