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We propose a didactic approach to use the Machine Learning protocol in orderto perform weather forecast. This study is motivated by the possibility toapply this method to predict weather conditions in proximity of the Etna andStromboli volcanic areas, located in Sicily (south Italy). Here the complexorography may significantly influence the weather conditions due to Stau andFoehn effects, with possible impact on the air traffic of the nearby Cataniaand Reggio Calabria airports. We first introduce a simple thermodynamicapproach, suited to provide information on temperature and pressure when theStau and Foehn effect takes place. In order to gain information to the rainfallaccumulation, the Machine Learning approach is presented: according to thisprotocol, the model is able to ``learn'' from a set of input data which are themeteorological conditions (in our case dry, light rain, moderate rain and heavyrain) associated to the rainfall, measured in mm. We observe that, since in theinput dataset provided by the Salina weather station the dry condition was themost common, the algorithm is very accurate in predicting it. Furtherimprovements can be obtained by increasing the number of considered weatherstations and time interval.

A didactic approach to the Machine Learning application to weather forecast