Precipitation Datasets

Precipitation datasets are crucial for understanding weather patterns and predicting extreme events like floods, but existing datasets often lack the resolution or length of record needed for accurate risk assessment. Current research focuses on improving dataset quality through advanced techniques like generative adversarial networks (GANs) and ensemble learning methods incorporating quantile regression and other distributional regression algorithms to enhance spatial resolution, accuracy, and uncertainty quantification. These improvements are achieved by merging satellite and ground-based data, employing sophisticated downscaling methods, and leveraging deep learning architectures such as U-Net and diffusion models. The resulting higher-quality datasets are vital for improving weather forecasting, hydrological modeling, and disaster preparedness.