通过通道混洗提升降水预报精度，降低模型复杂度。
SSA-UNet: Advanced Precipitation Nowcasting via Channel Shuffling
- 引入通道混洗机制优化UNet结构，提升特征表达能力。
- 在荷比卢和法国雷达数据上实现1~12小时的精准降水预测。
- 用Grad-CAM可视化关键区域，增强模型可解释性。
天气预报对社会经济活动与环境保护至关重要。深度学习正作为数值天气预报（NWP）的补充方法被广泛研究，具有降低复杂度、增强特定应用适应性的潜力。本文提出一种新架构Small Shuffled Attention UNet（SSA-UNet），在SmaAt-UNet基础上引入通道混洗机制，以优化性能并减少计算复杂度。该模型在两个数据集上进行评估：2016至2019年荷兰降水数据集，以及2017至2018年法国云盖雷达数据集。模型分别生成1、6、12小时的降水预测图。通过梯度加权类激活映射（Grad-CAM）分析输出热图，识别出输入中影响预测的关键区域。代码已开源于GitHub。
原文摘要 · Abstract (English)
Weather forecasting is essential for facilitating diverse socio-economic activity and environmental conservation initiatives. Deep learning techniques are increasingly being explored as complementary approaches to Numerical Weather Prediction (NWP) models, offering potential benefits such as reduced complexity and enhanced adaptability in specific applications. This work presents a novel design, Small Shuffled Attention UNet (SSA-UNet), which enhances SmaAt-UNet's architecture by including a shuffle channeling mechanism to optimize performance and diminish complexity. To assess its efficacy, this architecture and its reduced variant are examined and trained on two datasets: a Dutch precipitation dataset from 2016 to 2019, and a French cloud cover dataset containing radar images from 2017 to 2018. Three output configurations of the proposed architecture are evaluated, yielding outputs of 1, 6, and 12 precipitation maps, respectively. To better understand how this model operates and produces its predictions, a gradient-based approach called Grad-CAM is used to analyze the outputs generated. The analysis of heatmaps generated by Grad-CAM facilitated the identification of regions within the input maps that the model considers most informative for generating its predictions. The implementation of SSA-UNet can be found on our Github\footnote{\href{https://github.com/MarcoTurzi/SSA-UNet}{https://github.com/MarcoTurzi/SSA-UNet}}
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