## 3 Experiment Verification
### Comparative Experiments and Result Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**FAR Metric for Each Frame at 30 dBZ**} \\ \hline
**Model** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **Avg** \\ \hline STD-TranslatorNet & 0.20 & 0.24 & 0.30 & 0.35 & 0.37 & 0.41 & 0.45 & 0.47 & 0.48 & 0.52 & 0.379 \\ ConvLSTM & **0.13** & **0.15** & **0.19** & **0.21** & **0.24** & **0.29** & **0.37** & 0.43 & 0.50 & 0.58 & **0.309** \\ PredRNN & 0.15 & 0.19 & 0.24 & 0.27 & 0.30 & 0.34 & 0.40 & 0.43 & 0.49 & 0.53 & 0.334 \\ PredRNN++ & 0.15 & 0.18 & 0.23 & 0.26 & 0.29 & 0.34 & 0.40 & 0.42 & 0.46 & 0.50 & 0.323 \\ PredRNNV2 & 0.17 & 0.22 & 0.27 & 0.32 & 0.36 & 0.40 & 0.46 & 0.48 & 0.53 & 0.57 & 0.378 \\ MIN & 0.15 & 0.20 & 0.25 & 0.29 & 0.31 & 0.34 & 0.37 & 0.38 & **0.41** & **0.44** & 0.314 \\ ESDLSTM & 0.16 & 0.21 & 0.26 & 0.30 & 0.32 & 0.36 & 0.40 & 0.42 & 0.45 & 0.47 & 0.335 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of FAR of 10 images predicted by different models under 30 dBZ. The STD-TranslatorNet model demonstrates exceptional performance in accurately predicting strong echo regions over extended time sequences. Visual samples in Figure 13 clearly show that the model's predictions of strong echo intensity regions outperform those of other models. When compared with the true images in the first row, the STD-TranslatorNet model effectively preserves the features of strong echo areas in its predictions. Notably, as the prediction sequence lengthens, these observations become more pronounced. This trend is also supported by the data in Tables 3 and 4, where the decrease in CSI and POD metrics with increasing time sequence frames is less pronounced for the STD-TranslatorNet model compared to other models. This highlights the model's ability to maintain strong predictive performance even in long sequence predictions. When comparing traditional prediction methods like optical flow, it is evident from Figure 12 that images predicted using optical flow suffer from significant distortions. In particular, the anticipated movements of the cloud layer do not correspond with the actual trends observed in radar echo images. Moreover, there are considerable inaccuracies in predicting areas of radar echo intensity, with instances where low-intensity (blue) regions are mistakenly identified as high-intensity (red) areas. In contrast, our model's predictions generally align well with the actual trends in radar echo movement and accurately represent the true conditions of echo intensity. However, relative to optical flow, our model's predictions do exhibit some loss of detail in radar echo patterns, resulting in a somewhat blurrier depiction of echoes. An evaluation of the false alarm rate (FAR) metric reveals that the STD-TranslatorNet model exhibits inferior performance relative to other models, particularly in the initial  
Figure 13: Standard spatiotemporal prediction model temporal prediction local zoom visualization example. frames, where a notable increase in the FAR metric is observed. However, the increase in FAR for later frames is much smaller. Upon examining the visual example in Figure 12, it becomes apparent that the STD-TranslatorNet model retains some minor echo information in the initial frames, resulting in more diverse predicted radar echo areas. It is hypothesized that during the feature extraction phase, the STD-TranslatorNet model may excessively process the input of the initial frames, preserving too many minor features, which ultimately leads to a higher FAR metric. The image quality prediction metrics MSE, MAE, SSIM, and PSNR indicate that the SSIM of the STD-TranslatorNet model outperforms that of other models. This suggests that the STD-TranslatorNet model can reconstruct pixel values more accurately in spatiotemporal prediction tasks. The model effectively preserves structural information during prediction, striking a better balance between noise suppression and detail retention.