Articles | Volume 17, issue 3
https://doi.org/10.5194/amt-17-1123-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-1123-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Impacts of anemometer changes, site relocations and processing methods on wind speed trends in China
Yi Liu
School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
Lihong Zhou
School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
Yingzuo Qin
School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
Cesar Azorin-Molina
Centro de Investigaciones sobre Desertificación, Consejo Superior de Investigaciones Científicas (CIDE, CSIC-UV-Generalitat Valenciana), Climate, Atmosphere and Ocean Laboratory (Climatoc-Lab), Moncada, Valencia, Spain
Cheng Shen
Regional Climate Group, Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden
School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
Zhenzhong Zeng
CORRESPONDING AUTHOR
School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
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Cited articles
Azorin-Molina, C., Vicente-Serrano, S. M., McVicar, T. R., Jerez, S., Sanchez-Lorenzo, A., López-Moreno, J.-I., Revuelto, J., Trigo, R. M., Lopez-Bustins, J. A., and Espírito-Santo, F.: Homogenization and Assessment of Observed Near-Surface Wind Speed Trends over Spain and Portugal, 1961–2011, J. Climate, 27, 3692–3712, https://doi.org/10.1175/JCLI-D-13-00652.1, 2014.
Azorin-Molina, C., Asin, J., McVicar, T. R., Minola, L., Lopez-Moreno, J. I., Vicente-Serrano, S. M., and Chen, D.: Evaluating anemometer drift: A statistical approach to correct biases in wind speed measurement, Atmos. Res., 203, 175–188, https://doi.org/10.1016/j.atmosres.2017.12.010, 2018.
Azorin-Molina, C., Guijarro, J. A., McVicar, T. R., Trewin, B. C., Frost, A. J., and Chen, D.: An approach to homogenize daily peak wind gusts: An application to the Australian series, Int. J. Climatol., 39, 2260–2277, https://doi.org/10.1002/joc.5949, 2019.
Bathiany, S., Scheffer, M., van Nes, E. H., Williamson, M. S., and Lenton, T. M.: Abrupt Climate Change in an Oscillating World, Sci. Rep., 8, 5040, https://doi.org/10.1038/s41598-018-23377-4, 2018.
Camuffo, D., della Valle, A., Becherini, F., and Zanini, V.: Three centuries of daily precipitation in Padua, Italy, 1713–2018: history, relocations, gaps, homogeneity and raw data, Climatic Change, 162, 923–942, https://doi.org/10.1007/s10584-020-02717-2, 2020.
Cao, L. and Yan, Z.: Progress in Research on Homogenization of Climate Data, Advances in Climate Change Research, 3, 59–67, https://doi.org/10.3724/SP.J.1248.2012.00059, 2012.
Chappell, A. and Webb, N. P.: Using albedo to reform wind erosion modelling, mapping and monitoring, Aeolian Res., 23, 63–78, https://doi.org/10.1016/j.aeolia.2016.09.006, 2016.
China Meteorology Administration (CMA): National basic meteorological station in Guangzhou was relocated four times in 62 years, http://www.cma.gov.cn/2011xwzx/2011xmtjj/201110/t20111026_121807.html (last access: 22 December 2023), 2011.
China Meteorology Administration (CMA): Notice of the China meteorological administration on the issuance of provisional regulations on relocation and removal of national ground meteorological observation stations, http://www.gov.cn/gongbao/content/2013/content_2344560.htm (last access: 22 December 2023), 2012.
China Meteorological Administration (CMA): Meteorological data set description document, http://101.200.76.197:91/mekb/?r=data/detail&dataCode=SURF_CLI_CHN_MUL_DAY_V3.0 (last access: 22 December 2023), 2017.
China Meteorological Data Service Center (CMDSC): China Surface Climatic Data Daily Data Set 60 (Version 3.0), China Meteorological Data Service Center [data set], http://101.200.76.197:91/mekb/?r=data/cdcdetail&dataCode=SURF_CLI_CHN_MUL_DAY_V3.0 (last access: 13 February 2024), 2012.
Dunn, R. J. H., Azorin-Molina, C., Menne, M. J., Zeng, Z., Casey, N. W., and Shen, C.: Reduction in reversal of global stilling arising from correction to encoding of calm periods, Environ. Res. Commun., 4, 061003, https://doi.org/10.1088/2515-7620/ac770a, 2022.
Feng, S., Hu, Q., and Qian, W.: Quality control of daily meteorological data in China, 1951–2000: a new dataset, Int. J. Climatol., 24, 853–870, https://doi.org/10.1002/joc.1047, 2004.
