# Characterization of Wind Guests: A Study Based on Meteorological Tower Observations
## 3 Results and Discussion
### Gust Parameterizations  
Besides the distribution fitting of gust descriptors, the parameterization of gusts is also of essential importance, particularly for numerical weather prediction (NWP) models, mainly because they do not explicitly address the gust [24]. A series of wind gust parameterization has been developed and applied to post-processing the output of the NWP model (e.g., [48]); conditional parameterizations based on observational data were also presented in many existing studies (e.g., [49]).  
Figure 4 is the scatter diagram of the relationship between gust factor and 10-min average wind speed and turbulent intensity at three different heights (i.e., 10 m, 40 m and 320 m) based on observational data. In general, the gust factor values are negatively correlated with average wind speed; such a relationship may change slightly as a function of height. The slope coefficient of linear fits ranged from \(-\)0.043 to \(-\)0.021. Note that the dependence of gust factor on average wind speed derived in this study appears to be more pronounced than that obtained by [PERSON] et al. [5], in which the fitted slope coefficient was about 0.0001. On the other hand, a strong positive correlation between the gust factor and the turbulence intensity can be seen in Figure 4. The slope coefficient from linear regression fits varied between 1.03 and 1.69; the intercept varied between about 1.11 and 1.39. In comparison with prior studies, the results showed that the results in this study  
indicate a good agreement with that presented by [PERSON] et al. (2018), whereas the regression fits by [PERSON] (2018), [PERSON] and [PERSON] (2018) tended to over-predict the gust factor.  
Figure 3: Probability density histogram and distribution fitting of observed data. The four types of two-parameter positive-valued distribution used in distribution fitting are Weibull, Gamma, Lognormal and Log-logistic. For each gust parameter, the optimal distribution (bolded) was identified according to the maximum Log-likelihood (LogL), as shown in Table 4. (**a**) 10-min average wind speed, (**b**) gust amplitude, (**c**) gust amplitude, (**d**) gust coefficient, (**e**) peak coefficient, (**f**) turbulent intensity, (**g**) rising time, (**h**) elapsed time, (**i**) rising amplitude, (**j**) elapsed amplitude, (**k**) gust asymmetry coefficient and (**l**) gust length scale.  
Similarly, Figure 5 illustrates the respective relationship between peak factor and mean wind speed, as well as turbulent intensity. [PERSON] et al. [5] reported that the peak factor failed to exhibit a significant linear dependence on either mean wind speed or turbulent intensity. As shown in Figure 5a, the dependence of peak factor on the average wind speed was, indeed, weak, with a slope coefficient ranging from \(-0.05\) to \(0.05\). The subsequent intercept was between \(2.27\) and \(2.44\), which is close to the constant peak factor (\(\sim\)2.2) obtained by [PERSON] and [PERSON] [48]. Meanwhile, the peak factor obtained at different heights exhibited a consistent negative dependence on turbulent intensity. The slope coefficient was about \(-0.68\) to \(-2.29\); the intercepts ranged from \(2.25\) to \(2.87\).