# Research on Short-Term Prediction Methods for Small-Scale Three-Dimensional Wind Fields
## 2 WRF-CFD Coupled Small-Scale Wind Field Simulation
### CFD Model Settings  
Figure 2 is the schematic diagram of the CFD complex terrain and building modelling and grid partitioning. First, the ASTER GDEM elevation data of the simulated domain at a resolution of 30 m were transformed using ArcGIS software 10.7. Then, the data were imported into Blender software 3.5 along with OSM map data for the initial modelling of complex terrain and buildings. The models were further refined and adjusted using SCDM software 2022R1, incorporating the actual building data provided by the Hong Kong Lands Department for 225 buildings near the airport. The adjusted models are shown in Figure 2, in which the red dots indicate the location of the Sha Chau Automatic Weather Station near Hong Kong International Airport. Finally, the models were imported into Fluent Meshing for grid partitioning, resulting in the computational domain grid model shown in Figure 2. The simulated domain size was 15,000 m \(\times\) 15,000 m, with a solution domain top height of 3000 m.  
The case adopted unstructured polyhedral hybrid grids. Slit control and curvature control were used to fit fine building features, and the BOI block was applied to locally \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Nested Domain** & **dx/km** & **dy/km** & \
\begin{tabular}{c} **Number of Regional** \\ **Grid Points** \\ \end{tabular} &
\begin{tabular}{c} **Regional Integration** \\ **Step/s** \\ \end{tabular} \\ \hline d01 & 9 & 9 & 61 \(\times\) 61 & 36 \\ d02 & 3 & 3 & 61 \(\times\) 61 & 12 \\ d03 & 1 & 1 & 61 \(\times\) 61 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter settings for nested grids for WRF simulations. Figure 2: Schematic diagram of CFD complex terrain and building modelling and grid partitioning. (**a**) Complex terrain and building modelling; (**b**) overall computational domain and grid partitioning. refine the area below 600 m above the airport buildings to capture the wind field changes caused by buildings. Polyhedral grids were used to fit the model boundaries, ensuring a high-accuracy representation of complex geometries such as buildings and terrains while maintaining topological consistency. Hexahedral grids were used to partition the central wind field area to improve the efficiency and stability of subsequent numerical calculations. In the simulations, we used three grid partitioning schemes. For scheme 1, the maximum grid size in the refined area of the BOI block was set to be 50 m, the maximum grid spacing was set to be 150 m, and 1.83 million grids were generated. Li have demonstrated that a grid spacing of 150 m was sufficient to resolve the terrain-induced fluctuations in CFD simulations [23]. Finer grids were used for scheme 2. The maximum grid size was set to be 30 m, the maximum grid spacing was set to be 100 m, and 2.95 million grids were generated. Coarse grids were used for scheme 3 with a maximum grid size of 70 m, a maximum grid spacing of 200 m, and 1.02 million grids. For the three grid partitioning schemes, we used the output meteorological datasets at 2:00 on 27 March 2022 for the d03 domain as the boundary conditions for further wind speed simulations in the CFD model. We demonstrate the wind speed profile in Figure 3, taking a spot close to the airport runway as an example. It was noted that the wind speed was normally underestimated using the coarse grids, i.e., grid partitioning scheme 2, and the wind speed profiles almost coincided for scheme 1 and scheme 3. Hence, in the following discussions, we used grid partitioning scheme 1, considering both the computation precision and resources. The coupling between WRF and CFD enables the interpolation of the output data from the WRF model to the inflow boundary of the CFD-simulated domain. There are two commonly used interpolation methods: nearest neighbour interpolation and inverse distance weighted interpolation.