## 2 Literature Review  
Most research works on weather prediction are carried out through numerical methods. These methods are carried out by using the laws of fluid dynamics and of chemical processes taking place in the atmosphere to then integrate all the meteorological data available onto a computational grid and project their evolution in time. There are two types of models according to the resolution of their computational grid: synoptic scale models (macro-scale with wide mesh of 40 km or wider) and regional scale models (mesoscale with meshes of a few kilometers), which have better spatial and temporal resolution. There are many models being used nowadays, both government and private models. For instance, the Global Forecast System (GFS) model [3] is a numerical weather prediction model produced by the National Center for Environmental Prediction-National Oceanic and Atmospheric Administration (NOAA-NCEP). GFS is an open and free model at a synoptic scale with horizontal resolution of 27 km for mid-term prediction (eight days) and resolution of 70 km for long-term prediction (16 days). There is also the Weather Research and Forecasting (WRF) model [4], which is a  
Figure 1: Canary Islands, National Aeronautics and Space Administration (NASA) image acquired 13 October 2011.  
mesoscale system limited to a specific region (between 2 and 15 km) and mid-term weather prediction (1-2 days), and thus closer to reality. Another model is the European Centre for Medium-Range Weather Forecasts (ECMWF) model [5], a system with horizontal resolution of 9 km for 10-day prediction and a resolution of 18 km for 15 days. On the one hand, one of the main problems of weather forecasting with numerical methods is their poor performance in segmented regions with abrupt orography, which make them not very suitable for the prediction of the evolution of Mesoscale Convective Systems (MCS). On the other hand, there are numerous contributions regarding the use of ML models for weather prediction, despite the permanent controversy that, although through ML we can obtain models to predict a meteorological phenomenon in a more or less reliable way, ML cannot explain these phenomena in physical terms. In the reviewed literature, most research in meteorological prediction has been carried out mainly in two areas: the construction of models based on machine learning for the prediction of meteorological parameters in general and specific models for rainfall prediction, these ones due to the need to estimate risks appropriately and also because of economic reasons. With regard to the first models mentioned, it is worth highlighting the works of [6; 7; 8; 9; 10]. In [10], several data mining algorithms (DM) are explored using large datasets, especially daily meteorological data from NOAA, which make it possible to identify climate patterns and make valid mid-term predictions. In addition, in [8], useful knowledge is extracted from a 9-year period of meteorological data using DM. The steps taken in this study are: data preprocessing, outlier analysis, clustering, classification, and prediction. Each technique used is classified according to its relevance in the prediction. A more specific application is proposed in [7], where authors use a model to show the relationship between meteorological parameters read in meteorological stations and secondary parameters such as vertical velocity variance or surface heat flux. In the study by [9], a hybrid global climate model is suggested (H GCM) through the use of neural networks to speed up the calculations of predictions. In the work by [6], the authors suggest a procedure to predict the behavior of chaotic systems. As an application, this technique is used to obtain stable behavioral patterns of the North Atlantic Oscillation (NAO) index in its prediction through independent component analysis for spatiotemporal data of the variable sea-level pressure (SLP). Calculations are validated with temporal data registered by the NAO index.