  Weather forecasting is an essential task to tackle global climate change.
Weather forecasting requires the analysis of multivariate data generated by
heterogeneous meteorological sensors. These sensors comprise of ground-based
sensors, radiosonde, and sensors mounted on satellites, etc., To analyze the
data generated by these sensors we use Graph Neural Networks (GNNs) based
weather forecasting model. GNNs are graph learning-based models which show
strong empirical performance in many machine learning approaches. In this
research, we investigate the performance of weather forecasting using GNNs and
traditional Machine learning-based models.
