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.

For semantic segmentation tasks, there are problems in using multi-branch structures to enrich feature maps and aggregate different branches of feature maps in a certain network depth, such as the insufficient richness of feature maps and the incomplete aggregation of feature maps. Given multi-branch feature maps and branch aggregation, this study proposes a lightweight method, called multi-branch aggregation atrous spatial pyramidal pooling, by introducing an attention mechanism and CARAFE to enrich the features of the feature maps, giving the feature maps adaptive parameters, periodically adjusting the adaptive parameters, and aggregating the feature maps in both the vertical and horizontal directions. First, the atrous pyramid is retained and the attention mechanism and CARAFE are used to handle the pooling features to obtain 10 different feature maps. Secondly, giving each feature map a cascaded adaptive parameter and periodically adjusting the adaptive parameter to promote or suppress certain feature maps prevents the model from being at a local minimum or saddle point for long periods due to aleatory uncertainty. Finally, the feature maps are vertically aggregated, horizontally aggregated, and weighted for summation. This work demonstrates a competitive performance on the benchmark dataset, with an improvement of 1.88% in MPA, 0.66% in FWIoU, and 1.29% in MIoU compared to atrous spatial pyramid pooling. The benchmark datasets include PASCAL VOC 2012 and CIFAR-100.