Articles | Volume 25, issue 15
https://doi.org/10.5194/acp-25-8427-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-8427-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Aug 2025
Research article |
| 01 Aug 2025
| 01 Aug 2025
Characteristics of boundary layer turbulence energy budget in Shenzhen area based on coherent wind lidar observations
Jinhong Xian
Shenzhen National Climate Observatory, Meteorological Bureau of Shenzhen Municipality, Shenzhen 518040, China
School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
Zongxu Qiu
Shenzhen National Climate Observatory, Meteorological Bureau of Shenzhen Municipality, Shenzhen 518040, China
Huayan Rao
Shenzhen National Climate Observatory, Meteorological Bureau of Shenzhen Municipality, Shenzhen 518040, China
Zhigang Cheng
Key Laboratory of Urban Meteorology, China Meteorological Administration, Beijing 100089, China
Xiaoling Lin
Shenzhen National Climate Observatory, Meteorological Bureau of Shenzhen Municipality, Shenzhen 518040, China
Chao Lu
Shenzhen National Climate Observatory, Meteorological Bureau of Shenzhen Municipality, Shenzhen 518040, China
Honglong Yang
CORRESPONDING AUTHOR
Shenzhen National Climate Observatory, Meteorological Bureau of Shenzhen Municipality, Shenzhen 518040, China
Ning Zhang
CORRESPONDING AUTHOR
School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
Key Laboratory of Urban Meteorology, China Meteorological Administration, Beijing 100089, China
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Cited articles
Barman, N., Borgohain, A., Kundu, S. S., Roy, R., Saha, B., Solanki, R., Kumar, N., and Raju, P. L. N.: Daytime Temporal Variation of Surface-Layer Parameters and Turbulence Kinetic Energy Budget in Topographically Complex Terrain Around Umiam, India, Bound.-Lay. Meteorol., 172, 149–166, https://doi.org/10.1007/s10546-019-00443-6, 2019.
Canut, G., Couvreux, F., Lothon, M., Legain, D., Piguet, B., Lampert, A., Maurel, W., and Moulin, E.: Turbulence fluxes and variances measured with a sonic anemometer mounted on a tethered balloon, Atmos. Meas. Tech., 9, 4375–4386, https://doi.org/10.5194/amt-9-4375-2016, 2016.
Caughey, S. J. and Wyngaard, J. C.: Turbulence kinetic-energy budget in convective conditions, Q. J. Roy. Meteor. Soc., 105, 231–239, 1979.
Chou, S. H., Atlas, D., and Yeh, E. N.: Turbulence in a convective marine atmospheric boundary-layer, J. Atmos. Sci., 43, 547–564, https://doi.org/10.1175/1520-0469(1986)043<0547:TIACMA>2.0.CO;2, 1986.
Darbieu, C., Lohou, F., Lothon, M., Vilà-Guerau de Arellano, J., Couvreux, F., Durand, P., Pino, D., Patton, E. G., Nilsson, E., Blay-Carreras, E., and Gioli, B.: Turbulence vertical structure of the boundary layer during the afternoon transition, Atmos. Chem. Phys., 15, 10071–10086, https://doi.org/10.5194/acp-15-10071-2015, 2015.
Deardorff, J. W.: Three-dimensional numerical study of turbulence in an entraining mixed layer, Bound.-Lay. Meteorol., 7, 199–226, 1974.
Elguernaoui, O., Reuder, J., Li, D., Maronga, B., Paskyabi, M. B., Wolf, T., and Esau, I.: The Departure from Mixed-Layer Similarity During the Afternoon Decay of Turbulence in the Free-Convective Boundary Layer: Results from Large-Eddy Simulations, Bound.-Lay. Meteorol., 188, 259–284, https://doi.org/10.1007/s10546-023-00812-2, 2023.
Endoh, T., Matsuno, T., Yoshikawa, Y., and Tsutsumi, E.: Estimates of the turbulent kinetic energy budget in the oceanic convective boundary layer, J. Oceanogr., 70, 81–90, https://doi.org/10.1007/s10872-013-0215-3, 2014.
Frenzen, P. and Vogel, C. A.: The turbulent kinetic-energy budget in the atmospheric surface-layer – a review and an experimental reexamination in the field, Bound.-Lay. Meteorol., 60, 49–76, https://doi.org/10.1007/BF00122061, 1992.
Guo, J., Zhang, J., Yang, K., Liao, H., Zhang, S., Huang, K., Lv, Y., Shao, J., Yu, T., Tong, B., Li, J., Su, T., Yim, S. H. L., Stoffelen, A., Zhai, P., and Xu, X.: Investigation of near-global daytime boundary layer height using high-resolution radiosondes: first results and comparison with ERA5, MERRA-2, JRA-55, and NCEP-2 reanalyses, Atmos. Chem. Phys., 21, 17079–17097, https://doi.org/10.5194/acp-21-17079-2021, 2021.
