The Compatibility between the Pangu Weather Forecasting Model and Meteorological Operational Data
This addresses the practical integration of data-driven weather models like Pangu-Weather with existing operational systems for meteorologists, but it is incremental as it focuses on compatibility rather than new breakthroughs.
The paper evaluated the compatibility of the Pangu-Weather model with operational meteorological data, finding it works well with various initial conditions and shows stable forecasting, and verified that improving initial condition quality enhances its performance.
Recently, multiple data-driven models based on machine learning for weather forecasting have emerged. These models are highly competitive in terms of accuracy compared to traditional numerical weather prediction (NWP) systems. In particular, the Pangu-Weather model, which is open source for non-commercial use, has been validated for its forecasting performance by the European Centre for Medium-Range Weather Forecasts (ECMWF) and has recently been published in the journal "Nature". In this paper, we evaluate the compatibility of the Pangu-Weather model with several commonly used NWP operational analyses through case studies. The results indicate that the Pangu-Weather model is compatible with different operational analyses from various NWP systems as the model initial conditions, and it exhibits a relatively stable forecasting capability. Furthermore, we have verified that improving the quality of global or local initial conditions significantly contributes to enhancing the forecasting performance of the Pangu-Weather model.