Lidar is an effective approach for detecting the Planetary Boundary Layer Height (PBLH). Traditional lidar algorithms are prone to interference and misjudgment under complex atmospheric conditions such as cloud layers and suspended aerosol layers. Some studies have proposed the combined use of lidar and thermodynamic remote sensing to retrieve PBLH, which has improved the accuracy of retrieval. However, fundamentally, traditional algorithms are still utilized, and the retrieval results are still susceptible to the influence of complex conditions. This paper proposes a machine learning based PBLH retrieval model that integrates lidar and thermodynamic remote sensing data as the training dataset to predict PBLH. Experimental results demonstrate that, compared to traditional and combined algorithms, the proposed method estimates PBLH close to the height measured by radiosonde with minimal error. It is evident that the proposed method can reliably retrieve PBLH with minimal susceptibility to external interference.
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