# Research on Short-Term Prediction Methods for Small-Scale Three-Dimensional Wind Fields
## 2 WRF-CFD Coupled Small-Scale Wind Field Simulation
### Analysis of CFD Results  
We compared the differences between direct simulation using the WRF model and coupled simulation with two different boundary value assignment methods for the period from 0:00 27 March 2022 to 24:00 29 March 2022. The objective was to quantitatively analyse the comparison between the measured wind speed at the 30 m height of the Sha Chau Automatic Weather Station (refer to Figure 2a) and the simulated wind speed at the 30 m height of the Sha Chau Automatic Weather Station using three different methods. The calculation case was solved using the Realizable k-\(\varepsilon\) model in the RANS method, and the specific parameter settings are shown in Table 2. Figure 4a,b show the comparison of using the nearest neighbour interpolation method and the inverse distance weighted interpolation method to interpolate the wind speed component (u) to the CFD east inflow boundary, respectively. Figure 5a,b) depict the comparison of using the nearest neighbour interpolation method and the inverse distance weighted interpolation method to interpolate the temperature variable to the CFD east inflow boundary, respectively. It can be seen that for both the speed and temperature variables, the inverse distance weighted interpolation method depicts the detailed characteristics of the variable distribution more accurately compared with the nearest neighbour interpolation method. The statistical analysis of the simulation results obtained from the WRF model simulation coupled with the two interpolation methods is shown in Table 3. It can be seen that whether it is the results simulated directly by the WRF model or the results simulated by the coupling of interpolation and CFD, the average error is greater than zero, indicating that all three simulation methods overestimated the wind speed at the Sha Chau Automatic Weather Station. Compared with the simulation results of the WRF model, those of the coupled WRF and CFD model showed significantly reduced average error, root mean square error (RMSE), and mean absolute error. The error assessment indicators of the inverse distance weighted interpolation method were lower than those of the nearest neigh \begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Parameter** & **Setting** \\ \hline \multirow{4}{*}{Boundary condition} & Inflow boundary & Grid point speed and temperature \\  & Outflow boundary & Pressure outlet boundary \\  & Top boundary & Grid point speed and temperature \\  & Bottom boundary & Non-slip wall surface \\ \hline \multirow{4}{*}{Solving solution} & Solver software & Fluent 2022R1 \\  & Turbulence model & Realizable k-\(\varepsilon\) model \\ \cline{1-1}  & Pressure-speed coupling & SIMPLE \\ \cline{1-1}  & scheme & Second-order upwind scheme \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Settings of CFD solver parameter. bour interpolation method. Therefore, it can be concluded that the accuracy of simulating by coupling the inverse distance weighted interpolation method with WRF and CFD is superior to that of the nearest neighbour interpolation method and direct WRF simulation. Figure 6a-d show the results of the planar wind field calculations using the inverse distance weighted interpolation method. Our analysis showed that due to the impact of airflow on the terrain and buildings near the ground, a large amount of turbulent vortex motion was generated. As a result, significant wind speed gradients were observed at elevations of 50 m and 200 m (Figure 6c,d)). However, a significant reduction in wind speed was observed on their lee side due to the obstruction of terrain and buildings. For example, in Figure 6a, at the location of 12,000-14,000 m in the x-direction, and in Figure 6b, at the location of 8000 m (dashed box) in the y-direction, there are evident deceleration  
Figure 4: Wind speed components u at the CFD east boundary obtained with the two interpolation methods. (**a**) Nearest neighbour interpolation method; (**b**) inverse distance weighted interpolation method.