Probabilistic Precipitation Nowcasting with Rectified Flow Transformers

Accurate weather forecasts are essential across various domains and are safety-critical in extreme weather conditions.Compared to simulation-based forecasting, data-driven approaches show greater efficiency, enabling short-term, high-resolution nowcasting.In particular, diffusion models proved effective in weather nowcasting due to their strong probabilistic foundation.However, existing methods rely on deterministic compression to reduce the complexity of high-dimensional weather data, limiting their ability to capture uncertainty in the decoding process.In this work, we introduce FREUD, a Frame-wise Encoder and United Decoder model based on rectified flow transformers for efficient compression of spatio-temporal weather data. Frame-wise encoding enables continuous forecast updates, while the unified video decoder ensures temporal consistency. Our uncertainty-preserving first stage allows us to capture aleatoric uncertainty through ensembling, which is particularly beneficial for extreme weather events with high decoding variability. We achieve state-of-the-art performance in precipitation nowcasting with a compact latent-space rectified flow transformer on the SEVIR benchmark and show further performance gains by scaling model size. With FREUD and the latent rectified flow model, we aim to push the boundaries of data-driven weather nowcasting.