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

Cloud-layer identification is a key prerequisite for automated ground-based lidar processing, but remains challenging in Raman lidar networks because of incomplete near-range overlap, weak high-level cloud echoes, day-night signal-to-noise ratio (SNR) contrasts, energy fluctuations, and aerosol-cloud ambiguity. Leveraging the China Aerosol Raman Lidar Network (CARLNET), we propose a multi-channel cloud-layer identification framework that operates on 355/532/1064 nm elastic signals and 355/532 nm volume depolarization ratios, reducing dependence on absolute calibration and retrieved optical products. The framework combines an anisotropic encoder-decoder architecture with a three-stage training strategy, including self-supervised pretraining, traditional-algorithm-guided probabilistic distillation, and fine-tuning with limited expert refinement. Strict cross-site and cross-time evaluation on held-out sites shows that, without using any test-site observations during pretraining or test-site annotations during fine-tuning, the framework achieves an F1 score of 0.937. Compared with direct supervised training, the pretrained model yields comparable cloud-base accuracy (MAE: 59.1 versus 55.3 m) while reducing the detectable cloud-top MAE from 94.0 to 82.2 m. It also substantially outperforms the conventional VDE baseline in both cloud-base and cloud-top estimation. Independent evaluation based on a total of 47 matched lidar–radiosonde cases aggregated across the three held-out sites yields a structural consistency rate of 38.3 % with radiosonde-derived moist layers, compared with 34.0 % for VDE, providing an independent structural-consistency assessment across the two observing systems. These results demonstrate a cloud-layer identification technique that is transferable across held-out CARLNET sites and reduces dependence on absolute calibration, supporting network-scale Raman lidar processing.