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

TL;DR

This paper introduces a machine learning-based weather prediction method using historical data from multiple stations, offering accurate, resource-efficient forecasts that complement traditional physics-based models.

Contribution

It presents a novel approach combining simple machine learning models with multi-station data to improve short-term weather forecasting efficiency and accuracy.

Findings

Models achieve good accuracy for near-future forecasts.

Leveraging data from multiple stations improves prediction quality.

Resource-efficient models can operate on less powerful environments.

Abstract

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.