Precipitation Forecasting Using Transformer: A Comparative Study with Unet
In one sentence. Compares U-Net and Transformer for precipitation prediction from cloud-layer information; U-Net reaches 96.89% accuracy versus 94.13% for the Transformer.
Abstract
Accurate precipitation prediction has a huge socio-economic impact. With the development of algorithms, architectures, and hardware technologies in the field of machine learning, the disadvantages of traditional numerical weather prediction methods are becoming more and more obvious. Noting that related studies have attempted to use the latest machine learning architectures for precipitation prediction, this work uses Unet and Transformer for precipitation prediction based on cloud layer information and compares them. The results indicate that Transformer can achieve 94.13% accuracy, while Unet has 96.89%. Finally, in conducting the data comparison, a conjecture related to the lagging of Transformer data is proposed based on the results of this experiment.
Labels
- Topic
- Nowcasting
- Type
- New model or method
- Method
- Transformer / ViT, CNN / U-Net
- Time scale
- 0–6 h
- Data
- Satellite
- Status
- Research
- Identifiers
- DOI:10.54097/hset.v39i.6613 OpenAlex:W4362670437
- Added
- 2026-10-22
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