Accurate Medium-Range Global Weather Forecasting Based on Large AI Models
Weather forecasting is important for science and society.The conventional numerical weather prediction(NWP)method is computationally expensive and it is increasingly difficult to improve the forecast accuracy.This paper establishes Pangu-Weather,an AI-based weather forecasting system based on 3D neural networks,and equips it with Earth-specific priors and a hierarchical temporal aggregation strategy to deal with complex patterns in weather data and reduce accumulation errors in medium-range forecasting.Trained on 1979-2017 global weather data,Pangu-Weather obtains stronger deterministic forecast results on reanalysis data in all tested variables when compared with the operational integrated forecasting system(IFS)of the European Centre for Medium-Range Weather Forecasts(ECMWF).Pangu-Weather also works well with extreme weather forecasts and ensemble forecasts.When initialized with reanalysis data,the accuracy of tracking tropical cyclones is also higher than that of ECMWF-HRES.
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