Summary of Hr-extreme: a High-resolution Dataset For Extreme Weather Forecasting, by Nian Ran et al.
HR-Extreme: A High-Resolution Dataset for Extreme Weather Forecasting
by Nian Ran, Peng Xiao, Yue Wang, Wesley Shi, Jianxin Lin, Qi Meng, Richard Allmendinger
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper introduces a new dataset for training large deep learning models in weather forecasting, specifically focusing on extreme weather events. The dataset is derived from the High-Resolution Rapid Refresh (HRRR) data and includes high-resolution cases of extreme weather. Previous research has largely neglected extreme weather events, despite their critical importance in accurate forecasting. This study aims to address this gap by providing a comprehensive dataset for training models that can accurately predict extreme weather. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special set of weather data just for predicting really bad weather like hurricanes or tornadoes. Right now, most weather forecast models aren’t very good at predicting these kinds of events, which is a big problem because they’re really important to get right. The new dataset uses information from a high-resolution real-time weather system called HRRR and will help train computers to be better at forecasting extreme weather. |
Keywords
» Artificial intelligence » Deep learning