Heilman, W. E., Clements, C. B., Zhong, S., Clark, K. L., and Bian, X.: Atmospheric Turbulence, in: Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires, edited by: Manzello, S. L., Springer International Publishing, Cham, 1–17, https://doi.org/10.1007/978-3-319-51727-8_137-1, 2018.
Jensen, D. D., Price, T. A., Nadeau, D. F., Kingston, J., and Pardyjak, E. R.: Coastal Wind and Turbulence Observations during the Morning and Evening Transitions over Tropical Terrain, J. Appl. Meteorol. Clim., 56, 3167–3185, https://doi.org/10.1175/JAMC-D-17-0077.1, 2017.
Kaimal, J. C., Wyngaard, J. C., Haugen, D. A., Cote, O. R., Izumi, Y., Caughey, S. J., and Readings, C. J.: Turbulence Structure in Convective Boundary-Layer, J. Atmos. Sci., 33, 2152–2169, https://doi.org/10.1175/1520-0469(1976)033<2152:TSITCB>2.0.CO;2, 1976.
Kaimal, J. C. and Finnigan, J. J.: Atmospheric boundary layer flows, Atmospheric boundary layer flows, https://doi.org/10.1093/oso/9780195062397.001.0001, 1994.
Lang, C., Tao, S., Wang, X. J., Zhang, G., Li, J., and Fu, J. M.: Seasonal variation of polycyclic aromatic hydrocarbons (PAHs) in Pearl River Delta region, China, Atmos. Enviro., 41, 8370–8379, https://doi.org/10.1016/j.atmosenv.2007.06.015, 2007.
Lenschow, D. H.: Model of height variation of turbulence kinetic-energy budget in unstable planetary boundary-layer, J. Atmos. Sci., 31, 465–474, https://doi.org/10.1175/1520-0469(1974)031<0465:MOTHVO>2.0.CO;2, 1974.
Li, J., Dou, J. X., Lenschow, D. H., Zhou, M. Y., Meng, L. H., Qiu, X. B., Pan, Y. B., and Zhang, J. J.: Analysis of Boundary Layer Structure, Turbulence, and Flux Variations before and after the Passage of a Sea Breeze Front Using Meteorological Tower Data, J. Meteorol. Res.-PRC, 37, 855–877, https://doi.org/10.1007/s13351-023-3057-y, 2023.
Meng, D., Guo, J., Guo, X., Wang, Y., Li, N., Sun, Y., Zhang, Z., Tang, N., Li, H., Zhang, F., Tong, B., Xu, H., and Chen, T.: Elucidating the boundary layer turbulence dissipation rate using high-resolution measurements from a radar wind profiler network over the Tibetan Plateau, Atmos. Chem. Phys., 24, 8703–8720, https://doi.org/10.5194/acp-24-8703-2024, 2024.
Nilsson, E., Lohou, F., Lothon, M., Pardyjak, E., Mahrt, L., and Darbieu, C.: Turbulence kinetic energy budget during the afternoon transition – Part 1: Observed surface TKE budget and boundary layer description for 10 intensive observation period days, Atmos. Chem. Phys., 16, 8849–8872, https://doi.org/10.5194/acp-16-8849-2016, 2016a.
Nilsson, E., Lothon, M., Lohou, F., Pardyjak, E., Hartogensis, O., and Darbieu, C.: Turbulence kinetic energy budget during the afternoon transition – Part 2: A simple TKE model, Atmos. Chem. Phys., 16, 8873–8898, https://doi.org/10.5194/acp-16-8873-2016, 2016b.
Pozzobon, A. E. D., Acevedo, O. C., Puhales, F. S., Oliveira, P. E. S., Maroneze, R., and Costa, F. D.: Observed Budgets of Turbulence Kinetic Energy, Heat Flux, and Temperature Variance Under Convective and Stable Conditions, Bound.-Lay. Meteorol., 187, 619–642, https://doi.org/10.1007/s10546-023-00788-z, 2023.
Puhales, F. S., Rizza, U., Degrazia, G. A., and Acevedo, O. C.: A simple parameterization for the turbulent kinetic energy transport terms in the convective boundary layer derived from large eddy simulation, Physica A, 392, 583–595, https://doi.org/10.1016/j.physa.2012.09.028, 2013.
