The nowcasting of strong convective precipitation is highly demanded and presents significant challenges, as it offers meteorological services to diverse socio-economic sectors to prevent catastrophic weather events accompanied by strong convective precipitation from causing substantial economic losses and human casualties. With the accumulation of dual-polarization radar data, deep learning models based on data have been widely applied in the nowcasting of precipitation. Deep learning models exhibit certain limitations in the nowcasting approach: The evolutionary method is prone to accumulate errors throughout the iterative process (where multiple autoregressive models generate future motion fields and intensity residuals and then implicitly iterate to yield predictions), and the “regression to average” issue of autoregressive model leads to the “blurring” phenomenon. The evolution method’s generator is a two-stage model: In the initial stage, the generator employs the evolution method to generate the provisional forecasted data; in the subsequent stage, the generator reprocesses the provisional forecasted data. Although the evolution method’s generator is a generative adversarial network, the adversarial strategy adopted by this model ignores the significance of temporary prediction data. Therefore, this study proposes an Adversarial Autoregressive Network (AANet): Firstly, the forecasted data are generated via the two-stage generators (where FURENet directly produces the provisional forecasted data, and the Semantic Synthesis Model reprocesses the provisional forecasted data); Subsequently, structural similarity loss (SSIM loss) is utilized to mitigate the influence of the “regression to average” issue; Finally, the two-stage adversarial (Tadv) strategy is adopted to assist the two-stage generators to generate more realistic and highly similar generated data. It has been experimentally verified that AANet outperforms NowcastNet in the nowcasting of the next 1 h, with a reduction of 0.0763 in normalized error (NE), 0.377 in root mean square error (RMSE), and 4.2% in false alarm rate (FAR), as well as an enhancement of 1.45 in peak signal-to-noise ratio (PSNR), 0.0208 in SSIM, 5.78% in critical success index (CSI), 6.25% in probability of detection (POD), and 5.7% in F1.

With the advent of Generative Adversarial Networks (GANs), significant progress has been made in facial aging prediction. However, existing methods still face considerable challenges. Many studies estimate the ages of individuals in images based on their birth dates rather than visual cues, leading to discrepancies between the predicted ages and the actual appearance of facial aging. Moreover, these approaches often overlook racial consistency, resulting in models predominantly tailored to European populations, which limits their generalizability across different races. To address these issues, we propose a novel facial aging prediction framework that employs three independent encoders to model identity, texture features, and facial skeletal structure. We replace traditional convolutional networks with an attention mechanism-based backbone, integrating spatial and channel attention mechanisms to capture both spatial relationships and age-related feature importance. These attention-enhanced feature maps are then processed through a pyramid feature fusion architecture to facilitate multi-scale feature extraction. Our model effectively captures the subtleties of facial aging across different demographics. Extensive experiments and ablation studies demonstrate that our approach excels in preserving identity, ensuring racial consistency, and generating realistic aging effects. The results further highlight the superior ability of the attention mechanisms to extract detailed, localized features essential for facial aging prediction.