## 3 Discussion
### Mixing Height Retrieval  
The work of [PERSON] et al. [64] focused on developing two machine learning algorithms, K-means for the atmospheric boundary layer (KABL) and AdaBoost for the atmospheric boundary layer (ADABL), to estimate the height of the mixing layer (MLH, [27; 65]) from aerosol lidar measurements. The algorithms were applied to a two-year dataset from the Meteo-France operational elastic lidar network, and their performance was compared to radiosonde measurements, considered as a reference. The KABL algorithm is a non-supervised algorithm based on the K-means clustering algorithm. It looks for a natural separation in the backscatter signals between the boundary layer and the free atmosphere. The main findings related to KABL were as follows:  
* KABL generally outperformed the manufacturer's algorithm, but its performance varied between sites. At the Trappes site, KABL performed well, while at the Brest site, the manufacturer's algorithm performed better;
* The diurnal cycle of the KABL estimates showed a behavior similar to the manufacturer's algorithm but consistently yielded estimates higher by approximately 200 m;
* KABL frequently misidentified the top of the boundary layer, often confusing it with other surface layers or clouds;
* KABL showed versatility by being less dependent on instrumental settings and calibrations, which makes it compatible with backscatter profiles from other instruments. ADABL is a supervised algorithm based on the AdaBoost algorithm. It fits a large number of decision trees into a labeled dataset and aggregates them intelligently to provide accurate predictions. The main findings related to ADABL were as follows:  
* ADABL generally outperformed both KABL and the manufacturer's algorithm at both sites, exhibiting higher correlation and lower error;
* ADABL generated the most pronounced diurnal cycle of the MLH estimates, with a pattern that closely resembled the expected diurnal cycle;
* ADABL's performance was highly dependent on the day it was trained on, with the sunset and sunrise times of those days having an over-influential effect on the estimation;
* ADABL showed promise but had training issues that needed to be addressed, such as the need for an enhanced training set with various meteorological conditions. The authors concluded that the ADABL algorithm showed a higher potential, with a root mean square error (RMSE) of 550 m with respect to the radiosonde at Trappes vs. 800 m for the manufacturer. On the other hand, the KABL algorithm exhibited a lower performance, with an RMSE of 800 m at Trappes, but it offered greater versatility compared to ADABL. The manufacturer's algorithm, which used a wavelet covariance transform, performed well with minimal tuning, but was not open source. Future developments for ADABL and KABL could include enhancing the training set for ADABL, enforcing time and altitude continuity in KABL estimation, and comparing both algorithms with high-temporal-resolution radiosonde measurements. Another study by [PERSON] et al. [66] addressed the importance of the planetary boundary layer height (PBLH, [25]) for air pollution research and also the limitations of current weather forecast models in accurately predicting PBLH, particularly during night conditions. By applying the CNN algorithm for edge detection and denoising autoencoders for preprocessing, the proposed approach was effective in retrieving PBLH from lidar observations with higher accuracy with respect to classic methodologies. The findings demonstrated that the deep learning method aligned well with radiosonde measurements and outperformed traditional techniques such as wavelet methods. Furthermore, the integration of machine learning-derived PBLH into weather models, specifically the Weather Research and Forecasting with Chemistry (WRF-Chem model), improved the overall model accuracy and provided a breakthrough in air quality forecasting.