## 5 Convolutional Neural Network Architecture  
This section describes the CNN architecture and the metrics used to evaluate the performance of the different approaches, both SAMESNR and DIFFSNR. The architectures for the CNN implementation for the time-frequency input and the 1D time domain input are shown in Figure 4. An architecture with 3 convolutional layers was identified to be the optimal architecture across the time-frequency representations  
Figure 3: Visualization of the signals in the time domain (first column) and the corresponding wavelet transforms: CWT-G (second column), CWT-D (third column), CWT-S (fourth column), and CWT-M (fifth column). First row (**a**) TDL-A, second row (**b**) TDL-B, third row (**c**) TDL-C, fourth row (**d**) TDL-D, fifth row (**e**) TDL-E, sixth row (**f**) TDL-F.  
and 1D representation as discussed in Section 7. The 1D time-domain representation was also directly used as an input to a 1D CNN for comparison with the TFTs approach. Then, different TFTs were applied to the 1D time-domain representation to produce 2D representations (i.e., grayscale images), which are fed to the CNN. While the adopted CNN architecture is canonical, there are different hyper-parameters (e.g., solver algorithm) whose impact has been evaluated on the classification performance measured in terms of accuracy (see the end of this section for the definition of the evaluation metrics). The number of epochs was set to 40. The most common hyper-parameters of each layer were optimized for each time-frequency transform and the 1D time domain representation. While this task seems to be cumbersome, the list of optimized hyper-parameters was limited to the list shown in Table 1, where the range is shown for each hyper-parameter. The range of values is usually defined for the first layer and the subsequent layers are based on the value of the first layer. The optimal identified values are an example for a specific TFT the CWT with Sombrero mother wavelet. A similar process was performed for all the other TFTs and for the 1D time domain representation, but they are not shown here for reasons of space. The metric for the identification of the optimal values was accuracy. The values represented in Figure 4 are an example of the optimal values for the optimization of the CWT (Sombrero wavelet) for the weather radar data set. The entire weather radar data set was divided in 4 portions using a 4-fold approach where the testing portion was 1/4 of the entire data set, and the training set was 3/4 of the entire data set. Then, the ratio between the sizes of the testing and training data was 1/3. The training data set was further divided, with 10% of it allocated for the validation task. As it is a 4-fold algorithm, the training and data set portions were randomly selected from the main data set. To further improve the generalization of the results, the overall 4-fold classification process was repeated 10 times (then 4 folds per 10 = 40 times), and the results (i.e., accuracy) were averaged. Figure 4: CNN architectures for the 2D Time-frequency representation and the 1D time domain representation. The values shown for the hyper-parameters are related to the optimization of the CWT (Sombrero wavelet) for the weather radar data set. The results of the evaluation of the impact of the hyper-parameters listed in Table 1 are shown more in detail in Section 7 for a specific time-frequency representation (CWT with Sombrero mother wavelet). Other hyper-parameters have been also evaluated, but their impact was not shown in the paper because the differences were minimal. For example, the authors evaluated the performance of the hyper-parameter number of filters in the first convolutional layer using a range from 16 to 64 in step 16, and the value of 32 was finally used. The values of the hyper-parameters of the second and third convolutional layer (CL) were chosen on the basis of the values of the first.