## 3 Discussion
### Aerosol Classification  
[PERSON] et al. (2018) presented a study exploring the use of CNN to classify aerosol subtypes in lidar observations collected by the CALIOP instrument (Cloud-Aerosol Lidar with Orthogonal Polarization, ([PERSON] et al., 2018)) on the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations, ([PERSON] et al., 2018; [PERSON] et al., 2018)) satellite. The goal was to improve the accuracy of aerosol classification by leveraging the texture information inherent in the lidar measurements. The authors compared the performance of their CNN-based classification method with the existing CALIOP operational scene classification algorithms (COSCA). They evaluated the agreement between the CNN classifier and the COSCA in identifying aerosol subtypes, both in the troposphere and in the stratosphere. Using a three-month training set of measurements, the CNN classifier achieved a reasonable level of agreement with COSCA. In the troposphere, the CNN classifier identified the same aerosol subtypes as COSCA in approximately 62% of the cases, while in the stratosphere, the agreement was around 87%. An advantage of the CNN classifier was its ability to identify multiple subtypes of aerosols occurring within the same layer, while COSCA were limited to identifying homogeneous  
Figure 2: Comparison (**A**) CATS Attenuated Backscatter composite image (**B**) CATS cloud–aerosol discrimination (CAD) algorithm Implemented algorithm (**C**) Predicted cloud–aerosol discrimination (CAD) algorithm using CNN (**D**) Differences between (**B**) and (**C**) considering (**B**) as reference between CNN algorithm and operational CATS algorithm in detecting clouds and aerosols. CNN algorithm improved daytime detection. layers. In the cases where there was a mixture of different types of aerosols, the CNN method provided more accurate results. The study also highlighted some challenges and limitations of the classification process. The broad definitions of aerosol species used by COSCA and the reliance on specific discriminator parameters can introduce biases in classification. Furthermore, the paper discussed the difficulty in distinguishing between certain aerosol subtypes, such as polluted dust and smoke, due to their proximity to geographical locations and the lack of specificity in the class definitions. The performances of the implemented algorithm can be evaluated in [59], Figures 3-6 of [PERSON] article. The authors suggested future improvements, such as increasing CNN training sets with more accurate field campaign measurements and developing a CNN model of three-dimensional semantic segmentation that includes geographic information. These enhancements aim to further improve the accuracy of aerosol subtype classification, particularly in transition zones between major aerosol source regions, where mixing poses significant challenges. In summary, the paper demonstrated the potential of CNN and deep learning techniques to improve the classification of aerosol subtypes from lidar observations. Using texture information and overcoming the limitations of existing methods, the CNN classifier provides a more realistic representation of aerosol types within individual layers. The findings contributed to the advancement of aerosol characterization and have implications for various applications, including atmospheric modeling and climate studies. [PERSON] et al. [62] described a methodology for classifying aerosols based on lidar data using artificial neural networks (ANN). An aerosol model was developed to compute the optical properties of different types of aerosols. The model consists of six types of pure aerosol: continental, polluted continental, dust, marine, smoke, and volcanic. The model also included different mixtures of these types of aerosol. The aerosol model generated synthetic datasets of lidar measurements by varying the microphysical properties of the aerosols and their composition.