## 7 Discussion
### Impact of the Data Cleaning Step  
As previously shown in Section 6.1, the training data set contains instances with errors, particularly at lower degrees of elevation. Obviously, the noisy training data may affect the performance of the learning task. In this regard, a data cleaning step motivated by the meteorological perspective has been introduced in Section 5.3.1 for replacing the invalid values in the data set which were identified mostly for the product \(V\) at lower degrees of elevation (\(V01\)-\(V04\)). The analysis we performed on data after it was cleaned (Section 6.2) led us to the hypothesis that the cleaning step would impact the overall performance of \(NowDeepN\), and this should be visible at least at lower degrees of elevations for \(V\). Intuitively, as the data cleaning is correlated with the meteorological evidence, we expect a better performance of \(NowDeepN\), particularly at lower degrees of elevation.  
In order to empirically validate the hypothesis that the cleaning step improves the predictive performance of \(NowDeepN\), we have evaluated the model trained on the uncleaned data set, using the same methodology introduced in Section 5.  
Table 2 depicts the results obtained applying \(NowDeepN\) on the uncleaned data. Comparing the results with those obtained on the cleaned data (Table 1) we observe an improvement in the predictive performance of \(NowDeepN\) achieved on the cleaned data. The last column from the Table 2 illustrates the improvement obtained using the cleaning step, computed for each evaluation measure \(E\) (i.e., RMSE, NRMSE, \(\text{RMSE}_{non-zero}\), \(\text{NRMSE}_{non-zero}\)) for all 13 radar products. The improvement is computed as \(\frac{E_{unchanged}-E_{cleaned}}{E_{unchanged}}\).  
Figure 11 depicts a very similar situation as Figure 10, the only difference being that the image represents values for R01 predicted by \(NowDeepN\) trained on _uncleaned_ data. Similar to Figures 9 and 10, the image in Figure 11 represents a geographical area with each pixel representing a geographical location of roughly 1 km\({}^{2}\). Again, the values on axes OX and OY represent the coordinates of pixels inside the image relative to the \((0,0)\) origin in the upper-left corner, while axis OX runs along the longitudes and axis OY runs along the latitudes, with the \((0,0)\) origin being the most north-western point in the geographical area represented by the image. The most erroneous values are on the \(V\) products, yet the errors can still greatly affect the other products such as R01, presented in the figure. While in the predicted data the shapes and intensities are largely retained as  
in the real data, some significant anomalies can be observed. For example, around the point at (140, 250) in the predicted data can be seen an area of higher valued points that is completely absent int the real data, with no means to attribute it to the smoothing effect. Also, in the upper right corner at around (10, 380), there can be seen in the real data a significant shape with quite high values at the interior that is simply removed in the predicted data, although the surrounding shapes are much better represented. There are other such anomalies that cannot be explained by the smoothing effect that appear in the data predicted by \(NowDeepN\) trained on uncleaned data. None of these anomalies appear on the data predicted \(NowDeepN\) trained on cleaned data. This suggests that there were some erroneous values of \(V\) in those areas that also affected how \(NowDeepN\) predicted the other products, and thus, cleaning the data yields much better results.