Fu, G., Yu, J., Zhang, Y., Hu, S., Ouyang, R., and Liu, W.: Temporal variation of wind speed in China for 1961–2007, Theor. Appl. Climatol., 104, 313–324, https://doi.org/10.1007/s00704-010-0348-x, 2011.
He, Y., Chan, P. W., and Li, Q.: Standardization of raw wind speed data under complex terrain conditions: A data-driven scheme, J. Wind Eng. Ind. Aerod., 131, 12–30, https://doi.org/10.1016/j.jweia.2014.05.002, 2014.
Hong, H. P., Mara, T. G., Morris, R., Li, S. H., and Ye, W.: Basis for recommending an update of wind velocity pressures in Canadian design codes, Can. J. Civ. Eng., 41, 206–221, https://doi.org/10.1139/cjce-2013-0287, 2014.
Hu, W., Kong, L., Zhu, X., and Xue, W.: Accurancy analysis on contact anemometer self – recording records digitization processing system, Jounal of Arid Meteorology, 27, 168-171, http://www.ghqx.org.cn/CN/Y2009/V27/I2/168 (last access: 30 January 2024), 2009 (in Chinese).
Killick, R., Fearnhead, P., and Eckley, I. A.: Optimal Detection of Changepoints With a Linear Computational Cost, J. Am. Stat. Assoc., 107, 1590–1598, https://doi.org/10.1080/01621459.2012.737745, 2012.
Li, Y., Chen, Y., Li, Z., and Fang, G.: Recent recovery of surface wind speed in northwest China, Int. J. Climatol., 38, 4445–4458, https://doi.org/10.1002/joc.5679, 2018.
Liu, F., Sun, F., Liu, W., Wang, T., Wang, H., Wang, X., and Lim, W. H.: On wind speed pattern and energy potential in China, Appl. Energ., 236, 867–876, https://doi.org/10.1016/j.apenergy.2018.12.056, 2019.
Liu, Y., Zeng, Z., Xu, R., Ziegler, A. D., Jerez, S., Chen, D., Azorin-Molina, C., Zhou, L., Yang, X., Xu, H., Li, L., Dong, L., Zhou, F., Cao, R., Liu, J., Ye, B., Kuang, X., and Yang, X.: Increases in China's wind energy production from the recovery of wind speed since 2012, Environ. Res. Lett., 17, 114035, https://doi.org/10.1088/1748-9326/ac9cf4, 2022a.
Liu, Y., Xu, R., Ziegler, A. D., and Zeng, Z.: Stronger winds increase the sand-dust storm risk in northern China, Environ. Sci.-Atmos., 2, 1259–1262, https://doi.org/10.1039/D2EA00058J, 2022b.
Masters, F. J., Vickery, P. J., Bacon, P., and Rappaport, E. N.: Toward Objective, Standardized Intensity Estimates from Surface Wind Speed Observations, B. Am. Meteorol. Soc., 91, 1665–1682, https://doi.org/10.1175/2010BAMS2942.1, 2010.
McVicar, T. R., Roderick, M. L., Donohue, R. J., Li, L. T., Van Niel, T. G., Thomas, A., Grieser, J., Jhajharia, D., Himri, Y., Mahowald, N. M., Mescherskaya, A. V., Kruger, A. C., Rehman, S., and Dinpashoh, Y.: Global review and synthesis of trends in observed terrestrial near-surface wind speeds: Implications for evaporation, J. Hydrol., 416–417, 182–205, https://doi.org/10.1016/j.jhydrol.2011.10.024, 2012.
Rayner, D. P.: Wind Run Changes: The Dominant Factor Affecting Pan Evaporation Trends in Australia, J. Climate, 20, 3379–3394, https://doi.org/10.1175/JCLI4181.1, 2007.
Shen, C., Zha, J., Wu, J., and Zhao, D.: Centennial-Scale Variability of Terrestrial Near-Surface Wind Speed over China from Reanalysis, J. Climate, 34, 5829–5846, https://doi.org/10.1175/JCLI-D-20-0436.1, 2021.
Sohu: Due to lack of sufficient attention, 60 % of the national ground meteorological observation stations were forced to relocate, https://news.sohu.com/20040921/n222160625.shtml (last access: 22 December 2023), 2004 (in Chinese).
Thiessen, A. H.: Precipitation Averages for Large Areas, Mon. Weather Rev., 39, 1082, https://doi.org/10.1175/1520-0493(1911)39<1082b:PAFLA>2.0.CO;2, 1911.
