Articles | Volume 14, issue 6
https://doi.org/10.5194/gmd-14-3939-2021
https://doi.org/10.5194/gmd-14-3939-2021
Model evaluation paper
 | 
29 Jun 2021
Model evaluation paper |  | 29 Jun 2021

Surface representation impacts on turbulent heat fluxes in the Weather Research and Forecasting (WRF) model (v.4.1.3)

Carlos Román-Cascón, Marie Lothon, Fabienne Lohou, Oscar Hartogensis, Jordi Vila-Guerau de Arellano, David Pino, Carlos Yagüe, and Eric R. Pardyjak

Related authors

From weak to intense downslope winds: origin, interaction with boundary-layer turbulence and impact on CO2 variability
Jon Ander Arrillaga, Carlos Yagüe, Carlos Román-Cascón, Mariano Sastre, Maria Antonia Jiménez, Gregorio Maqueda, and Jordi Vilà-Guerau de Arellano
Atmos. Chem. Phys., 19, 4615–4635, https://doi.org/10.5194/acp-19-4615-2019,https://doi.org/10.5194/acp-19-4615-2019, 2019
Short summary
Interactions among drainage flows, gravity waves and turbulence: a BLLAST case study
C. Román-Cascón, C. Yagüe, L. Mahrt, M. Sastre, G.-J. Steeneveld, E. Pardyjak, A. van de Boer, and O. Hartogensis
Atmos. Chem. Phys., 15, 9031–9047, https://doi.org/10.5194/acp-15-9031-2015,https://doi.org/10.5194/acp-15-9031-2015, 2015
Short summary
The BLLAST field experiment: Boundary-Layer Late Afternoon and Sunset Turbulence
M. Lothon, F. Lohou, D. Pino, F. Couvreux, E. R. Pardyjak, J. Reuder, J. Vilà-Guerau de Arellano, P Durand, O. Hartogensis, D. Legain, P. Augustin, B. Gioli, D. H. Lenschow, I. Faloona, C. Yagüe, D. C. Alexander, W. M. Angevine, E Bargain, J. Barrié, E. Bazile, Y. Bezombes, E. Blay-Carreras, A. van de Boer, J. L. Boichard, A. Bourdon, A. Butet, B. Campistron, O. de Coster, J. Cuxart, A. Dabas, C. Darbieu, K. Deboudt, H. Delbarre, S. Derrien, P. Flament, M. Fourmentin, A. Garai, F. Gibert, A. Graf, J. Groebner, F. Guichard, M. A. Jiménez, M. Jonassen, A. van den Kroonenberg, V. Magliulo, S. Martin, D. Martinez, L. Mastrorillo, A. F. Moene, F. Molinos, E. Moulin, H. P. Pietersen, B. Piguet, E. Pique, C. Román-Cascón, C. Rufin-Soler, F. Saïd, M. Sastre-Marugán, Y. Seity, G. J. Steeneveld, P. Toscano, O. Traullé, D. Tzanos, S. Wacker, N. Wildmann, and A. Zaldei
Atmos. Chem. Phys., 14, 10931–10960, https://doi.org/10.5194/acp-14-10931-2014,https://doi.org/10.5194/acp-14-10931-2014, 2014

Related subject area

Atmospheric sciences
SLUCM+BEM (v1.0): a simple parameterisation for dynamic anthropogenic heat and electricity consumption in WRF-Urban (v4.3.2)
Yuya Takane, Yukihiro Kikegawa, Ko Nakajima, and Hiroyuki Kusaka
Geosci. Model Dev., 17, 8639–8664, https://doi.org/10.5194/gmd-17-8639-2024,https://doi.org/10.5194/gmd-17-8639-2024, 2024
Short summary
NAQPMS-PDAF v2.0: a novel hybrid nonlinear data assimilation system for improved simulation of PM2.5 chemical components
Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang
Geosci. Model Dev., 17, 8495–8519, https://doi.org/10.5194/gmd-17-8495-2024,https://doi.org/10.5194/gmd-17-8495-2024, 2024
Short summary
Source-specific bias correction of US background and anthropogenic ozone modeled in CMAQ
T. Nash Skipper, Christian Hogrefe, Barron H. Henderson, Rohit Mathur, Kristen M. Foley, and Armistead G. Russell
Geosci. Model Dev., 17, 8373–8397, https://doi.org/10.5194/gmd-17-8373-2024,https://doi.org/10.5194/gmd-17-8373-2024, 2024
Short summary
Observational operator for fair model evaluation with ground NO2 measurements
Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao
Geosci. Model Dev., 17, 8267–8282, https://doi.org/10.5194/gmd-17-8267-2024,https://doi.org/10.5194/gmd-17-8267-2024, 2024
Short summary
Valid time shifting ensemble Kalman filter (VTS-EnKF) for dust storm forecasting
Mijie Pang, Jianbing Jin, Arjo Segers, Huiya Jiang, Wei Han, Batjargal Buyantogtokh, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao
Geosci. Model Dev., 17, 8223–8242, https://doi.org/10.5194/gmd-17-8223-2024,https://doi.org/10.5194/gmd-17-8223-2024, 2024
Short summary

Cited articles

Angevine, W.: Surface representation impacts on turbulent heat fluxes in WRF (v.4.1.3), Comment on gmd-2020-371, Wayne Angevine, 12 February 2021, https://doi.org/10.5194/gmd-2020-371-RC1, 2021. a
Angevine, W. M., Bazile, E., Legain, D., and Pino, D.: Land surface spinup for episodic modeling, Atmos. Chem. Phys., 14, 8165–8172, https://doi.org/10.5194/acp-14-8165-2014, 2014. a, b
Anonymous: Surface representation impacts on turbulent heat fluxes in WRF (v.4.1.3), Reply on RC1, Carlos Román-Cascón, 10 March 2021, https://doi.org/10.5194/gmd-2020-371-AC1, 2021. a
Auffret, A. G., Kimberley, A., Plue, J., and Waldén, E.: Super-regional land-use change and effects on the grassland specialist flora, Nat. Commun., 9, 1–7, 2018. a
Ball, J. T., Woodrow, I. E., and Berry, J. A.: A Model Predicting Stomatal Conductance and its Contribution to the Control of Photosynthesis under Different Environmental Conditions, in: Progress in Photosynthesis Research, edited by: Biggins, J., Springer, Dordrecht, https://doi.org/10.1007/978-94-017-0519-6_48, 1987. a
Download
Short summary
The type of vegetation (or land cover) and its status influence the heat and water transfers between the surface and the air, affecting the processes that develop in the atmosphere at different (but connected) spatiotemporal scales. In this work, we investigate how these transfers are affected by the way the surface is represented in a widely used weather model. The results encourage including realistic high-resolution and updated land cover databases in models to improve their predictions.