FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond
10 Days Lead
- URL: http://arxiv.org/abs/2304.02948v1
- Date: Thu, 6 Apr 2023 09:16:39 GMT
- Title: FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond
10 Days Lead
- Authors: Kang Chen and Tao Han and Junchao Gong and Lei Bai and Fenghua Ling
and Jing-Jia Luo and Xi Chen and Leiming Ma and Tianning Zhang and Rui Su and
Yuanzheng Ci and Bin Li and Xiaokang Yang and Wanli Ouyang
- Abstract summary: We present FengWu, an advanced data-driven global medium-range weather forecast system based on Artificial Intelligence (AI)
FengWu is able to accurately reproduce the atmospheric dynamics and predict the future land and atmosphere states at 37 vertical levels on a 0.25deg latitude-longitude resolution.
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.
- Score: 93.67314652898547
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
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