KSR-Huber: A Robust Method for Wind Vector Retrieval from Doppler Wind Lidar Observations

Three-dimensional wind vector retrieval from Coherent Doppler Wind Lidar (CDWL) in Velocity–Azimuth Display (VAD) mode is susceptible to anomalous radial velocity observations induced by low signal-to-noise ratios, clutter echoes, and spectral estimation errors, which degrade inversion accuracy. To address this issue, a robust retrieval method, termed KSR-Huber, is proposed by integrating K-nearest-neighbor (KNN)-based local statistical priors with Huber iterative reweighted least squares (IRLS). The method employs KNN-based local consistency and adaptive Sigmoid weighting, together with Huber residual reweighting within the IRLS framework, to suppress anomalous observations while preserving valid data. Simulations across diverse scenarios, conducted under controlled numerical experiments with varying observation redundancies and outlier contamination levels, show that the proposed method consistently outperforms existing approaches, including DSWF, KNN-COOKS, and airSWF, particularly in terms of robustness under controlled noise and outlier conditions. Real lidar observations further demonstrate the practical applicability of the method, while comprehensive validation against independent reference measurements is left for future work.