PreDiff: Precipitation Nowcasting with Latent Diffusion Models

Earth system forecasting has traditionally relied on complex physical models that are computationally expensive and require significant domain expertise.In the past decade, the unprecedented increase in spatiotemporal Earth observation data has enabled data-driven forecasting models using deep learning techniques.These models have shown promise for diverse Earth system forecasting tasks but either struggle with handling uncertainty or neglect domain-specific prior knowledge, resulting in averaging possible futures to blurred forecasts or generating physically implausible predictions.To address these limitations, we propose a two-stage pipeline for probabilistic spatiotemporal forecasting: 1) We develop, a conditional latent diffusion model capable of probabilistic forecasts.