Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 19, issue 20
Articles | Volume 19, issue 20
https://doi.org/10.5194/amt-19-6757-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-19-6757-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 20
Research article
 | 
27 Oct 2026
Research article |  | 27 Oct 2026

A self-supervised and distillation-guided framework for robust cloud-layer identification in multi-channel Raman lidar networks

Weijie Zou, Zhenping Yin, Tingxin Sun, Zhichao Bu, Yaru Dai, Haofei Wang, Longlong Wang, Yun He, Shuangliang Li, Jiarui Chen, Xuan Wang, Siwei Li, and Detlef Müller

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Short summary
Consistent cloud detection across lidar instruments and sites remains challenging. We develop a transferable artificial intelligence method using multi-wavelength lidar signals to improve cloud boundary detection with limited expert labels. Tests at independent sites and comparisons with weather balloon observations demonstrate its reliability. It will be deployed in the China Aerosol Raman Lidar Network (CARLNET) to support reliable automated cloud and aerosol processing.
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