# Contrails and Their Dependence on Meteorological Situations
## 4 Discussion  
Several techniques using the selected parameters were tested to determine the most accurate classification of the contrail lifetime. The programmed classification models showed different classification accuracy, ranging from 73-97%. The Random Forests method showed the highest accuracy. The trained Random Forests-based classifier is able to perform the classification correctness of the final variable (contrail lifetime) at 97%, which means that based on the measured data related to contrails and meteorological parameters, the contrail lifetime can be classified very well into short-lived and long-lived with a resolution of 1 min. This classifier also has the highest precision value of 97% and sensitivity of 99%. The F1 score of 98% demonstrates the high quality of the classifier. This is also the best result considering the amount of input data used for the model, despite the incorrect classification of some values. Random Forests modeling for the finer subdivision of contrail lifetime by 0.5 min, 1 min, and 2 min also showed excellent results. Their accuracy was calculated at 93%, and the other performance characteristics were also successful. This classifier can be used very well to predict the average contrail lifetime, which is 122 s. Random Forests are a heuristic method that can handle a relatively small dataset. Their success lies in the fact that they purposely go inside the dataset and look for where the most information occurs. For data that are not rich in information, the method can be excluded so that it does not interfere. Practical applications of this method are the prediction of short-lived contrails up to 1 min, long-lived contrails over 1 min, and the prediction of the average contrail lifetime (almost 2 min). This finding is valid for the area of Decin and its surroundings up to 100 km. The second most successful model was the Discrete categorical model with a 5 min division of contrail lifetime. It had the second-highest quantified success rate. The classification accuracy was 90%, so it is able to classify the contrail lifetime with that resolution \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Measured y** & **Predicted y\({}_{\rm 1}\)** & **Predicted y\({}_{\rm 2}\)** & **Predicted y\({}_{\rm 3}\)** & **Predicted y\({}_{\rm 4}\)** & \(\Sigma\) \\ \hline \(y_{\rm 1}\) & 195 & 3 & 1 & 0 & 199 \\ \(y_{\rm 2}\) & 6 & 34 & 2 & 0 & 42 \\ \(y_{\rm 3}\) & 3 & 0 & 51 & 1 & 55 \\ \(y_{\rm 4}\) & 4 & 2 & 1 & 27 & 34 \\ \(\Sigma\) & 208 & 39 & 55 & 28 & 330 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Confusion matrix of outputs y and y\({}_{\rm p}\) for Random Forests with division into four intervals of contrail lifetime. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**S** & **P** & **R** & **F1** \\ \hline
0.9303 & 0.9253 & 0.8778 & 0.9009 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance characteristics of Random Forests (0.5, 1, and 2 min divisions). reasonably well. It also has a high precision and sensitivity of 99%. This is a very excellent result. The classification was successful despite the misidentification of some values. Its minor limitation is that it recognized only two long-lived contrails out of 33. Basically, it expresses the well-known problem of classification models with the recognition of values in an ensemble where there is a small sample of data. The practical use of this model is for predicting extremely long-lived contrails above 5 min, which have the largest impact on atmospheric warming. Throughout all model runs, it was shown that the higher the threshold setting in minutes for the separation of short-lived and long-lived contrails, the less all models correctly predicted the occurrence of long-lived contrails but increased their success in correctly classifying them. The contrail lifetime depends mainly on meteorological variables, which can vary considerably within a day.