## 2 Near-Term Rainfall Deep Learning Model
### Translator Encoder-Decoder Structure
Each input channel utilizes a separate convolution kernel, where each kernel has a channel size of 1, and the number of kernels equals the number of input channels. The kernel size is (5.5). Following each convolution, the features are concatenated along the channel dimension to obtain the output features. The primary advantages of small-kernel depthwise convolution include high computational efficiency, fewer parameters, and a significant reduction in the computational and parameter load of the convolution layers, while still effectively extracting spatial features from each channel. The depthwise dilated convolution network enhances the depthwise convolution by incorporating dilated convolution, which introduces \"holes\" (filled with zeros) between the kernel elements to expand the convolution kernel's receptive field. This approach effectively increases the receptive field without adding extra parameters, making it adept at capturing long-term dependencies. In this research, a dilation rate of 3 is employed, resulting in dilated convolution kernels of size (7.7) with padding of (9.9). The 1 \(\times\) 1 convolution (Conv1\(\times\)1) [29] is utilized to reduce the channel dimension of the data while preserving the feature map size, introducing non-linearity to the model, and bolstering inter-channel interaction. The intra-frame static attention network initially extracts local features from the input tensor using small-kernel depthwise convolution. It subsequently employs depthwise dilated convolution to broaden the receptive field, thereby capturing more contextual information and long-term dependencies. Finally, Conv1\(\times\)1 is employed to fuse the features extracted from both depthwise convolution and depthwise dilated convolution, resulting in the capture of more representative local features and essential spatial information. The intra-frame static attention network, by itself, is inadequate for effectively capturing the temporal dynamics along the time axis. To remedy this limitation, the inter-frame dynamic attention network employs AvgPool to extract comprehensive spatial information from each channel while simultaneously reducing the spatial dimensions of the feature map, thereby preserving the average value of each channel. Following this, FC is implemented to ascertain the significance weights of each channel. The resultant channel weights are then multiplied with the original input feature map to modulate the intensity of the features associated with each channel. This squeeze-and-excitation mechanism significantly enhances the network's capability to learn the temporal evolution of the input sequence, thereby augmenting its proficiency in capturing temporal correlation features. See Figure 4 for an illustration of the intra-frame static attention network structure. T rep-resents the sequence length of the input image, while C, W, and H indicate the number of channels,  
Figure 3: Schematic of deep convolution with small kernel. width, and height, respectively. Merging the T and C dimensions helps the model learn temporal correlations in the input sequence more effectively. The TAU can be mathematically represented as:  
\[ \begin{split} SA=Conv_{1\times 1}(DW\_DConv(DW\ Conv(H)))\\ DA=FC(Avgpool(H))\\ H^{\prime}=(SA\otimes DA)\odot H)\end{split}  \tag{1}\]  
In Equation (1) [21], H represents the hidden layer features input to the TAU module, SA denotes the output of the intra-frame static attention network, DA represents the inter-frame dynamic attention network, FC and AvgPool refer to the fully connected layer and average pooling operation, and \(\otimes\) and \(\odot\) represent the cross product and dot product. TAU uses intra-frame static attention networks to expand the convolutional receptive field obtained within frames, thereby acquiring more contextual information. This allows the model to better capture more representative local features and important spatial information.