Physics-informed neural network for enhancement of weather forecasts

The significance of accurate weather prediction has become more relevant in recent years. Currently, weather models primarily rely on historic data statistics and numerical methods. However, the emergence of artificial intelligence offers new possibilities for addressing the demand for accurate information on short-to-mid-term weather events. One particular field in which this is crucial concerns airports' vicinities, where it is vital for air control management to be constantly informed on potential severe weather conditions that could affect the airport operation. Accurate predictions can lead to significant cost savings by enabling efficient flight planning and optimal allocation of operational resources. To achieve these goals, operators require predictions with look-ahead times of at least one hour in addition to high spatial resolution. Our research focuses on leveraging physics-informed neural networks (PINNs) to precisely reconstruct the weather field from limited data provided by weather stations on a finer spatially-resolved grid. By enforcing compliance with physics constraints, we can enhance the deterministic and comprehensive reconstruction of the field data (i.e. wind velocity and pressure), enabling better anticipation of weather event's time evolution.