Towards an end-to-end artificial intelligence driven global weather forecasting system

Understand

The weather forecasting system is important for science and society, and significant achievements have been made in applying artificial intelligence (AI) to medium-range weather forecasting.

• However, existing AI-based weather forecasting models rely on analysis or reanalysis products from traditional numerical weather prediction (NWP) systems as initial conditions for making predictions.
• The initial states are typically generated by traditional data assimilation components, which are computationally expensive and time-consuming.
• Here, by cyclic training to model the steady-state background error covariance and introducing the confidence matrix to characterize the quality of observations, we present an AI-based data assimilation model, i.e., Adas, for global weather variables.