## 3 Discussion
### Aerosol Classification
Synthetic datasets were used as input for the ANN, which were trained to classify aerosol types based on the lidar data. Various learning rules were tested, including momentum, conjugate gradient descent, step, and Levenberg-Marquardt. The study considered different spectral parameters, such as the extinction Angstrom exponent and the backscatter coefficient, to characterize the aerosols. These parameters were calculated on the basis of synthetic datasets. The results showed that the trained ANN achieved a classification accuracy of 75% for more than 80% of the measured data and 75% for more than 90% of the synthetic data. Different ANN architectures were tested, including the multilayer perceptron (MLP), Jordan/Elman networks, generalized feed forward network, self-organizing feature maps, and recurrent neural networks. The study also discussed the uncertainties associated with lidar measurements and synthetic data, emphasizing the importance of considering relative errors in optical parameters. It highlighted the need for a trade-off between the finesse of the output aerosol types and the computing time. In summary, the presented methodology demonstrated the use of artificial neural networks for classifying aerosol types based on lidar data, utilizing a developed aerosol model and synthetic datasets. The results showed promising classification accuracy, offering a potential tool for studying aerosol properties and their impact on the atmosphere. According to [PERSON] et al. [63], Doppler wind lidars have gained global recognition for their role in wind monitoring and, more recently, aerosol detection. Automatic classification of lidar signals obtained from lidar observations is a challenging task. In this study, the authors explored the capabilities of machine learning in classifying backscattered signals from Doppler wind lidars deployed at the international airport of Reykjavik and Keflavik in Iceland. Their approach involved integrating supervised and unsupervised machine learning algorithms with traditional lidar data processing methods. Through the training of two models, their objective was to effectively filter out noise signals and categorize Doppler lidar observations into distinct classes, including clouds, aerosols, and rain. To initiate the process, they employed machine learning techniques to classify lidar signals. The initial challenge consisted of constituting a dataset that labeled the lidar data to be used as training datasets by the supervised machine learning algorithm. To address this, they combined the unsupervised data clustering method DBSCAN with a conventional threshold-based approach and manual correction. This combined method was applied to classify four days of data obtained during two dust events in 2019. Subsequently, the labeled dataset was utilized to train two models: a noise discrimination model and a classification model using the RF algorithm. In their study, conducted in Iceland, traditional noise identification in lidar data, based on carrier-to-noise ratio (CNR) thresholds, was found to be suboptimal. In particular, there was not always a linear correlation between the data quality and CNR values, leading to inconsistencies in noise determination. In certain scenarios, CNR values can uncharacteristically spike, rendering typical CNR filtering ineffective. This can result in challenges distinguishing between high clouds and noise, as the cloud measurements' CNR values, especially for higher-altitude clouds, are usually lower than boundary layer signals. To address these issues, the authors introduced a new method of noise identification. After an initial CNR filtering, the DBSCAN clustering method was applied. This approach categorizes data points by their density, effectively distinguishing noise from non-noise, due to their significant density differences. This refined method successfully isolated clouds significantly distant from the boundary layer, labeling them as \"high clouds'.