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Cloud fields and aerosol classification with lidar using advanced AI approach
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DOI：10.5194/amt-19-4415-2026.png)
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Abstract
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Abstract. Understanding the vertical distribution of aerosol and clouds i.s critical for climate modeling; weather forecasting; and air quality monitoring. Lidar observations are central to profiling atmospheric composition; yet signal attenuation in optically thick layers limits the effective retrieval of some important properties above those layers. More complex measurement approaches; using a combination of Lidar and cloud radar systems; can be taken to support more inclusive and accurate inference. In this study; we develop a deep learning framework to address this trade-off and gap in the cost of data acquisition by enabling full-column aerosol and cloud classification using only standard lidar inputs; achieving particularly high skill for aerosol typing while demonstrating robust; physically consistent classification of ice-cloud fields even under conditions of strong lidar signal attenuation; with liquid-cloud uncertainties primarily arising from closely related microphysical classes. The approach is based on a U-Net architecture trained to predict combined aerosol and cloud types from vertical profiles of backscatter and depolarization. Classification targets integrate established aerosol typing from PollyXT with cloud and precipitation categorization from Cloudnet; facilitating a unified scheme. The model achieves high precision; recall; and F1-scores above 95 %. By evaluating numerous complex case studies; we establish the model's ability to exploit information embedded in the lidar signal below attenuating layers; including structural and contextual features; to infer atmospheric conditions at higher altitudes; offering a robust AI-based enhancement to lidar-based atmospheric profiling and target classification. The application of AI in this context closes the gap between the need for vertical cloud maps and the sparse availability of Cloudnet.
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