This paper introduces Precipitation Attention-based U-Net (PAUNet), a deep
learning architecture for predicting precipitation from satellite radiance
data, addressing the challenges of the Weather4cast 2023 competition. PAUNet is
a variant of U-Net and Res-Net, designed to effectively capture the large-scale
contextual information of multi-band satellite images in visible, water vapor,
and infrared bands through encoder convolutional layers with center cropping
and attention mechanisms. We built upon the Focal Precipitation Loss including
an exponential component (e-FPL), which further enhanced the importance across
different precipitation categories, particularly medium and heavy rain. Trained
on a substantial dataset from various European regions, PAUNet demonstrates
notable accuracy with a higher Critical Success Index (CSI) score than the
baseline model in predicting rainfall over multiple time slots. PAUNet's
architecture and training methodology showcase improvements in precipitation
forecasting, crucial for sectors like emergency services and retail and supply
chain management.

这篇论文介绍了基于降水关注的 U-Net（PAUNet）的深度学习架构，用于根据卫星辐射数据预测降水，解决 Weather4cast 2023 比赛的挑战。PAUNet 是 U-Net 和 Res-Net 的一种变体，通过编码器卷积层的中心裁剪和注意机制，有效捕捉多波段卫星图像中的大尺度上下文信息，包括可见光、水汽和红外波段。通过基于各种欧洲地区的大规模数据集进行训练，PAUNet 在多个时间段的降雨预测中表现出了显著的准确性，具有比基准模型更高的临界成功指数（CSI）得分。PAUNet 的架构和训练方法对于降水预测的改进具有重要意义，对紧急救援、零售和供应链管理等领域至关重要。