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Axial-UNet: A Neural Weather Model for Precipitation Nowcasting

arXiv 2504.19408 ↗First posted 28 Apr 2025cs.LG cs.CV eess.SPcited by 0

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Abstract (arXiv)

Accurately predicting short-term precipitation is critical for weather-sensitive applications such as disaster management, aviation, and urban planning. Traditional numerical weather prediction can be computationally intensive at high resolution and short lead times. In this work, we propose a lightweight UNet-based encoder-decoder augmented with axial-attention blocks that attend along image rows and columns to capture long-range spatial interactions, while temporal context is provided by conditioning on multiple past radar frames. Our hybrid architecture captures both local and long-range spatio-temporal dependencies from radar image sequences, enabling fixed lead-time precipitation nowcasting with modest compute. Experimental results on a preprocessed subset of the HKO-7 radar dataset demonstrate that our model outperforms ConvLSTM, pix2pix-style cGANs, and a plain UNet in pixel-fidelity metrics, reaching PSNR 47.67 and SSIM 0.9943. We report PSNR/SSIM here; extending evaluation to meteorology-oriented skill measures (e.g., CSI/FSS) is left to future work. The approach is simple, scalable, and effective for resource-constrained, real-time forecasting scenarios.

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References · 20

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8 more
  1. 2021An Introduction to Deep Generative Modelingcited by 104
  2. 2019Understanding LSTM -- a tutorial into Long Short-Term Memory Recurrent Neural Networkscited by 103
  3. 2020SmaAt-UNet: Precipitation Nowcasting using a Small Attention-UNet Architecturecited by 71
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  6. 2019Distributed Deep Learning for Precipitation Nowcastingcited by 6
  7. 2021Encoder-Decoder Neural Architecture Optimization for Keyword Spottingcited by 2
  8. 2018Precipitation Nowcasting: Leveraging bidirectional LSTM and 1D CNNcited by 1

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