## 3 Discussion  
### Aerosol Layers Detection  
In their research article, [PERSON] et al. [55] explored the application of convolutional neural network algorithms for the detection of aerosol and cloud optical and geometric properties features from lidar remote sensing observations. In fact, lidar technology provides vertically resolved profiles of cloud and aerosol features, making it a valuable tool for atmospheric modeling and forecasting. The traditional approach to determining aerosol and cloud geometrical properties involves thresholding techniques, which can be subjective and arbitrary. To improve accuracy and reduce latency, the authors proposed the use of AI/machine learning-based algorithms for real-time detection of atmospheric features. The study focuses on the Cloud Physics Lidar (CPL, [56]) instrument flown on the NASA ER-2 high-altitude aircraft during the IMPACTS field campaign [57]. The CPL data, collected at a high resolution of 10 Hz, were processed in real-time using the CNN algorithm implemented on a Nvidia Jetson TX2 board. The CNN algorithm detected aerosol layers and clouds of the composite image of lidar profiles and output a binary image representing the presence or absence of features. The algorithm's performance was compared to the traditional processing method, and a confusion matrix analysis demonstrated a high level of agreement between the CNN-derived results and the traditional method. The results indicated that the real-time AI/ML-based approach provided a comparable performance to post-flight processing, even when operating on noisier and lower signal-to-noise ratio (SNR) data. The CNN algorithm enabled true real-time layer detection at native data resolution, significantly improving the availability of data products and increasing horizontal resolution. A confusion matrix was used to evaluate the performance of the AI/ML algorithm in a supervised learning context. This matrix contrasted the AI/ML-generated outcomes with a reference, which in this context was derived from the results produced using the conventional processing method (noting that even this method was not entirely accurate). On 22 February 2022, there was a strong alignment between the two methods, with more than 98% agreement for the \"no-layer\" classifications and 89% for the classifications containing layers. This confusion matrix is illustrated in Figure 3 of MCGill publication. It is crucial to highlight that the conventional technique processed 50 averaged profiles, which improved the signal-to-noise ratio (SNR), while the AI/ML approach analyzed individual profiles. This represents a limitation of the AI/ML method; processing a single profile can lead to misdetections of layers, due to a lower SNR. The authors highlighted the potential application of these techniques to spaceborne lidar data, where real-time data and products are crucial for forecasting models. They also discussed future research directions, including classification of detected layers into specific aerosol and cloud types. Overall, this study demonstrated the effectiveness of AI/ML techniques, specifically CNN algorithms, for real-time feature detection and height determination in lidar data. Further advances in real-time layer classification hold promise for enhancing the accuracy and utility of lidar-based atmospheric observations, especially from satellite platforms. [PERSON] et al. [58], in 2021, also explored the application of machine learning techniques, specifically CNN, to enhance cloud and aerosol detection in lidar observations obtained from the spaceborne Cloud-Aerosol Transport System (CATS [57]) instrument on the International Space Station (ISS). By employing a wavelet denoising technique during the day, the signal-to-noise ratio (SNR) of CATS lidar data was significantly improved, allowing for the detection of finer horizontal resolution layers.