# Research on Short-Term Prediction Methods for Small-Scale Three-Dimensional Wind Fields
## 4 Experimental Simulation and Result Analysis  
### Construction of Datasets  
To verify the effectiveness of the proposed prediction model, the output results of the WRF model for domain d03 at a frequency of every 10 min from 15 March 2022 12:00 to 31 March 2022 12:00, i.e., a duration of 16 days, were used as the initial data (Table 1). A total of 2305 outputs were obtained in this duration. To make it convenient for network training and to save computational resources, this study selected the ARWpost tool to post-process the initial data. The data below the height of 3000 m (the top height of the CFD domain) in the vertical direction of the WRF output were interpolated into 60 layers in a sparse-to-dense manner, while 60 grid point data closer to the boundaries of the CFD model were selected in both the east-west and north-south directions. Regardless of whether the grid point information was extracted from the horizontal or vertical profiles, the number of grid points in the profiles was 60 \(\times\) 60. Based on the statistical analysis of the post-processed data, Equation (13) was used to transform the grid point data in the profiles into greyscale pixel values through linear projection:  
\[I_{t}=f(G_{t}^{D})=255*\frac{G_{t}^{D}-G_{\text{min}}^{D}}{G_{\text{max}}^{D}-G _{\text{min}}^{D}}, \tag{13}\]  
where \(G_{t}^{D}\) represents all the speed or temperature grid point data in a profile at time \(t\); \(I_{t}\) represents all the transformed grayscale pixel values in the same profile at time \(t\); \(G_{max}^{D}\) and \(G_{min}^{D}\) respectively indicate the maximum and minimum values of the speed or temperature grid point data in the profile during the simulation period. Since the data transformation is linear, Equation (14) can also be used to project the predicted pixel values outputted by the hybrid network model back into the profile grid point data to drive the CFD module for prediction:  
\[G_{t}^{D}=f^{-1}(I_{t})=\frac{G_{\text{max}}^{D}-G_{\text{min}}^{D}}{255}*I_{t }+G_{\text{min}}^{D} \tag{14}\]  
In this experiment, the longitude component of the wind speed \(u\) and the temperature variable \(tk\) on the vertical profile of the WRF post-processed grid point data closest to the eastern inflow boundary of the CFD model were selected to generate two greyscale image datasets, which were used to evaluate the performance of the network model. The images generated after visualising the two datasets are shown in Figure 10a,b. Since the WRF model had a total of 2305 outputs for the d03 domain during the simulation period, both datasets contained 2305 greyscale images. The objective of the experiment was to predict the next five consecutive frames based on a continuous sequence of five previous input frame images. In other words, it aimed to use the past 50 min of WRF output variables to predict the next 50 min of WRF output  
Figure 10: Visualizations of the wind speed and temperature variables of the WRF output profile. (a) Longitude component of wind speed; (b) temperature. variables. Specifically, this means using the input frame sequence \(\{I_{t-4},I_{t-3},\cdots I_{t}\}\) to obtain the predicted frame sequence \(\{\hat{I}_{t+1},\hat{I}_{t+2},\cdots\hat{I}_{t+5}\}\) in an interval of 10 min. To reduce the amount of data processing for model learning, the experiment adopted the method of rolling prediction [33]. First, the predicted frame sequence \(\{\hat{I}_{t-3},\hat{I}_{t-2},\cdots\hat{I}_{t+1}\}\) was obtained using the input frame sequence \(\{I_{t-4},I_{t-3},\cdots I_{t}\}\). Then, the frames \(I_{t-4}\) were removed from the original input frame sequence \(\{I_{t-4},I_{t-3},\cdots I_{t}\}\), and the frame \(\hat{I}_{t+1}\) from the predicted frame sequence was appended to the end of the input frame sequence. This resulted in a newly constructed frame sequence \(\{I_{t-3},I_{t-2},I_{t-1},I_{t},\hat{I}_{t+1}\}\).