Weather forecasting has two halves: estimating the current state (data assimilation) and projecting it forward (prediction). Traditional systems handle these separately — a physics-based assimilation system ingests observations, then a numerical model integrates the equations forward. The assimilation is the harder half, requiring the inversion of a chaotic system from sparse, noisy observations.
FuXiWeather2 unifies both halves in a single neural network. The same architecture that forecasts also assimilates — it learns to produce the atmospheric state estimate that, when forecast forward, best matches future observations. Training aligns the objectives: the analysis is good insofar as it produces good forecasts, not insofar as it matches a reanalysis dataset.
The recursive unrolling method bridges a gap that has plagued learned weather models: the training inputs (reanalysis fields) differ from the operational inputs (self-generated analyses). By training the system on its own outputs iteratively, the model learns to work with the imperfect analyses it will actually encounter in production.
The results: 0.25° global analysis and 10-day forecasts in minutes, surpassing NCEP-GFS analysis and exceeding ECMWF HRES forecasts in 91% of evaluated metrics. Strong typhoon track prediction demonstrates that the learned system captures extreme-event dynamics, not just climatological averages.
The unification is the point. When assimilation and prediction share a loss function, the analysis optimizes for what matters (forecast skill) rather than for a proxy (matching reanalysis). The two halves constrain each other.