Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 20
Articles | Volume 18, issue 20
https://doi.org/10.5194/gmd-18-7781-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-7781-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 20
Development and technical paper
 | 
27 Oct 2025
Development and technical paper |  | 27 Oct 2025

Coupling the TKE-ACM2 Planetary Boundary Layer Scheme with the Building Effect Parameterization Model

Wanliang Zhang, Chao Ren, Edward Yan Yung Ng, Michael Mau Fung Wong, and Jimmy Chi Hung Fung

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Cited articles

Arregocés, H. A., Rojano, R., and Restrepo, G.: Sensitivity analysis of planetary boundary layer schemes using the WRF model in Northern Colombia during 2016 dry season, Dynamics of Atmospheres and Oceans, 96, 101261, https://doi.org/10.1016/j.dynatmoce.2021.101261, 2021. a
Ayotte, K., Sullivan, P., Andren, A., Doney, S., Holtslag, B., Large, W., McWilliams, J., Moeng, C.-H., Otte, M., Tribbia, J., and Wyngaard, J.: An Evaluation of Neutral and Convective Planetary Boundary-Layer Parameterizations Relative to Large Eddy Simulations, Boundary-Layer Meteorology, 79, 131–175, https://doi.org/10.1007/BF00120078, 1996. a, b, c
Banks, R. F., Tiana-Alsina, J., Baldasano, J. M., Rocadenbosch, F., Papayannis, A., Solomos, S., and Tzanis, C. G.: Sensitivity of boundary-layer variables to PBL schemes in the WRF model based on surface meteorological observations, lidar, and radiosondes during the HygrA-CD campaign, Atmospheric Research, 176-177, 185–201, https://doi.org/10.1016/j.atmosres.2016.02.024, 2016. a
Bougeault, P. and Lacarrere, P.: Parameterization of Orography-Induced Turbulence in a Mesobeta–Scale Model, Monthly Weather Review, 117, 1872–1890, https://doi.org/10.1175/1520-0493(1989)117<1872:POOITI>2.0.CO;2, 1989. a, b, c, d
Britter, R. E. and Hanna, S. R.: Flow and Dispersion in Urban Areas, Annual Review of Fluid Mechanics, 35, 469–496, https://doi.org/10.1146/annurev.fluid.35.101101.161147, 2003. a
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This study focuses on improving the accuracy of numerical weather prediction (NWP) model particularly in urbanized areas. We coupled a recently validated boundary layer model with a building effect model within an NWP. Validation has been performed under idealized atmospheric conditions by benchmarking the coupled model with a fine-scale numerical model. Subsequently, the improvements and limitations are investigated aided by observations in real case simulations.
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