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Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
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Wind vector retrieval algorithm for a coherent Doppler lidar based on KNN-COOKS.

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    A new algorithm improves wind vector retrieval accuracy from Doppler lidar systems. The KNN-COOKS method reduces errors in horizontal wind speed and direction, especially at far ranges.

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    Area of Science:

    • Atmospheric science
    • Remote sensing technology
    • Signal processing

    Background:

    • Coherent Doppler lidar systems are crucial for atmospheric wind measurements.
    • Plan Position Indicator (PPI) scanning can lead to significant wind vector errors at far ranges.
    • Radial wind speed inaccuracies are a primary cause of these errors.

    Purpose of the Study:

    • To develop a robust algorithm for accurate wind vector retrieval from Doppler lidar data.
    • To mitigate inversion errors in horizontal wind speed and direction, particularly in far-range gates.
    • To improve the reliability and efficiency of wind measurements in atmospheric remote sensing.

    Main Methods:

    • A novel weighted sine wave fitting algorithm, K-nearest neighbors and Cook's distance (KNN-COOKS), was developed.
    • The algorithm integrates K-nearest neighbors for data point identification and Cook's distance for outlier detection.
    • Performance was evaluated through numerical simulations and validation with real-world Doppler lidar data.

    Main Results:

    • KNN-COOKS demonstrated higher accuracy in wind vector retrieval compared to Direct Sine Wave Fitting (DSWF) and Adaptive Iterative Reweighted Sine Wave Fitting (AIR).
    • The algorithm's performance was comparable to Filtered Sinusoidal Wave Fitting (FSWF).
    • Real-world data validation showed a 22.5% increase in valid data over DSWF and 12.5% over AIR, with reduced computation time.

    Conclusions:

    • The KNN-COOKS algorithm effectively reduces wind vector inversion errors in Doppler lidar, enhancing data quality.
    • It offers a more accurate and computationally efficient alternative to existing methods for atmospheric wind measurement.
    • This advancement improves the utility of Doppler lidar for meteorological and environmental monitoring applications.