IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i14p10806-d1190664.html

Seasons Effects of Field Measurement of Near-Ground Wind Characteristics in a Complex Terrain Forested Region

Author

Listed:
  • Hao Yue

    (School of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China)

  • Yagebai Zhao

    (School of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China)

  • Dabo Xin

    (School of Civil Engineering and Architecture, Hainan University, Haikou 570228, China)

  • Gaowa Xu

    (School of Architecture and Civil Engineering, Jiangsu University of Science and Technology, Zhenjiang 212100, China)

Abstract

The wind characteristics of the atmospheric boundary layer in forested regions exhibit a significant complexity due to rugged terrain, seasonal climate variability, and seasonal growth of vegetation, which play a key role not only in designing optimal blades to gain better performance but also in assessing the structural response, and there is a paucity of research on such wind fields. Therefore, this paper investigates wind characteristics via on-site wind field measurement. The mean and fluctuating wind characteristics of the forested region in different seasons were analyzed based on the field measurement data. The results show that for the mean wind characteristics, the seasonally fitted exponents play a decisive role in characterizing the mean wind profile, while the season and temperature are the key factors affecting the mean wind direction in forested regions. For fluctuating wind characteristics, the seasonal power-law function can accurately characterize the turbulence intensity profile. Moreover, the ratio of the three turbulence intensity components is significantly affected by temperature and season, and the Von Kármán spectrum has better applicability in the cold and less canopy-disturbed winter than in the other three seasons. The proposed seasonally fitted parameters show better applicability in terms of vertical coherence.

