Preprints
https://doi.org/10.5194/amt-2021-375
https://doi.org/10.5194/amt-2021-375

  01 Dec 2021

01 Dec 2021

Review status: a revised version of this preprint is currently under review for the journal AMT.

Estimating vertical wind power density by using tower observation and empirical models over varied desert steppe terrain in northern China

Shaohui Zhou1, Yuanjian Yang1, Zhiqiu Gao1,2, Xingya Xi3, Zexia Duan1, and Yubin Li1 Shaohui Zhou et al.
  • 1Collaborative Innovation Centre on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, China
  • 2State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
  • 3Collaborative Innovation Centre of the Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China

Abstract. A complex and varied terrain has a great impact on the distribution of wind energy resources, resulting in uncertainty in accurately assessing wind energy resources. In this study, three wind speed distributions of kernel, Weibull, and Rayleigh type for estimating average wind power density were first compared by using meteorological tower data from 2018 to 2020 under varied desert steppe terrain contexts in northern China. Then three key parameters of scale factor (c) and shape factor (k) from the Weibull model and surface roughness (z0) were investigated for estimating wind energy resource. The results show that the Weibull distribution is the most suitable wind speed distribution over that terrain. The scale factor (c) in the Weibull distribution model increases with an increase in height, exhibiting an obvious form of power function. While there were two different forms for the relationship between the shape factor (k) and height: i.e., the reciprocal of the quadratic function and the logarithmic function, respectively. The estimated roughness length (z0) varied with the withering period, the growing period, and the lush period, which can be represented by the estimated median value in each period. The maximum and minimum values of surface roughness length over the whole period are 0.15 m and 0.12 m, respectively. The power-law model and the logarithmic model are used to estimate the average power density values at six specific heights, which show greater differences in autumn and winter, and smaller differences in spring and summer. The gradient of the increase in average power density values with height is largest in autumn and winter, and smallest in spring and summer. Our findings suggest that dynamic changes in three key parameters (c, k, and z0) should be accurately considered for estimating wind energy resources under varied desert steppe terrain contexts.

Shaohui Zhou et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2021-375', Anonymous Referee #2, 17 Dec 2021
    • AC1: 'Reply on RC1', shaohui zhou, 12 Jan 2022
  • RC2: 'Comment on amt-2021-375', Anonymous Referee #1, 22 Dec 2021
    • AC2: 'Reply on RC2', shaohui zhou, 12 Jan 2022

Shaohui Zhou et al.

Shaohui Zhou et al.

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Short summary
Our research has determined the possible relationship between Weibull natural wind mesoscale parameter c and shape factor k with height under the conditions of a desert steppe terrain in northern China, which has great potential in wind power generation. We have gained an enhanced understanding of the seasonal changes in the surface roughness of the desert grassland and the changes in the incoming wind direction.