In this paper, we present Pangu-Weather, a deep learning based system for
fast and accurate global weather forecast. For this purpose, we establish a
data-driven environment by downloading 43 years of hourly global weather data
from the 5th generation of ECMWF reanalysis (ERA5) data and train a few deep
neural networks with about 256 million parameters in total. The spatial
resolution of forecast is 0.25∘×0.25∘, comparable to the ECMWF
Integrated Forecast Systems (IFS). More importantly, for the first time, an
AI-based method outperforms state-of-the-art numerical weather prediction (NWP)
methods in terms of accuracy (latitude-weighted RMSE and ACC) of all factors
(e.g., geopotential, specific humidity, wind speed, temperature, etc.) and in
all time ranges (from one hour to one week). There are two key strategies to
improve the prediction accuracy: (i) designing a 3D Earth Specific Transformer
(3DEST) architecture that formulates the height (pressure level) information
into cubic data, and (ii) applying a hierarchical temporal aggregation
algorithm to alleviate cumulative forecast errors. In deterministic forecast,
Pangu-Weather shows great advantages for short to medium-range forecast (i.e.,
forecast time ranges from one hour to one week). Pangu-Weather supports a wide
range of downstream forecast scenarios, including extreme weather forecast
(e.g., tropical cyclone tracking) and large-member ensemble forecast in
real-time. Pangu-Weather not only ends the debate on whether AI-based methods
can surpass conventional NWP methods, but also reveals novel directions for
improving deep learning weather forecast systems.Comment: 19 pages, 13 figures: the first ever AI-based method that outperforms
traditional numerical weather prediction method