A machine learning algorithm for planetary boundary layer height estimating by combining remote sensing data

Zhong xing Zhao; Song lin Fu; Jun jie Chen

doi:10.1117/12.3019615

30 April 2024 A machine learning algorithm for planetary boundary layer height estimating by combining remote sensing data

Zhong xing Zhao, Song lin Fu, Jun jie Chen

Author Affiliations +

Proceedings Volume 13157, Sixth Conference on Frontiers in Optical Imaging and Technology: Applications of Imaging Technologies; 131571A (2024) https://doi.org/10.1117/12.3019615
Event: Sixth Conference on Frontiers in Optical Imaging Technology and Applications (FOI2023), 2023, Nanjing, JS, China

Abstract

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.

Citation Download Citation

Zhong xing Zhao, Song lin Fu, and Jun jie Chen "A machine learning algorithm for planetary boundary layer height estimating by combining remote sensing data", Proc. SPIE 13157, Sixth Conference on Frontiers in Optical Imaging and Technology: Applications of Imaging Technologies, 131571A (30 April 2024); https://doi.org/10.1117/12.3019615

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KEYWORDS

Remote sensing

Machine learning

Education and training

Atmospheric modeling

LIDAR

Data modeling

Clouds

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