Towards an end-to-end artificial intelligence driven global weather forecasting system
This addresses the computational bottleneck in weather forecasting for meteorologists and society by enabling faster, more efficient predictions with practical real-world applications.
The authors tackled the problem of AI-based weather forecasting systems requiring computationally expensive traditional data assimilation for initial conditions by developing Adas, an AI-based data assimilation model, and combining it with the FengWu forecasting model to create FengWu-Adas, the first end-to-end AI-based global weather forecasting system, which achieved forecasts with a skillful lead time exceeding that of the IFS.
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. Initial states are typically generated by traditional data assimilation components, which are computational expensive and time-consuming. Here we present an AI-based data assimilation model, i.e., Adas, for global weather variables. By introducing the confidence matrix, Adas employs gated convolution to handle sparse observations and gated cross-attention for capturing the interactions between the background and observations. Further, we combine Adas with the advanced AI-based forecasting model (i.e., FengWu) to construct the first end-to-end AI-based global weather forecasting system: FengWu-Adas. We demonstrate that Adas can assimilate global observations to produce high-quality analysis, enabling the system operate stably for long term. Moreover, we are the first to apply the methods to real-world scenarios, which is more challenging and has considerable practical application potential. We have also achieved the forecasts based on the analyses generated by AI with a skillful forecast lead time exceeding that of the IFS for the first time.