Suggested Citation

  • Hao Yue & Yagebai Zhao & Dabo Xin & Gaowa Xu, 2023. "Seasons Effects of Field Measurement of Near-Ground Wind Characteristics in a Complex Terrain Forested Region," Sustainability, MDPI, vol. 15(14), pages 1-33, July.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:10806-:d:1190664
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/14/10806/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/14/10806/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wen, Jiahao & Zhou, Lei & Zhang, Hongfu, 2023. "Mode interpretation of blade number effects on wake dynamics of small-scale horizontal axis wind turbine," Energy, Elsevier, vol. 263(PA).
    2. Yu-Ling Hsiao, Cody & Sheng, Ni & Fu, Shenze & Wei, Xinyang, 2022. "Evaluation of contagious effects of China's wind power industrial policies," Energy, Elsevier, vol. 238(PB).
    3. Giannaros, Theodore M. & Melas, Dimitrios & Ziomas, Ioannis, 2017. "Performance evaluation of the Weather Research and Forecasting (WRF) model for assessing wind resource in Greece," Renewable Energy, Elsevier, vol. 102(PA), pages 190-198.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Reddy, K. Bheemalingeswara & Bhosale, Amit C., 2024. "Effect of number of blades on performance and wake recovery for a vertical axis helical hydrokinetic turbine," Energy, Elsevier, vol. 299(C).
    2. Fuquan Zhao & Fanlong Bai & Xinglong Liu & Zongwei Liu, 2022. "A Review on Renewable Energy Transition under China’s Carbon Neutrality Target," Sustainability, MDPI, vol. 14(22), pages 1-27, November.
    3. Guanghui Che & Daocheng Zhou & Rui Wang & Lei Zhou & Hongfu Zhang & Sheng Yu, 2024. "Wind Energy Assessment in Forested Regions Based on the Combination of WRF and LSTM-Attention Models," Sustainability, MDPI, vol. 16(2), pages 1-17, January.
    4. Dimitris K. Papanastasiou & Athanasios I. Gelasakis & Giorgos Papadopoulos & Dimitrios Melas & Kostas Douvis & Ioannis Faraslis & Stavros Keppas & Ioannis Stergiou & Anastasia Poupkou & Dimitris Volou, 2025. "Projected Heat-Stress in Sheep and Cattle in Greece Under Future Climate Change Scenarios," Agriculture, MDPI, vol. 15(20), pages 1-16, October.
    5. Tian, Chaonan & Niu, Tong & Wei, Wei, 2022. "Developing a wind power forecasting system based on deep learning with attention mechanism," Energy, Elsevier, vol. 257(C).
    6. Herrero-Novoa, Cristina & Pérez, Isidro A. & Sánchez, M. Luisa & García, Ma Ángeles & Pardo, Nuria & Fernández-Duque, Beatriz, 2017. "Wind speed description and power density in northern Spain," Energy, Elsevier, vol. 138(C), pages 967-976.
    7. Dayal, Kunal K. & Bellon, Gilles & Cater, John E. & Kingan, Michael J. & Sharma, Rajnish N., 2021. "High-resolution mesoscale wind-resource assessment of Fiji using the Weather Research and Forecasting (WRF) model," Energy, Elsevier, vol. 232(C).
    8. Duarte Jacondino, William & Nascimento, Ana Lucia da Silva & Calvetti, Leonardo & Fisch, Gilberto & Augustus Assis Beneti, Cesar & da Paz, Sheila Radman, 2021. "Hourly day-ahead wind power forecasting at two wind farms in northeast Brazil using WRF model," Energy, Elsevier, vol. 230(C).
    9. Santos, J.V.C. & Moreira, D.M. & Moret, M.A. & Nascimento, E.G.S., 2019. "Analysis of long-range correlations of wind speed in different regions of Bahia and the Abrolhos Archipelago, Brazil," Energy, Elsevier, vol. 167(C), pages 680-687.
    10. Ou, Yinlin & Hsiao, Cody Yu-Ling & Chui, Chin Man, 2024. "How does the supply chain market respond to policy shocks? Evidence from solar photovoltaic sectors in China," Renewable Energy, Elsevier, vol. 232(C).
    11. Effrosyni Giama & Georgios Chantzis & Serafim Kontos & Stavros Keppas & Anastasia Poupkou & Natalia Liora & Dimitrios Melas, 2022. "Building Energy Simulations Based on Weather Forecast Meteorological Model: The Case of an Institutional Building in Greece," Energies, MDPI, vol. 16(1), pages 1-15, December.
    12. Dercas, Nicholas & Tziatzios, Georgios A. & Sidiropoulos, Pantelis & Sarchani, Sofia & Faraslis, Ioannis & Belaud, Gilles & Daudin, Kevin & Spiliotopoulos, Marios & Sakellariou, Stavros & Alpanakis, N, 2025. "Monitoring satellite-based crop irrigation water requirements for maize, cotton and hay in distinct Mediterranean regions," Agricultural Water Management, Elsevier, vol. 319(C).
    13. Nicholas Christakis & Ioanna Evangelou & Dimitris Drikakis & George Kossioris, 2024. "A Computational Methodology for Assessing Wind Potential," Energies, MDPI, vol. 17(6), pages 1-23, March.
    14. Argüeso, D. & Businger, S., 2018. "Wind power characteristics of Oahu, Hawaii," Renewable Energy, Elsevier, vol. 128(PA), pages 324-336.
    15. Chunyi Lu & Zhuoqi Teng & Yu Gao & Renhong Wu & Md. Alamgir Hossain & Yuantao Fang, 2022. "Analysis of Early Warning of RMB Exchange Rate Fluctuation and Value at Risk Measurement Based on Deep Learning," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1501-1524, April.
    16. Zhou, Lei & Wen, Jiahao & Wang, Zhaokun & Deng, Pengru & Zhang, Hongfu, 2023. "High-fidelity wind turbine wake velocity prediction by surrogate model based on d-POD and LSTM," Energy, Elsevier, vol. 275(C).
    17. Perini de Souza, Noele Bissoli & Sperandio Nascimento, Erick Giovani & Bandeira Santos, Alex Alisson & Moreira, Davidson Martins, 2022. "Wind mapping using the mesoscale WRF model in a tropical region of Brazil," Energy, Elsevier, vol. 240(C).
    18. Katikas, Loukas & Dimitriadis, Panayiotis & Koutsoyiannis, Demetris & Kontos, Themistoklis & Kyriakidis, Phaedon, 2021. "A stochastic simulation scheme for the long-term persistence, heavy-tailed and double periodic behavior of observational and reanalysis wind time-series," Applied Energy, Elsevier, vol. 295(C).
    19. Bangjun Wang & Qiaoqiao Xing, 2022. "Evaluation of the Wind Power Industry Policy in China (2010–2021): A Quantitative Analysis Based on the PMC Index Model," Energies, MDPI, vol. 15(21), pages 1-14, November.
    20. Zhao, Weigang & Wang, Xinye & Yan, Ying, 2025. "The substitution of fossil fuels for renewables in the electricity mix of China: From the perspectives of generation, capacity, and demand," Energy, Elsevier, vol. 315(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:10806-:d:1190664. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.