Assessment of Deep Learning-Based Nowcasting Using Weather Radar in South Korea
In one sentence. Compares RainNet, ConvLSTM U-Net, recursive U-Net and GAN nowcasting on South Korean S-band radar, with RainNet reaching CSI 0.826 at 0.1 mm/h.
Abstract
This study examines the effectiveness of various deep learning algorithms in nowcasting using weather radar data from South Korea. Herein, the algorithms examined include RainNet, ConvLSTM2D U-Net, a U-Net-based recursive model, and a generative adversarial network. Moreover, this study used S-band radar data from the Ministry of Environment to assess the predictive performance of these models. Results show the efficacy of these algorithms in short-term rainfall prediction. Specifically, for a threshold of 0.1 mm/h, the recursive RainNet model achieved a critical success index (CSI) of 0.826, an F1 score of 0.781, and a mean absolute error (MAE) of 0.378. However, for a higher threshold of 5 mm/h, the model achieved an average CSI of 0.498, an F1 score of 0.577, and a MAE of 0.307. Furthermore, some models exhibited spatial smoothing issues with increasing rainfall-prediction times. The findings of this research hold promise for applications of societal importance, especially for preventing disasters due to extreme weather events.
Labels
- Topic
- Nowcasting
- Type
- Evaluation or intercomparison of existing models
- Method
- CNN / U-Net, RNN / LSTM / ConvLSTM, GAN or VAE
- Time scale
- 0–6 h
- Domain
- Regional
- Data
- Radar
- Status
- Research
- Identifiers
- DOI:10.3390/rs15215197 OpenAlex:W4388077113
- Added
- 2026-10-22
Labels and summary are generated by Claude and reviewed weekly. Wrong? Edit this entry.