FuXiWeather2 is a unified end-to-end neural framework for weather assimilation and forecasting that outperforms global operational systems.
arXiv · March 17, 2026 · 2603.15358
The Takeaway
By aligning training objectives with real observations and reanalysis data, it bypasses traditional numerical modeling bottlenecks. It generates 10-day forecasts in minutes that exceed the skill of the ECMWF-HRES system in over 90% of evaluated metrics.
From the abstract
Numerical weather prediction has long been constrained by the computational bottlenecks inherent in data assimilation and numerical modeling. While machine learning has accelerated forecasting, existing models largely serve as "emulators of reanalysis products," thereby retaining their systematic biases and operational latencies. Here, we present FuXiWeather2, a unified end-to-end neural framework for assimilation and forecasting. We align training objectives directly with a combination of real-