Summary of Fengwu-w2s: a Deep Learning Model For Seamless Weather-to-subseasonal Forecast Of Global Atmosphere, by Fenghua Ling et al.
FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere
by Fenghua Ling, Kang Chen, Jiye Wu, Tao Han, Jing-Jia Luo, Wanli Ouyang, Lei Bai
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 A novel deep learning approach, FengWu-Weather to Subseasonal (FengWu-W2S), is proposed to seamlessly forecast atmospheric conditions up to 42 days ahead. Building on the FengWu global weather forecast model, this framework incorporates an ocean-atmosphere-land coupling structure and a diverse perturbation strategy. FengWu-W2S demonstrates reliable predictions of atmospheric conditions out to 3-6 weeks ahead, enhancing predictive capabilities for global surface air temperature, precipitation, geopotential height, and intraseasonal signals such as the Madden-Julian Oscillation (MJO) and North Atlantic Oscillation (NAO). The model’s performance is evaluated through hindcast results, showcasing its potential in developing AI-based integrated systems for seamless weather-climate forecasting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FengWu-W2S is a new way to predict the weather and climate. Instead of using separate models for each, this approach combines them into one deep learning system. The system uses information from oceans, atmosphere, and land to make predictions. It can forecast atmospheric conditions up to 42 days ahead, which is much longer than current systems can do. This helps improve our understanding of the weather and climate, especially for things like temperature, precipitation, and large-scale patterns like the Madden-Julian Oscillation (MJO) and North Atlantic Oscillation (NAO). |
Keywords
* Artificial intelligence * Deep learning * Temperature
