## 3 Results and Discussion
### Estimation and Verification of Extreme Wind Speed  
GB50009 [38] (Chinese National Standard: Load Code for Building Structures) specified design wind pressures for Guangzhou corresponding to different return periods, along with conversion formulae for reference wind pressures:  
\[X_{T}=X_{10}+(X_{100}-X_{10})\bigg{(}\frac{\ln T}{\ln 10}-1\bigg{)} \tag{26}\]  
where \(X_{T}\) is the reference wind pressure (kN/m\({}^{2}\)) for return period \(T\) (years) and \(X_{10}\) is the reference wind pressure for the 10-year return period. The basic wind speed \(v_{0}\) (m/s) is derived from the reference wind pressure \(\omega_{0}\) (kN/m\({}^{2}\)) via  
\[v_{0}=\sqrt{2\omega_{0}/\rho} \tag{27}\]  
where the air density \(\rho\) (kg/m\({}^{3}\)) is calculated as \(\rho=1.25e^{-0.001Z}\), with \(Z\) being Guangzhou's altitude (m above sea level). The designed wind pressures and corresponding wind speeds for Guangzhou under different return periods are summarized in Table 3.  
There is currently no universally accepted formula for the determination of parameter B in China. When a fixed value for B is used, as long as the value is within a reasonable range, the simulated wind speeds can align well with the observed data. According to [PERSON] [39], in the context of the Yan Meng wind field model, a fixed B value of approximately 0.9 yields wind speed simulations that closely match observations. Therefore, in this study, a constant B value is adopted following this recommendation. The parameter \(Z_{0}\) is assigned a fixed value on the basis of the terrain characteristics of the target location, representing its surface roughness. In general, \(Z_{0}\) is smaller in flat and open areas and larger in densely built-up regions. According to previous studies [40; 41], for urban areas with dense building clusters, a \(Z_{0}\) value between 1.0 and 2.0 is recommended, whereas in cities with denser and taller buildings, a range of 2.0-4.0 is suggested. Considering that Guangzhou is a densely built-up city, \(Z_{0}\) is set as a fixed value of 2.0 in this study. The code specifies 10 min mean wind speeds at a height of 10 m. To enable comparative analysis with code specifications, this section calculates extreme wind speeds under identical conditions. As the [PERSON] model outputs hourly interval typhoon wind speeds, a conversion factor of 1.06 is applied to transform these values into equivalent 10 min averages [12; 27; 42]. During the generation of synthetic typhoons, the annual occurrence rate can be zero (i.e., no typhoon in a given year, approximately 10% of cases), and including these zeros directly in extreme-value fitting would severely bias the parameter estimates. Therefore, we fit the Weibull or Gumbel distribution only to the nonzero annual maximum wind speeds. When estimating return-level wind speeds, we then employ a dual strategy that combines a zero-inflated parametric approach with an empirical distribution method. On the one hand, if the probability corresponding to the target return period falls within the \"no- typhoon\" range, the return level is set to zero; otherwise, the remaining nonzero probability is rescaled and inverted through the fitted extreme-value distribution. On the other hand, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Return Period (Year)** & **10** & **30** & **50** & **100** & **200** \\ \hline Basic wind pressure (kN/m\({}^{2}\)) & 0.30 & 0.44 & 0.50 & 0.60 & 0.70 \\ Basic wind speed (m/s) & 21.94 & 26.62 & 28.37 & 31.08 & 33.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The design wind pressures and speeds given by the code for Guangzhou. we sort the full sample, including zeros, and directly select the ordered observation at the corresponding percentile as the empirical return level. This approach ensures that zero-occurrence years are properly accounted for while enabling cross-validation of the parametric model against the data-driven empirical estimates.