## 6 Discussion of the Results  
### Classification  
The final classification results on the test dataset are listed in Table 3. As expected, the ViT models show, again, superior performance compared to the ResNet3D-PREC and Conv-LSTM-PREC, with the ViT-B-PREC and ViT-S-PREC models both achieving up to 84.41% top 1 accuracy and up to 96.10% top 2 accuracy (meaning that the target class is within the two most probable predicted classes). Interestingly enough, however, ViT-S-PREC is significantly smaller, with circa 29% fewer parameters, but on the other hand conducting more MAC operations due to the missing dropout (see Section 5.1), which leads to a design trade-off between memory requirements and computing power. It is worth mentioning that the corresponding small model versions show quite a similar performance with a model size of just 190 k and 320 k parameters. Opposed to the ViT models, however, the ResNet-PREC and Conv-LSTM-PREC show better performance on the test data compared to the initial validation results, which is due to the fact that the models improved quite remarkably over the 200 training epochs compared to the 30-epoch training during model validation. Also, the small model versions are performing significantly better, with Conv-LSTM-PREC-s reaching up to 74.04% top 1 accuracy and ResNet-PREC-s achieving 77.92% top 1 accuracy, which might indicate some remaining degree of overfitting in the case of the larger model versions. The general superiority of the transformer-based model is not surprising and was also demonstrated and explained by earlier works [43]. Thus, by incorporating the self \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Top 1** & **Top 2** & **F1 Score** & **\# Params** & **MACs** \\ \hline \hline
3D Convolutional Networks & & & & & \\ \hline ResNet3D-PREC & 74.02\% & 88.31\% & 0.69 & 1.41 M & 368,50 G \\ ResNet3D-PREC-s & 77.92\% & **96.10**\% & 0.73 & 0.17 M & 37.71 G \\ \hline Convolution-LSTMs & & & & & \\ \hline Conv-LSTM-PREC & 64.93\% & 92.21\% & 0.67 & 0.73 M & 3.02 G \\ Conv-LSTM-PREC-s & 74.04\% & 85.71\% & 0.74 & 0.21 M & 1.32 G \\ \hline \hline Vision Transformers & & & & & \\ \hline ViT-B-PREC & **84.41**\% & **96.10**\% & 0.85 & 1.19 M & 0.22 G \\ ViT-B-PREC-s & 79.22\% & 93.51\% & 0.78 & 0.19 M & 1.50 G \\ ViT-S-PREC & **84.41**\% & **96.10**\% & 0.85 & 0.85 M & 1.86 G \\ ViT-S-PREC-s & 79.22\% & **96.10**\% & 0.83 & 0.32 M & 0.63 G \\ \hline \hline Convolutional Transformers & & & & & \\ \hline CCT-PREC & 83.12\% & **96.10**\% & 0.84 & 0.42 M & 16.63 G \\ CCT-PREC-s & 75.32\% & 93.51\% & 0.76 & 0.27 M & 1.97 G \\ \hline \hline Video Vision Transformers & & & & & \\ \hline ViViT-PREC & 79.22\% & 93.51\% & 0.80 & 2.62 M & 0.46 G \\ ViViT-PREC-s & 75.32\% & 92.21\% & 0.75 & 0.81 M & 0.25 G \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of model sizes and respective classification performance on the test dataset. The best performance in terms of top 1 and top 2 accuracy is marked in bold, whereas the correspondingly required computational resources are indicated by the number of model parameters (# Params) and MAC operations. attention mechanism (see Section 5.1), Vision Transformers have the ability to learn a global, spatiotemporal context over the whole time series, whereas 3D convolutions and LSTMs rely on a rather local, temporal context. As mentioned earlier, the intensity of the detected LiDAR noise can, however, fluctuate quite strongly in between single frames, which makes a broad temporal context crucial. This observation is further backed by evaluating the discriminatory power of the models with respect to specific precipitation classes. For this purpose, the normalized top 1 and top 2 confusion matrices of each model were consulted and, in case of the best performing ViT models, are illustrated in Figures 13 and 14. Similar to the discussed related works, the predicted precipitation classes (Class ID pred) are evaluated with respect to the actual, measured value (Class ID meas).