Summary of Fuxi Weather: a Data-to-forecast Machine Learning System For Global Weather, by Xiuyu Sun and Xiaohui Zhong and Xiaoze Xu and Yuanqing Huang and Hao Li and J. David Neelin and Deliang Chen and Jie Feng and Wei Han and Libo Wu and Yuan Qi
FuXi Weather: A data-to-forecast machine learning system for global weather
by Xiuyu Sun, Xiaohui Zhong, Xiaoze Xu, Yuanqing Huang, Hao Li, J. David Neelin, Deliang Chen, Jie Feng, Wei Han, Libo Wu, Yuan Qi
First submitted to arxiv on: 10 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Atmospheric and Oceanic Physics (physics.ao-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract presents a novel machine learning weather forecasting system called FuXi Weather, which can generate reliable and accurate 10-day global weather forecasts with a spatial resolution of 0.25^. Unlike traditional numerical weather prediction (NWP) systems, FuXi Weather assimilates data from multiple satellites and operates on a 6-hourly data assimilation (DA) and forecast cycle. This system outperforms the European Centre for Medium-range Weather Forecasts (ECMWF) high-resolution forecasts (HRES) in observation-sparse regions such as central Africa, demonstrating its potential to improve forecasts where observational infrastructure is limited. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FuXi Weather is a new way to make accurate weather forecasts using computers. Instead of relying on big supercomputers like before, FuXi Weather uses special machines that can learn from lots of data. This helps us predict the weather better and more quickly. The system looks at data from many satellites and makes predictions for the next 10 days. It’s very good at predicting the weather in places where there aren’t as many weather stations. |
Keywords
» Artificial intelligence » Machine learning
