## 6 Experimental Results
### Data Set  
This study uses data provided by the WSR-98D weather radar [6] located in Bobohalma, Romania and stored in the NEXRAD Level III format, as described in Section 5.1. The day used as case study is the 5 th of June 2017, a day with moderate atmospheric instability manifested through thunderstorms accompanied by heavy rain and medium-size hail. In our study we selected an area from the central Transylvania region (parts of Mures, Cluj, Alba and Sibiu counties) representing a grid having the geographical coordinates (46.076 N, 46.725 N, 23.540 E and 25.064 E). In the chosen geographical area, there were two distinct episodes with intense meteorological events in 5 June 2017: the first one between approximately 09:00 and 11:00 UTC, and the second one between approximately 12:00 and 17:00 UTC, with the most severe events taking place between 14:00 and 15:00 UTC. Concerning these phenomena, the National Meteorological Administration issued five severe weather warnings, code yellow.  
The data grid provided by the radar for the selected geographical area at a given time moment is fit to a matrix. The radar provides one data matrix for each radar product. As stated in Section 5.1, the radar data is split into multiple time stamps, each time stamp representing data gathered by the radar every 6 min (the radar takes 6 min to gather the data for the area). The radar data used in our case study has been recorded between 00:04:04 UTC and 23:54:02 UTC.  
In the current study, we are using only 13 products (i.e., \(np=13\)): base _reflectivity_ (\(R\)) of particles on six elevations (\(R01\)-\(R04\), \(R06\)-\(R07\)) _velocity_ (\(V\)) on six elevation (\(V01\)-\(V04\), \(V06\)-\(V07\)) and the estimated quantity of water (\(VIL\)) contained by a one square meter column of air. Thus, the set of considered radar products is \(\text{\emph{Prod}}=\{R01\), \(R02\), \(R03\), \(R04\), \(R06\), \(R07\), \(V01\), \(V02\), \(V03\), \(V04\), \(V06\), \(V07\), \(VIL\}\). Accordingly, _NowDeepN_ ensemble of regressors will predict 13 values, corresponding to the products previously enumerated. Our study uses only \(R\), \(V\) and \(VIL\) products, as they are mostly used by meteorologists for weather nowcasting.  
As mentioned in Section 5.2, we consider each point in the grid an instance. For each point we consider its neighbours in a certain radius. We decided to select a value of 13 for the length \(l\) of the neighborhood surrounding each point (see Section 5.3.1). More exactly, the 2D data subgrid representing the neighborhood for a point is a 13 by 13 matrix. The reason for choosing 13 as the dimensionality of the neighborhood is that it represents about 5 km in the physical world, which, from a meteorological view, is a common distance to determine small gradients. Thus for each instance we are considering a matrix of 13 by 13 points, each of which have 13 products. Therefore, for each instance we have \(13\times 13\times 13=2197\) attributes. For each timestamp we have a grid of the size \(400\times 312\). We only used the instances for which we could get the entire neighbourhood (i.e., where the neighbourhood matrix would not exceed the limit of the grid), thus obtaining 116.400 instances per timestamp. The day used as a case study contains 231 timestamps, thus of our data set consists of 26.888.400 instances. The data used in the experiments are publicly available at [[http://www.cs.ubbcluj.ro/~mihai.andrei/datasets/nowdeepn/](http://www.cs.ubbcluj.ro/~mihai.andrei/datasets/nowdeepn/)]([http://www.cs.ubbcluj.ro/~mihai.andrei/datasets/nowdeepn/](http://www.cs.ubbcluj.ro/~mihai.andrei/datasets/nowdeepn/)).