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Wind vector retrieval algorithm for a coherent Doppler lidar based on KNN-COOKS.

The wind vector retrieval from a coherent Doppler lidar system in the plan position indicator (PPI) scanning mode often suffers from high inversion errors in horizontal wind speed and direction… Click to show full abstract

The wind vector retrieval from a coherent Doppler lidar system in the plan position indicator (PPI) scanning mode often suffers from high inversion errors in horizontal wind speed and direction at far-range gates due to "erroneous" radial wind speed. To address this, we propose a weighted sine wave fitting algorithm that combines K-nearest neighbors and Cook's distance (KNN-COOKS). Numerical simulation experiments show KNN-COOKS achieves higher accuracy than direct sine wave fitting (DSWF) and adaptive iterative reweighted sine wave fitting (AIR) and performs comparably to filtered sinusoidal wave fitting (FSWF). Validation with real-world data shows KNN-COOKS increases valid data by 22.5% and 12.5% over DSWF and AIR, respectively, while reducing computation time by 62% compared to FSWF and 38% compared to AIR.

Keywords: doppler lidar; wave fitting; vector retrieval; knn cooks; coherent doppler; wind vector

Journal Title: Applied optics
Year Published: 2025

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