  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.
