Synthetic Weather Radar

Synthetic weather radar research focuses on generating realistic radar data using computational methods to overcome limitations of real-world data acquisition, such as cost, sparsity, and safety concerns. Current efforts employ diverse approaches, including physics-based simulations, generative adversarial networks (GANs), and physics-informed neural networks (PINNs), often incorporating data fusion techniques to leverage multiple data sources. This work is crucial for advancing applications like autonomous driving (using radar object detection), improving wave prediction models, and enhancing wildlife monitoring through more complete spatiotemporal movement reconstructions, ultimately leading to more robust and reliable analyses across various domains.