Enhancing Spatial Variability Representation of Radar Nowcasting with Generative Adversarial Networks
This repo contains a PyTorch implementation of the Spatial Variability Representation Enhancement (SVRE) loss function and the Attentional Generative Adversarial Network (AGAN) for improving radar nowcasting.
Since the codes are based on Python, you need to install Python 3.8 first. The following dependencies are also needed.
numpy=1.20.3
netcdf4=1.5.7
pandas=1.4.3
matplotlib=3.5.1
cartopy=0.20.3
pyproj=3.3.1
pysteps=1.4.1
Run the bash scripts to train the model with the radar dataset.
- Ablation experiments for SVRE and AGAN
sh train_attn_unet.sh
sh train_attn_unet_svre.sh
sh train_agan.sh
sh train_agan_svre.sh
- Comparison experiments for baseline models
sh test_pysteps.sh
sh train_motion_rnn.sh
sh train_smaat_unet.sh
If you find this repo helpful, please cite the following article.
Gong, A.; Li, R.; Pan, B.; Chen, H.; Ni, G.; Chen, M. Enhancing Spatial Variability Representation of Radar Nowcasting with Generative Adversarial Networks. Remote Sens. 2023, 15, 3306. https://doi.org/10.3390/rs15133306