## 6 Summary and Discussion
The time series for the 10 min mean wind speeds are plotted in Figure 19. The result indicates that the times to reach the maximum winds for the PALM only differ by around 30 min-1 h from the measurements at anemometers CB1, CB3, CB7, and CB8 (Table 8), and the simulated maximum winds are only slightly smaller than the measurements (Table 9). This suggests that simulations driven by GRAPES-Meso could provide guidance for traffic control as these anemometers are closer to the bridge deck and better represent the wind conditions experienced by the driver. However, the simulated wind speeds at CB5 and CB6 are smaller in this case; further investigation and tuning should be performed to improve the wind speed simulation for these two anemometers. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Maximum Wind Time** & **CB1** & **CB3** & **CB5** & **CB6** & **CB7** & **CB8** \\ \hline measurement & 21:41:00 & 21:45:00 & 19:01:00 & 21:51:00 & 21:51:00 & 21:51:00 \\ \hline RAMS & 22:09:10 & 18:30:50 & 18:31:00 & 18:31:00 & 18:31:00 & 18:31:00 \\ \hline PALM & 22:09:40 & 22:55:40 & 22:28:00 & 18:09:20 & 22:25:20 & 22:25:00 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Time at which the 10 min mean wind speeds reached the maximum for Saola. The coupled model is driven by GRAPES-Meso. As the simulation result of the coupling model is largely influenced by the underlying NWP model, one possible improvement is to leverage ensembles to provide the initial and boundary conditions for the mesoscale-CFD coupled model. With this approach, probabilistic guidance on the exceedance of the wind speed limit could be generated. However, such an approach would be very computationally demanding. Figure 19: Time series of measured and simulated 10 min mean wind speed for the six anemometers (**a**) CB1, (**b**) CB3, (**c**) CB5, (**d**) CB6, (**e**) CB7, and (**f**) CB8 on 1 September 2023 in local time. The simulated wind speeds are driven by GRAPES-Meso. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Wind Speed (m/s)** & **CB1** & **CB3** & **CB5** & **CB6** & **CB7** & **CB8** \\ \hline measurement & 30 & 29.1 & 44.9 & 41.7 & 31.2 & 21.7 \\ \hline RAMS & 20.6 & 17.1 & 19.4 & 19.5 & 17.0 & 17.2 \\ \hline \% diff of RAMS & \(-31\%\) & \(-41\%\) & \(-57\%\) & \(-53\%\) & \(-45\%\) & \(-21\%\) \\ \hline PALM & 24.2 & 24.7 & 24.5 & 23.8 & 26.3 & 19.7 \\ \hline \% diff of PALM & \(-19\%\) & \(-15\%\) & \(-45\%\) & \(-43\%\) & \(-16\%\) & \(-9\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Maximum of 10 min mean wind speed and percentage error with respect to the measurement for Saola. The coupled model is driven by GRAPES-Meso. Recently, [PERSON] et al. suggested a data-driven approach for typhoon-induced strong wind prediction on long-span bridges [38]. By developing data-driven predictive models, the wind speed forecasting process could potentially be accelerated compared to computationally intensive CFD simulations. Building upon this concept, one possible extension of the current work could be to simulate a large sample of time series of wind speeds along the bridge for different tropical cyclone track and intensity scenarios. These simulated wind speed time series could then be used as training data to develop a machine learning model capable of predicting the wind speed time series on the bridge given a forecast tropical cyclone track. This would enable the generation of ensemble-based wind speed predictions in operation, without the need to run computationally expensive CFD simulations in real time. Further tuning, including the assimilation of real wind data for the better representation of the wind structure of the typhoon, could be explored. More cases of tropical cyclones could be considered in future studies, at least for those with larger circulation and better captured by NWP models. An even higher spatial resolution and the even more realistic representation of the surrounding environment could also be considered. Conceptualization, P.-W.C.; methodology, P.-W.C.; software, K.-W.L. and K.-K.L.