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Traditionally, weather predictions are performed with the help of largecomplex models of physics, which utilize different atmospheric conditions overa long period of time. These conditions are often unstable because ofperturbations of the weather system, causing the models to provide inaccurateforecasts. The models are generally run on hundreds of nodes in a large HighPerformance Computing (HPC) environment which consumes a large amount ofenergy. In this paper, we present a weather prediction technique that utilizeshistorical data from multiple weather stations to train simple machine learningmodels, which can provide usable forecasts about certain weather conditions forthe near future within a very short period of time. The models can be run onmuch less resource intensive environments. The evaluation results show that theaccuracy of the models is good enough to be used alongside the currentstate-of-the-art techniques. Furthermore, we show that it is beneficial toleverage the weather station data from multiple neighboring areas over the dataof only the area for which weather forecasting is being performed.

Smart Weather Forecasting Using Machine Learning:A Case Study in Tennessee