论文标题
AA-TRANSUNET：关注型Transunet的注意力
AA-TransUNet: Attention Augmented TransUNet For Nowcasting Tasks
论文作者
论文摘要
基于数据驱动的建模方法最近在许多具有挑战性的气象应用中引起了很多关注，包括天气元素预测。本文介绍了一个基于Transunet的新型数据驱动的预测模型，以实现降水为现象任务。结合变压器和U-NET模型的Transunet模型以前已成功应用于医疗分割任务。在这里，Transunet被用作核心模型，并进一步配备了卷积块注意模块（CBAM）和深度不可分割的卷积（DSC）。在两个不同的数据集上评估了拟议的注意增强Transunet（AA-Transunet）模型：荷兰沉淀地图数据集和法国云封面数据集。获得的结果表明，所提出的模型在两个测试数据集上都优于其他检查的模型。此外，提供了对拟议的AA-Transunet的不确定性分析，以提供有关其预测的更多见解。
Data driven modeling based approaches have recently gained a lot of attention in many challenging meteorological applications including weather element forecasting. This paper introduces a novel data-driven predictive model based on TransUNet for precipitation nowcasting task. The TransUNet model which combines the Transformer and U-Net models has been previously successfully applied in medical segmentation tasks. Here, TransUNet is used as a core model and is further equipped with Convolutional Block Attention Modules (CBAM) and Depthwise-separable Convolution (DSC). The proposed Attention Augmented TransUNet (AA-TransUNet) model is evaluated on two distinct datasets: the Dutch precipitation map dataset and the French cloud cover dataset. The obtained results show that the proposed model outperforms other examined models on both tested datasets. Furthermore, the uncertainty analysis of the proposed AA-TransUNet is provided to give additional insights on its predictions.
