We present FengWu, an advanced data-driven global medium-range weather
forecast system based on Artificial Intelligence (AI). Different from existing
data-driven weather forecast methods, FengWu solves the medium-range forecast
problem from a multi-modal and multi-task perspective. Specifically, a deep
learning architecture equipped with model-specific encoder-decoders and
cross-modal fusion Transformer is elaborately designed, which is learned under
the supervision of an uncertainty loss to balance the optimization of different
predictors in a region-adaptive manner. Besides this, a replay buffer mechanism
is introduced to improve medium-range forecast performance. With 39-year data
training based on the ERA5 reanalysis, FengWu is able to accurately reproduce
the atmospheric dynamics and predict the future land and atmosphere states at
37 vertical levels on a 0.25{\deg} latitude-longitude resolution. Hindcasts of
6-hourly weather in 2018 based on ERA5 demonstrate that FengWu performs better
than GraphCast in predicting 80\% of the 880 reported predictands, e.g.,
reducing the root mean square error (RMSE) of 10-day lead global z500
prediction from 733 to 651 $m^{2}/s^2$. In addition, the inference cost of each
iteration is merely 600ms on NVIDIA Tesla A100 hardware. The results suggest
that FengWu can significantly improve the forecast skill and extend the
skillful global medium-range weather forecast out to 10.75 days lead (with ACC
of z500 > 0.6) for the first time.

该研究论文介绍了一个基于人工智能的先进数据驱动的全球中程天气预报系统 FengWu，该系统从多模态和多任务的角度解决介质范围的天气预报问题，可在 0.25 度经纬度分辨率下预测未来 37 个垂直层次的地面和大气状态，与现有天气预报方法相比，FengWu 性能更佳。