Coarse-to-fine Physics-guided Mixture-of-experts for Urban Wind Field Reconstruction from a Single Radar Column

Accurate low-altitude urban wind fields are essential for UAV safety, pollutant dispersion assessment, and wind analysis around buildings. However, reconstructing high-resolution 3D urban wind fields from a single vertical radar column is highly challenging due to extremely sparse observations. Existing methods often rely on distributed sensors or multiple LiDARs, while direct sparse-to-dense mapping entangles large-scale wind field completion with fine-scale structure recovery, and shared representations struggle to capture diverse local flow patterns. To address these challenges, we propose a coarse-to-fine urban wind field reconstruction framework that decomposes the task into low-resolution wind field completion and multi-stage super-resolution reconstruction, progressively bridging the large spatial gap between sparse observations and dense targets. We further introduce a physics-guided mixture-of-experts module with different physical priors that encourages different experts to specialize in distinct local flow patterns, including general air, building boundaries, vortices, highspeed regions, and ordinary areas. To mitigate the mismatch between ideal and predicted intermediate states during cascaded reconstruction, we develop a momentum-energy-guided state selection mechanism that prioritizes representative predicted states according to their momentum and kineticenergy discrepancies. Our experiments achieve 0.1408 MAE and 0.0404 MSE, reducing these errors by 46.00% and 70.19% over the state-of-theart method, respectively, demonstrating the effectiveness of our proposed method.Code is available at https://anonymous.4open.science/r/windreconstruction-anonymous-4C75/