## 4 Discussion and Suggestions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Location**} & \multirow{2}{*}{**Date**} & \multicolumn{4}{c}{**CNR of Noise Points (dB)**} \\ \cline{3-5}  & & **Mean** & **Minimum** & **Maximum** \\ \hline Reykjavik \({}^{\star}\) & 06/15/2019 & \(-34.11\) & \(-41.29\) & \(-10.75\) \\ \hline Reykjavik & 07/10/2019 & \(-33.88\) & \(-41.28\) & \(-12.82\) \\ \hline Reykjavik \({}^{\star}\) & 07/31/2019 & \(-32.40\) & \(-40.25\) & \(-10.75\) \\ \hline Keflavik & 07/31/2019 & \(-33.92\) & \(-49.79\) & \(-12.82\) \\ \hline \hline \end{tabular} \({}^{\star}\) Data used to train the model.  
\end{table} 
Table 3: CNR value for model classified noise data points. help to label the rain data. Another way forward is to explore the possibility of detecting solid precipitation in wintertime. For cloud classification, the method in this study is quite promising. However, for now, we only distinguish the low and high clouds based on different labeling methods. A possible improvement to be implemented would be to include more data collected in wintertime with low ice clouds and snow. Hopefully, the model can distinguish water clouds and ice clouds, and it could be more meaningful for meteorological monitoring and research. For the classification of aerosols, the major challenge is having a better understanding of lidar aerosols measurements. For example, the boundary between aerosol types II and III on 31 July is not entirely clear, and we do not know the exact physical difference between these aerosol types yet. Unlike type I, type II and III are aerosols considered sharing the same origin, which is the west of the Icelandic highlands. The main difference is due to the i) weather conditions and ii) aerosols concentration: type II had a lower concentration in a relatively dry atmosphere, so the \(\delta\) value was high but \(\beta\) value was lower, while type III had a higher concentration in a more humid atmosphere. These features are not necessary for aerosols classification, any particles with similar observations could be identified as these types of aerosols, and this can be checked in operations. For possible eruptions in Iceland, the two models can identify the signals from volcanic ash with similar properties as these three types of aerosols but may miss the ash particles that are different. In general, a larger data set with more precise labels is recommended for improving the performance of the lidar classification model. For the \"others\" class, which is considered as the observations when the backscattered lidar signal is strong enough (not noise), and no special phenomenon (clouds, rain, aerosols) was identified, i.e., the air should be relatively clean air within the boundary layer. As we mentioned above, the rain class was underestimated, which means some rain data were classified as others. Besides the rain, some low clouds are also difficult to distinguish and label, which means some low clouds may be mislabeled and classified as others. However, compared to the rain class, this mislabel issue is of little importance since the lidar signal is more sensitive to cloud than rain droplets. A better labeling data set could improve this. Neither model uses measurement time as the input feature; that is, the classification is not time-dependent. This means that these models can be deployed for real-time classification. This was examined by compiling a profile-by-profile classification (not shown). The features of the classification model are normalized because the lidar calibrations are different in June and July 2019. From an operational perspective, if a lidar has stable operations, the model can be re-trained without normalization. Otherwise, the newly measured data need to be normalized with the data measured 6-24 h before the event. Using the normalized data, the models will try to identify the different classes based on the relative values, which can minimize the impact of shifting between different instruments or calibration parameters.