Tian, Q., Huang, G., Hu, K., and Niyogi, D.: Observed and global climate model based changes in wind power potential over the Northern Hemisphere during 1979–2016, Energy, 167, 1224–1235, https://doi.org/10.1016/j.energy.2018.11.027, 2019.
Trewin, B.: Exposure, instrumentation, and observing practice effects on land temperature measurements, WIREs Climate Change, 1, 490–506, https://doi.org/10.1002/wcc.46, 2010.
Vautard, R., Cattiaux, J., Yiou, P., Thépaut, J.-N., and Ciais, P.: Northern Hemisphere atmospheric stilling partly attributed to an increase in surface roughness, Nat. Geosci., 3, 756–761, https://doi.org/10.1038/ngeo979, 2010.
Wan, H., Wang, X. L., and Swail, V. R.: Homogenization and Trend Analysis of Canadian Near-Surface Wind Speeds, J. Climate, 23, 1209–1225, https://doi.org/10.1175/2009JCLI3200.1, 2010.
Wang, X., Piao, S., Ciais, P., Li, J., Friedlingstein, P., Koven, C., and Chen, A.: Spring temperature change and its implication in the change of vegetation growth in North America from 1982 to 2006, P. Natl. Acad. Sci. USA, 108, 1240–1245, https://doi.org/10.1073/pnas.1014425108, 2011.
Wang, X. L.: Accounting for Autocorrelation in Detecting Mean Shifts in Climate Data Series Using the Penalized Maximal t or F Test, J. Appl. Meteorol. Clim., 47, 2423–2444, https://doi.org/10.1175/2008JAMC1741.1, 2008.
World Meteorological Organization (WMO): WMO guidelines on the calculation of climate normals, WMO-No. 1203, https://library.wmo.int/viewer/55797?medianame=1203_en_#page=1&viewer=picture&o=bookmark&n=0&q= (last access: 22 December 2023), 2017.
World Meteorological Organization (WMO): Guide to Instruments and Methods of Observation. Volume V – Quality Assurance and Management of Observing Systems, 2018 edition, World Meteorological Organization, https://doi.org/10.25607/OBP-690, 2018.
World Meteorological Organization (WMO): Guidelines on Homogenization, 2020 edition, World Meteorological Organization, https://doi.org/10.25607/OBP-1920, 2020.
Xin, Y., Chen, H., and Li, Y.: Homogeneity adjustment of annual mean wind speed and elementary calculation of fundamental wind pressure over Xinjiang meteorological stations, Climatic and Environmental Research, 17, 184–196, https://doi.org/10.3878/j.issn.1006-9585.2011.10093, 2012 (in Chinese).
Yang, Q., Li, M., Zu, Z., and Ma, Z.: Has the stilling of the surface wind speed ended in China?, Sci. China Earth Sci., 64, 1036–1049, https://doi.org/10.1007/s11430-020-9738-4, 2021.
Zeng, Z., Ziegler, A., Searchinger, T., Yang, L., Chen, A., Ju, K., Piao, S., Li, L., Ciais, P., Chen, D., Liu, J., Azorin-Molina, C., Chappell, A., Medvigy, D., and Wood, E.: A reversal in global terrestrial stilling and its implications for wind energy production, Nat. Clim. Change, 9, 1–7, https://doi.org/10.1038/s41558-019-0622-6, 2019.
Zha, J., Shen, C., Zhao, D., Wu, J., and Fan, W.: Slowdown and reversal of terrestrial near-surface wind speed and its future changes over eastern China, Environ. Res. Lett., 16, 034028, https://doi.org/10.1088/1748-9326/abe2cd, 2021.
Zhang, G., Azorin-Molina, C., Wang, X., Chen, D., McVicar, T. R., Guijarro, J. A., Chappell, A., Deng, K., Minola, L., Kong, F., Wang, S., and Shi, P.: Rapid urbanization induced daily maximum wind speed decline in metropolitan areas: A case study in the Yangtze River Delta (China), Urban Climate, 43, 101147, https://doi.org/10.1016/j.uclim.2022.101147, 2022.
Short summary
Our research analyzed China's wind speed data and addressed inconsistencies caused by factors like equipment changes and station relocations. After improving data quality, China's recent wind speed decrease reduced by 41 %, revealing an increasing trend. This emphasizes the importance of rigorous data processing for accurate trend assessments in various research fields.
Our research analyzed China's wind speed data and addressed inconsistencies caused by factors...