Towards High Resolution Probabilistic Coastal Inundation Forecasting from Sparse Observations

The introduction of DIFF-SPARSE marks a significant advancement in coastal inundation forecasting, particularly in areas like the Eastern Shore of Virginia, where sparse sensor networks limit data availability. By leveraging historical inundation data and employing a masked conditional diffusion model, DIFF-SPARSE effectively addresses the challenges posed by missing observations, achieving up to 62% improvement in forecasting accuracy. This is particularly relevant as coastal flooding poses growing threats to communities globally, necessitating more reliable forecasting systems for emergency response. The model's innovative approach not only enhances prediction capabilities but also underscores the importance of integrating advanced methodologies in environmental monitoring and disaster preparedness.