## 4 Conclusions  
This paper presents a novel model called STD-TranslatorNet, which is based on the translator encoder-decoder conditional generative adversarial network. The model aims to tackle the challenge of spatiotemporal prediction for radar echo images, specifically applied to the task of imminent rainfall forecasting. Its effectiveness and versatility were validated through comparisons with eight standard models on benchmark datasets such as  
Figure 17: POD metric line chart and box plot of 10-frame prediction images for translator replacement experiment at 30 dBZ: (**a**) POD metric line chart of 10-frame prediction images; (**b**) POD metric box plot of 10-frame prediction images. Figure 18: FAR metric line chart and box plot of 10-frame prediction images for translator replacement experiment at 30 dBZ: (**a**) FAR metric line chart of 10-frame prediction images; (**b**) FAR metric box plot of 10-frame prediction images. SRAD and SEVIR. Furthermore, replacement experiments featuring various representative modules highlighted the superiority of the TAU as an intermediate connection unit. The primary research findings include:  
(1) The enhanced model incorporates dual spatial and temporal discriminators, along with a temporal attention unit, proposing a translator encoder-decoder structure. This significantly boosts the model's spatiotemporal learning capabilities, outperforming the second-best model by 6.9% and 7.6% in CSI and POD metrics, respectively. It also effectively retains various echo intensity regions during long sequence predictions, demonstrating superior performance in long-term forecasting compared to other models. This advancement addresses the challenges associated with capturing the temporal correlations of radar echoes and predicting high-intensity areas over extended time series. (2) When compared to traditional prediction methods such as optical flow, our model's radar echo predictions align more closely with actual radar echo movement trends. Replacement comparisons with other representative translator modules underscore the unique advantages of the TAU in capturing temporal dependencies and spatiotemporal changes. (3) Utilizing the CGAN network as the primary architecture effectively mitigated issues related to blurry image boundaries and mode collapse. The design of dual spatial and temporal discriminators better constrains the generator's output, allowing for the retention of more spatiotemporal information from radar echoes. This structure brings the predicted images' structural similarity closer to that of real images, achieving the highest SSIM value in comparative experiments with other spatiotemporal prediction models. Notwithstanding its advantages, the model exhibits several limitations that warrant further investigation, which will be the focus of subsequent research endeavors. Firstly, the imminent rainfall forecasting method demonstrates suboptimal performance on the FAR metric when compared to its efficacy on the CSI and POD metrics. Future research should prioritize enhancing the model's accuracy in predicting small echo regions while concurrently improving its FAR metric. Furthermore, the model's performance on the PSNR metric is relatively inadequate, indicating difficulties in effectively reconstructing high-frequency components, such as edges and textures, within the images. Investigating the integration of attention mechanisms may further augment the model's capacity to capture and reconstruct high-frequency information, representing a promising avenue for future research. Conceptualization, X.M. and X.Z. ; methodology, X.M. and X.Z. ; software, Y.H. and W.L. ; validation, Y.H. and W.L. ; formal analysis, X.M. and X.Z. ; investigation, Y.H. and W.L. ; resources, X.M. and X.Z. ; data curation, Y.H. ; writing--original draft preparation, Y.H. ; writing--review and editing, X.M. ; visualization, Y.H. ; supervision, X.M. ; project administration, X.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding.