Summary of Fuxi-ens: a Machine Learning Model For Medium-range Ensemble Weather Forecasting, by Xiaohui Zhong and Lei Chen and Hao Li and Jun Liu and Xu Fan and Jie Feng and Kan Dai and Jing-jia Luo and Jie Wu and Bo Lu
FuXi-ENS: A machine learning model for medium-range ensemble weather forecasting
by Xiaohui Zhong, Lei Chen, Hao Li, Jun Liu, Xu Fan, Jie Feng, Kan Dai, Jing-Jia Luo, Jie Wu, Bo Lu
First submitted to arxiv on: 9 May 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 The abstract proposes an advanced machine learning (ML) model called FuXi-ENS for ensemble weather forecasting. The model aims to improve upon existing ML models by providing 6-hourly global ensemble weather forecasts up to 15 days, with a significantly increased spatial resolution of 0.25°. FuXi-ENS incorporates 18 variables at different pressure levels and the surface, leveraging the probabilistic nature of Variational AutoEncoder (VAE) to optimize a loss function combining CRPS and KL divergence. This innovative approach outperforms ensemble forecasts from ECMWF in 98.1% of variable and forecast lead time combinations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FuXi-ENS is a new way to predict the weather, using computer models to make better predictions. It can predict what will happen with the weather up to 15 days in advance, which is important for things like planning outdoor events or preparing for natural disasters. The model uses a special type of artificial intelligence called Variational AutoEncoder (VAE) to make its predictions. This helps it take into account different possibilities and probabilities, making its predictions more accurate. FuXi-ENS can also predict weather patterns at a higher level of detail than other models, which is important for many applications. |
Keywords
» Artificial intelligence » Loss function » Machine learning » Variational autoencoder
