## 1 Introduction
At this point, the operator should appreciate the current phase of the storm and by extrapolating those values in time, usually up to one hour, predicts the future development (intensity and area affected) of the storm. Although extrapolating the radar echoes is one of the main techniques used in nowcasting, it presents weaknesses in terms of processing times and precision, all related to the skills and experience of the meteorologist. Most of the currently existing operational and semi-operational methods for nowcasting use the extrapolation of radar data and algorithms mainly based on cell tracking [7, 8, 9]. However, an important limitation of existing centroid cell-tracking algorithms lies in the detection of storms segments having irregular shapes or with variable wind speeds, resulting in identification and tracking errors. With the goal of helping meteorologists in analysing radar data for issuing nowcasting warnings, we are introducing in this paper a supervised learning model \(NowDeepN\) based on an ensemble of deep neural network (DNN) regressors for predicting the values for radar products which may be used for weather nowcasting. As an additional goal, we aim to empirically validate the hypothesis that similar values for the radar products at a given time moment for a certain geographical region are encoded in similar neighborhoods of that region at previous time moments. The values predicted by \(NowDeepN\) for the radar products at certain time moments may be used by the meteorologist in estimating the future development of potential severe phenomena and thus \(NowDeepN\) would replace the time consuming process for the operational meteorologists of extrapolating the radar echoes. As a proof of concept, \(NowDeepN\) is proposed for learning to approximate a function between past values of the radar products extracted from radar observations and their future values. Experiments will be performed on real radar data provided by the Romanian National Meteorological Administration and collected on the Central Transylvania region. Our experimental goal is to obtain an empirical evidence that in both normal and severe weather conditions the values for a radar product at a given moment in a certain location are predictable from the values of the neighboring locations from previous time moments. If this stands, the study may be further improved and extended on a larger scale. In addition, we are proposing a _data cleaning_ step useful for correcting the erroneous input radar data. To the best of our knowledge it is the first time an approach such as \(NowDeepN\) is introduced in the nowcasting literature. To summarize the contribution of this paper, our goal is to answer the following research questions:  
* Are deep neural networks able to predict the values for a radar product at a given moment in a certain geographical location from the values of its neighboring locations from previous time moments? To what extent this holds both for both normal and severe weather conditions? For answering this research question, we are introducing a supervised learning model \(NowDeepN\) consisting of an ensemble of DNN-based regressors. * How does the data cleaning step introduced for correcting the erroneous input data impact the predictive performance of the \(NowDeepN\) model? In addition, to what extent is the proposed data cleaning step correlated with the meteorological perspective? * How relevant are the features considered in the supervised learning task? More specifically, are the radar products' values from the neighboring area of a certain geographical location \(l\) at time \(t-1\) relevant for predicting the radar products' values on location \(l\) at time \(t\)? The remainder of the paper is structured as follows. The problem of weather nowcasting, its importance and main challenges are discussed in Section 2. Section 3 discusses about the fundamental concepts regarding the used machine learning models, whilst a literature review on supervised learning based weather nowcasting is presented in Section 4.