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Joint multiscale cloud detection algorithm for ground-based lidar.

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    A new joint multiscale algorithm improves cloud detection from ground-based lidar data. This method enhances the identification of tenuous cloud layers and edges, crucial for climate research.

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    Area of Science:

    • Atmospheric Science
    • Remote Sensing
    • Cloud Physics

    Background:

    • Ground-based lidar is vital for analyzing cloud vertical structure and optical properties.
    • Existing cloud detection algorithms struggle with identifying tenuous cloud layers and edges, leading to significant detection errors.

    Purpose of the Study:

    • To develop and validate a novel joint multiscale cloud layer detection algorithm for ground-based lidar.
    • To improve the accuracy and completeness of cloud layer detection, especially for challenging cloud formations.

    Main Methods:

    • The proposed algorithm utilizes joint multiscale detection techniques.
    • It incorporates a trend function and the Bernoulli distribution assumption to identify cloud layers.
    • Depolarization properties are used to validate the reliability of detected cloud layers.

    Main Results:

    • The joint multiscale algorithm detected 10.45% more cloud layers compared to the Micro Pulse Lidar Network (MPLNET) official product.
    • Daytime and nighttime detection rates increased by 7.93% and 12.57%, respectively.
    • Validation using depolarization properties confirmed the reliability of the additionally detected cloud layers.

    Conclusions:

    • The joint multiscale algorithm significantly enhances the capability of ground-based lidar for cloud detection.
    • This improved detection of tenuous cloud layers and edges has critical implications for cloud climatology and climate change studies.
    • The algorithm shows potential for widespread adoption within the atmospheric science community.