Maximising Weather Forecasting Accuracy through the Utilisation of Graph Neural Networks and Dynamic GNNs

Understand

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.