PAUNet: Precipitation Attention-based U-Net for rain prediction from
  satellite radiance data
P. Jyoteeshkumar Reddy Harish Baki Sandeep Chinta Richard Matear John Taylor
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.