Explainable Graph Neural Networks for Observation Impact Analysis in Atmospheric State Estimation

Weather forecasting, a critical component in industries like transportation and manufacturing, relies heavily on Numerical Weather Prediction (NWP) systems, which are based on 3D physical models and dynamical equations [1, 2]. For NWP systems to predict future atmospheric states effectively, they require accurate current atmospheric states as initial values. This necessity underscores the importance of a data assimilation (DA) system, which approximates the true atmospheric states by merging observations with prediction results from dynamical models [3]. The integration of a wide range of observations, from sources like aircraft, radiosondes, and satellites, is crucial for enhancing the DA system's accuracy [4]. Traditional methods to assess the impact of observations on weather forecasts include forecast sensitivity to observation (FSO) and its variations, such as ensemble FSO and hybrid FSO [2, 5, 6].