Use of Deep Learning for Weather Radar Nowcasting
In one sentence. Residual convolutional encoder-decoder models for radar echo nowcasting match or beat operational systems and transfer across regions without performance decay.
Abstract
Abstract Nowcasting based on weather radar uses the current and past observations to make estimations of future radar echoes. There are many types of operationally deployed nowcasting systems, but none of them are currently based on deep learning, despite it being an active area of research in the last few years. This paper explores deep learning models as alternatives to current methods by proposing different architectures and comparing them against some operational nowcasting systems. The methods proposed here, harnessing residual convolutional encoder-decoder architectures, reach a level of performance expected of current systems and in certain scenarios can even outperform them. Finally, some of the potential drawbacks of using deep learning are analyzed. No decay in the performance on a different geographical area from where the models were trained was found. No edge or checkerboard artifact, common in convolutional operations, was found that affects the nowcasting metrics.
Labels
- Topic
- Nowcasting
- Type
- New model or method
- Method
- CNN / U-Net
- Time scale
- 0–6 h
- Domain
- Regional
- Data
- Radar
- Status
- Research
- Identifiers
- DOI:10.1175/jtech-d-21-0012.1 OpenAlex:W3180010583
- Added
- 2026-10-22
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