## 6 Experimental Results
### Data Analysis  
In order to estimate the impact of the data cleaning step, we analyzed the data set before and after cleaning. For each of the data products \(V01\), \(V02\), \(V03\), \(V04\), \(V06\) and \(V07\) (which were possibly cleaned) and each time stamp \(t,\ 1\leq t\leq 231\), we computed the average values of the radar products for all the cells from the analyzed grid. Additionally, the average of the mean values for all \(V\) products and all the cells from the grid were calculated for each time stamp.  
Figure 5 comparatively depicts the variation of the mean \(V\) value with respect to each time stamp (ranging from 1 to 231) for three cases: (1) _before the cleaning_ step; (2) before cleaning but _ignoring the invalid values_; and (3) _after the cleaning_. A step of 10 was selected on OX axis (i.e., an hour). Graphical representations similar to the ones from Figure 5 were created for \(V01\)-\(V07\), as well. The time series plots for \(V01\) and \(V06\) are illustrated in Figures 6 and 7, respectively.  
Analyzing the plots from Figures 5-7 and comparing the evolution of values _before cleaning_ (red coloured), before cleaning but ignoring _invalid values_ (blue coloured) and _after cleaning_ (green coloured) we observe the following. At lower degrees of elevations (see the plot for \(V01\) from Figure 6) there are much more invalid values than at higher degrees of elevation (see the graph for V06 from Figure 7). Besides, from a meteorological viewpoint, higher degrees of elevation are related to higher altitudes, implying a less chaotic air circulation, leading to more precise radar soundings. The impact of the data cleaning step is noticeable from the time series plots, as for \(V01\) the graph before data cleaning significantly differs from the graph after cleaning. The two graphs have very different shapes and this suggests that the noise introduced in data after the cleaning step is considerably smaller than the noise existing in data before replacing the invalid \(V\) values. We note that a similar situation has been observed for \(V02\)-\(V04\) as well.  
Much more, the time series plots before data cleaning but ignoring invalid values (blue coloured) resembles to the graph after the invalid values were cleaned (green coloured). Figure 8 depicts a zoomed-in version of the graphs from the upper side of Figure 6 (the time series before cleaning but ignoring the invalid values and time series after cleaning). At higher degrees of elevation (V06), there is no significant difference between the evolution of \(V\) values before and after cleaning (the red coloured and green coloured plots from Figure 7). The shapes of the two plots are very similar for \(V06\) and this was also observed for \(V07\).  
Figure 5: Time series plot for mean \(V\) values: ignoring invalid values, before and after cleaning.  
Figure 6: Time series plot for average \(V01\) values: ignoring invalid values, before and after cleaning.  
Figure 8: Time series plot for average \(V01\) values: ignoring invalid values and after cleaning.  
Figure 7: Time series plots for average \(V06\) values: ignoring invalid values, before and after cleaning.  
All the previous observations lead 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 \(V\).