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. A complex and varied terrain has a great impact on thedistribution of wind energy resources, resulting in uncertainty inaccurately assessing wind energy resources. In this study, three wind speeddistributions of kernel, Weibull, and Rayleigh type for estimating averagewind power density were first compared by using meteorological tower datafrom 2018 to 2020 under varied desert steppe terrain contexts in northernChina. 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 eachperiod. The maximum and minimum values of surface roughness length over thewhole period are 0.15 and 0.12 m, respectively. The power-law model andthe logarithmic model are used to estimate the average power density valuesat six specific heights, which show greater differences in autumn andwinter, and smaller differences in spring and summer. The gradient of theincrease in average power density values with height is largest in autumnand winter, and smallest in spring and summer. Our findings suggest thatdynamic changes in three key parameters (c, k, and z0) should be accurately considered for estimating wind energy resources under varied desert steppe terrain contexts.

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