Deep Learning for Precipitation Nowcasting

Yifan Tao

doi:10.54691/b8ex5b27

Authors

Yifan Tao Anhui Wenda University Of Information Engineering, Hefei, China

DOI:

https://doi.org/10.54691/b8ex5b27

Keywords:

Precipitation Nowcasting; Deep Learning; Convolutional LSTM; Generative Adversarial Networks.

Abstract

This paper provides a comprehensive review of deep learning approaches for precipitation nowcasting, a challenging spatiotemporal prediction task in meteorology. We systematically categorize existing methods into four major families: recurrent neural network (RNN)-based models, convolutional encoder-decoder architectures, generative adversarial networks (GANs), and transformer-based models. For each category, we analyze its underlying modeling principles, representative architectures, strengths, and limitations. Furthermore, we summarize widely used radar datasets and evaluation metrics to provide a unified benchmark perspective. Finally, we discuss key challenges such as prediction blurriness, class imbalance in extreme precipitation events, and computational inefficiency, and outline promising future directions including multimodal fusion, efficient attention mechanisms, and self-supervised pretraining. This review aims to provide a structured reference for both researchers and practitioners in the field of data-driven weather forecasting.

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References

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