  Traditionally, weather predictions are performed with the help of large
complex models of physics, which utilize different atmospheric conditions over
a long period of time. These conditions are often unstable because of
perturbations of the weather system, causing the models to provide inaccurate
forecasts. The models are generally run on hundreds of nodes in a large High
Performance Computing (HPC) environment which consumes a large amount of
energy. In this paper, we present a weather prediction technique that utilizes
historical data from multiple weather stations to train simple machine learning
models, which can provide usable forecasts about certain weather conditions for
the near future within a very short period of time. The models can be run on
much less resource intensive environments. The evaluation results show that the
accuracy of the models is good enough to be used alongside the current
state-of-the-art techniques. Furthermore, we show that it is beneficial to
leverage the weather station data from multiple neighboring areas over the data
of only the area for which weather forecasting is being performed.
