Qin, Y., Cao, J., Wang, T., Yin, Y., Li, L., Xiang, S., Zhang, Y., & Zimmermann, R. (2026). SRDiff: A Cross-Modal Diffusion Model for Satellite-to-Radar Translation in Precipitation Nowcasting. IEEE Transactions on Geoscience and Remote Sensing, 64, 4106416. https://doi.org/10.1109/tgrs.2026.3686188
Abstract:
Satellite-to-radar translation is a critical yet underexplored task in modern weather forecasting. While satellites provide near-global coverage and high-frequency updates, their multichannel radiance data is difficult to interpret directly by forecasters or end users, and not readily compatible with existing radar-based forecasting models. By converting satellite observations into radar-equivalent representations, we can: 1) extend radar-like availability to regions without ground-based radar coverage; 2) provide a more compact and operationally meaningful representation of storms; and 3) enable the reuse of a large ecosystem of radar-based nowcasting models without retraining. Despite its importance, methods tailored for this task remain limited, and the performance of general-purpose generative models has not been systematically benchmarked. To fill this gap, we propose SRDiff, a cross-modal, sequence-aware diffusion model specifically designed for satellite-to-radar translation. SRDiff employs a cross-modal conditional adapter (CMCA) to align heterogeneous satellite channels with radar reflectivity, and a Diffusion Transformer (DiT) backbone to capture sequential dependencies, producing stable and coherent radar predictions. Extensive experiments on the SEVIR and Sat2Rdr datasets show that SRDiff outperforms existing deterministic and diffusion-based methods on the satellite-to-radar translation task across all key metrics. As a downstream application, we integrate SRDiff with off-the-shelf radar-based nowcasting models, demonstrating three key advantages: 1) operational efficiency, since no new training is required; 2) strong forecasting accuracy, often surpassing models trained from scratch; and 3) practical validation that effective satellite-to-radar translation directly improves downstream nowcasting.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the The National Research Foundation, Singapore and the National Environment Agency, Singapore - Weather Science Research Programme
Grant Reference no. : WSRP-2025-1R-03-03