FuXi: a cascade machine learning forecasting system for 15-day global weather forecast
Problem
Medium-range weather forecasts accumulate errors beyond 10 days, so ML models still lag the ECMWF ensemble for 15-day forecasts.
Why it matters
Accurate 15-day global forecasts protect lives and support disaster preparedness, agriculture and energy planning.
What the AI did
FuXi cascades multiple ML forecasting models to reduce error accumulation across short and long lead times in global forecasts.
Results
Abstract states ML models outperform ECMWF HRES in 10-day forecasts at 0.25° resolution; FuXi's own results truncated
Impact
Evidence supports ML rivaling physics-based forecasting, but FuXi's specific gains are not shown in the abstract.
Record
| Venue | npj Climate and Atmospheric Science |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Lei Chen, Xiaohui Zhong, Feng Zhang, Yuan Cheng, Yinghui Xu, Qi Yuan, Hao Li |
| Citations | 355 (OpenAlex, at import time) |
| References | 45 |
| Model or method | FuXi (cascaded machine learning system) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1038/s41612-023-00512-1 |
| Open access | gold |
| Keywords | machine learning, weather forecasting, medium-range, cascade, ECMWF |
Importance rating
major — millions of lives, an industry or a field
Global 15-day forecasting underpins disaster preparedness and many sectors; abstract shows ML rivaling ECMWF but states no quantified human/economic impact.
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