Qiu, Z. X., Xian, J. H., Yang, Y. X., Lu, C., Yang, H. L., Hu, Y. Y., Sun, J. Q., and Zhang, C. S.: Characteristics of Coastal Low-Level Jets in the Boundary Layer of the Pearl River Estuary, Journal of Marine Science and Engineering, 11, 1128, https://doi.org/10.3390/jmse11061128, 2023.
Solanki, R., Guo, J., Lv, Y., Zhang, J., Wu, J., Tong, B., and Li, J.: Elucidating the atmospheric boundary layer turbulence by combining UHF radar wind profiler and radiosonde measurements over urban area of Beijing, Urban Climate, 43, 101151, https://doi.org/10.1016/j.uclim.2022.101151, 2022.
Song, L., Deng, T., Li, Z.-N., Wu, S., He, G.-W., Li, F., Wu, M., and Wu, D.: Retrieval of boundary layer height and its influence on PM2.5 concentration based on LiDAR observation over Guangzhou, J. Trop. Meteorol., 27, 303–318, 2021.
Stull, R. B.: An Introduction to Boundary Layer Meteorology, Springer Netherlands, https://doi.org/10.1007/978-94-009-3027-8, 1988.
Therry, G. and Lacarrere, P.: Improving the eddy kinetic-energy model for planetary boundary-layer description, Bound.-Lay. Meteorol., 25, 63–88, https://doi.org/10.1007/BF00122098, 1983.
Wyngaard, J. C.: Turbulence in the Atmosphere, Cambridge University Press, New York, https://doi.org/10.1017/CBO9780511840524, 2010.
Xian, J., Yang, H., and Zhang, N.: Turbulent Energy Budget Analysis Based on Coherent Wind Lidar Observations, Zenodo [data set], https://doi.org/10.5281/zenodo.13624484, 2024a.
Xian, J., Lu, C., Lin, X., Yang, H., Zhang, N., and Zhang, L.: Directly measuring the power-law exponent and kinetic energy of atmospheric turbulence using coherent Doppler wind lidar, Atmos. Meas. Tech., 17, 1837–1850, https://doi.org/10.5194/amt-17-1837-2024, 2024b.
Xian, J., Luo, H. Y., Lu, C., Lin, X. L., Yang, H. L., and Zhang, N.: Characteristics of the atmospheric boundary layer height: A perspective on turbulent motion, Sci. Total Environ., 919, 170895, https://doi.org/10.1016/j.scitotenv.2024.170895, 2024c.
Xian, J., Qiu, Z., Luo, H., Hu, Y., Lin, X., Lu, C., Yang, Y., Yang, H., and Zhang, N.: Turbulent energy budget analysis based on coherent wind lidar observations, Atmos. Chem. Phys., 25, 441–457, https://doi.org/10.5194/acp-25-441-2025, 2025.
Yus-Díez, J., Udina, M., Soler, M. R., Lothon, M., Nilsson, E., Bech, J., and Sun, J.: Nocturnal boundary layer turbulence regimes analysis during the BLLAST campaign, Atmos. Chem. Phys., 19, 9495–9514, https://doi.org/10.5194/acp-19-9495-2019, 2019.
Zeman, O. and Tennekes, H.: Parameterization of turbulent energy budget at top of daytime atmospheric boundary-layer, J. Atmos. Sci., 34, 111–123, https://doi.org/10.1175/1520-0469(1977)034<0111:POTTEB>2.0.CO;2, 1977.
Zhou, M. Y., Lenschow, D. H., Stankov, B. B., Kaimal, J. C., and Gaynor, J. E.: Wave and turbulence structure in a shallow baroclinic convective boundary-layer and overlying inversion, J. Atmos. Sci., 42, 47–57, https://doi.org/10.1175/1520-0469(1985)042<0047:WATSIA>2.0.CO;2, 1985.
Zhou, Q. J., Li, L., Chan, P. W., Cheng, X. L., Lan, C. X., Su, J. C., He, Y. Q., and Yang, H. L.: Observational Study of Wind Velocity and Structures during Supertyphoons and Convective Gales over Land Based on a 356-m-High Meteorological Gradient Tower, J. Appl. Meteorol. Clim., 62, 103–118, https://doi.org/10.1175/JAMC-D-22-0013.1, 2023.
Short summary
We studied how turbulence kinetic energy (TKE) changes in the lower atmosphere over Shenzhen, focusing on its role in weather and climate. Using advanced wind lidar technology, we tracked how TKE varies with height and across seasons. We found that heat near the ground drives turbulence, while wind effects become stronger higher up. Our results help improve weather and climate models by providing better data on how turbulence behaves in the atmosphere, aiding understanding of climate change.
We studied how turbulence kinetic energy (TKE) changes in the lower atmosphere over Shenzhen,...
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