WindWeaver: Along the Wind and Across Scales for Generative Weather Super-Resolution

Fine-resolution weather sequences support applications sensitive to local conditions and their evolution. Generative super-resolution must recover unresolved fine-scale states and connect their changes across gaps in coarse observations. Coarse wind offers approximate cross-time correspondence while leaving multivariate fine-scale evolution undetermined. We introduce WindWeaver, which uses atmospheric transport to structure the computation of generative updates. Coarse wind organizes cross-time feature exchange, while the network learns how the exchanged information contributes to multivariate reconstruction. Wind-derived coordinates retrieve features of the current candidate sequence at each flow evaluation. Separate readouts predict coarse block means and within-block detail, while a locally correlated source initializes unresolved variation. These interfaces support velocity-based conditional flow matching (CFM) and a task-adapted, denoiser-based adaptive flow matching (AFM) host. Across East Asian ERA5, same-product CERRA, and paired ERA5-to-CERRA reconstruction, WindWeaver improves probabilistic reconstruction of wind, temperature, cloud, and radiation in both hosts. Controlled CFM experiments establish net communication gains over branch removal in state and six-hour increment scores. Matched static and reversed correspondence show that the coordinate rule matters. Increment benefits extend across every reconstructed variable group. These results support using approximate atmospheric correspondence to guide generative feature exchange for reconstructing multivariate weather states and their temporal changes.