A CBAM-integrated deep convolutional neural network for weather radar echo correction to overcome terrain occlusion challenge

Key Points

• Echo correction improves significantly when using a deep convolutional neural network, showing promising results in addressing terrain occlusion.
• The model achieves notable performance metrics, with improvements shown in various terrain scenarios, ensuring better radar data accuracy.
• Using a CBAM-integrated architecture, this study employs advanced image processing techniques to enhance weather radar capabilities.
• Findings may enable better weather forecasting and disaster management through improved radar data interpretation and reliability.