# Research on Short-Term Prediction Methods for Small-Scale Three-Dimensional Wind Fields
## 4 Experimental Simulation and Result Analysis
### Visualisation of Boundary Condition Prediction Results  
During the selected simulation period, the wind field and temperature field were stable for the majority of the time. Taking the wind field as an example, Figure 11(a) is a visualisation of the wind speed component obtained from WRF for the period from 21:20 to 22:50 on 23 March 2022. It can be seen that the overall trend of the wind field during this period was relatively stable. The black dashed box in the figure indicates the process of the wind speed component gradually decreasing over time at that location. Figure 11(b) shows the visualisation of the wind speed component from 22:10 to 22:50 predicted based on the visualisation of the wind speed component from 21:20 to 22:00 using the ConvLSTM-DSConvGRU model. The RMSEs of the predicted frames \(\hat{I}_{6}\)-\(\hat{I}_{10}\) were 0.464 m/s, 0.664 m/s, 0.848 m/s, 1.059 m/s, and 1.311 m/s, respectively. Figure 11(b) shows that the hybrid model accurately predicted the overall structure and distribution of the wind field, while also providing a good prediction of the gradual decrease in wind speed at the location indicated by the black dashed box. Figure 11(c) shows the absolute error between the wind field component distribution images from the WRF output and the predicted images. The prediction errors of each frame are mainly concentrated in the lower right area where significant changes in the wind field occurred. Furthermore, these errors tended to increase over time, which is consistent with the results shown in Figure 10(a).  
However, the wind field underwent dramatic changes over a large area during certain periods, as shown in Figure 12(a). The variation in the wind speed component in frames \(\hat{I}_{1}\)-\(\hat{I}_{7}\) of the image was relatively stable, but in frame \(\hat{I}_{8}\), the wind speed component in the upper half suddenly increased and continued to grow over time. This resulted in significant errors in frames \(\hat{I}_{8}\)-\(\hat{I}_{10}\) of the predicted images of the hybrid network, as shown in Figure 12(b). Specifically, the RMSEs for frames \(\hat{I}_{6}\)-\(\hat{I}_{10}\) were 0.930 m/s, 1.300 m/s, 1.825 m/s, 2.226 m/s, and 2.633 m/s, respectively. Figure 12(c) shows that the errors are primarily concentrated  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Number of Parameters** & **Runtime (s)** & **RMSE1 (m/s)** & **MAPE1 (\%)** & **RMSE2 (K)** & **MAPE2 (\%)** \\ \hline
2D-CNN & 0.697 M & 0.198 & 1.678 & 28.688 & 0.596 & 0.226 \\
3D-CNN & 2.475 M & 0.370 & 1.549 & 23.669 & 0.537 & 0.121 \\
3D-ConvLSTM & 11.596 M & 1.735 & 1.448 & 18.778 & 0.462 & 0.100 \\ SA-ConvLSTM & 5.933 M & 1.194 & 1.545 & 23.509 & 0.499 & 0.111 \\ The algorithm of this study & 3.767 M & 0.735 & 1.384 & 18.382 & 0.465 & 0.102 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of the comparative experiment using different models.  
in the upper half of the predicted frames. This result is reasonable because the wind field was stable and calm most of the time compared with the cases where the wind field underwent large and dramatic changes. As a result, the proportion of wind field variations with significant changes in the training dataset was relatively small, resulting in a poor prediction performance of the hybrid network.