XiChen: An observation-scalable fully AI-driven global weather forecasting system with 4D variational knowledge
This system addresses the bottleneck of dependency on Numerical Weather Prediction systems for meteorologists, offering a scalable and faster alternative.
The authors tackled the problem of slow initial condition preparation in AI-driven weather forecasting by introducing XiChen, a fully AI-driven system that completes data assimilation and medium-range forecasting in 17 seconds, achieving a skillful forecasting lead time exceeding 8.25 days.
Recent advancements in Artificial Intelligence (AI) demonstrate significant potential to revolutionize weather forecasting. However, most AI-driven models rely on Numerical Weather Prediction (NWP) systems for initial condition preparation, which often consumes hours on supercomputers. Here we introduce XiChen, the first observation-scalable fully AI-driven global weather forecasting system, whose entire pipeline, from Data Assimilation (DA) to medium-range forecasting, can be accomplished within only 17 seconds. XiChen is built upon a foundation model that is pre-trained for weather forecasting. Meanwhile, this model is subsequently fine-tuned to serve as both observation operators and DA models, thereby scalably assimilating conventional and raw satellite observations. Furthermore, the integration of four-dimensional variational knowledge ensures that XiChen's DA and medium-range forecasting accuracy rivals that of operational NWP systems, amazingly achieving a skillful forecasting lead time exceeding 8.25 days. These findings demonstrate that XiChen holds strong potential toward fully AI-driven weather forecasting independent of NWP systems.