## 1 Introduction  
Weather nowcasting [1, 2] refers to short-time weather prediction, namely weather analysis and forecast for the next 0 to 6 h. Nowadays, the role of nowcasting in crisis management and risk prevention is increasing, as more and more severe weather events are expected [3]. Large volumes of meteorological data, including radar, satellite and weather stations' observations, are held by meteorological institutes and available for analysis. Radars and weather stations are constantly collecting real-time data, while data about cloud patterns, winds, temperature are continuously gathered by weather-focused satellites. Thus, there is large amount of meteorological related data available to be analyzed using machine learning (ML) based algorithms for improving the accuracy of short-term weather-prediction techniques. The World Meteorological Organization (WMO) [4] mentions that \"nowcasting plays an increasing role in crisis management and risk prevention, but its realization is a highly complex task\", the highest difficulties being related to the small-scale nature of convective weather phenomena. This implies that the Numerical Weather Prediction approach (NWP) [5] is not feasible and that the forecast has to rely mainly on the extrapolation of known weather parameters. As meteorological institutes worldwide expect climate changes including extreme rain phenomena [3], there is an increasing need for accurate and early warning of severe weather events. Considering the increased number and intensity of severe meteorological phenomena, predicting them in due time to avoid disasters becomes highly demanding for meteorologists. Mainly due to the extremely large volume of data that has to be analyzed in a short period of time, issuing a nowcasting warning is a complex and difficult task. For operative meteorologists it is difficult to issue nowcasting warnings, as there is a large volume of meteorological data (radar, satellite, or other weather stations' observations) that has to be analyzed in order to take an appropriate decision. Besides, given the stochastic and chaotic character of the atmosphere, the evolution of certain weather phenomena are difficult to predict by human experts. Thus, machine learning (ML) and _deep learning_[1,2] techniques are useful for assisting meteorologists in the decision-making process, offering solutions for nowcasting by learning relevant patterns from large amount of weather data. Most of the existing operational and semi-operational methods for nowcasting are using the extrapolation of radar data and algorithms mainly based on cell tracking. Existing nowcasting techniques use various data sources which may be relevant for accurate nowcasting, such as: meteorological data (radar, satellite, meteorological observations) and geographical data (elevation, exposure, vegetation, hydrological features, anthropic features). In the current study we used real radar data provided by one of the WSR-98D weather radars [6] of the Romanian National Weather Administration. Given its capability to determine the location, size, direction and speed of water droplets, the weather radar is an essential tool used by meteorologists for nowcasting. For this type of forecast, fast decisions are imposed, so with the aim of facilitating the analysis in an operative environment, the data retrieved by the radar is supplied to the meteorologist in the form of coloured maps, which are easy to assess on a brief overview. On such a map, each pixel corresponds to a geographical location and its colour represents a certain value of the displayed product. In a short period of time, commonly under one minute, the meteorologist can locate potential dangerous storm cells, analyse their vertical structure, relative speed and direction, the dimension of hail, top of the clouds, and other relevant parameters.