Summary of Karina: An Efficient Deep Learning Model For Global Weather Forecast, by Minjong Cheon et al.
KARINA: An Efficient Deep Learning Model for Global Weather Forecast
by Minjong Cheon, Yo-Hwan Choi, Seon-Yu Kang, Yumi Choi, Jeong-Gil Lee, Daehyun Kang
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Atmospheric and Oceanic Physics (physics.ao-ph)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 deep learning-based model called KARINA for climate research, specifically for global weather prediction at high resolution. The existing models require massive computational resources, which is overcome by this new approach. KARINA uses ConvNext, SENet, and Geocyclic Padding to achieve forecasting accuracy comparable to higher-resolution counterparts with significantly less computational resources, requiring only 4 NVIDIA A100 GPUs and less than 12 hours of training. This model sets new benchmarks in weather forecasting accuracy, surpassing existing models like the ECMWF S2S reforecasts at a lead time of up to 7 days. Additionally, KARINA outperforms recently developed models (Pangu-Weather, GraphCast, ClimaX, and FourCastNet) trained with high-resolution data having 100 times larger pixels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper presents a new way to predict the weather on a global scale. Current methods use lots of computer power and take a long time to work out. The new approach, called KARINA, uses less computer power and is faster than existing methods. It works by combining different techniques like ConvNext, SENet, and Geocyclic Padding. This helps improve the accuracy of weather forecasts, making it better at predicting what the weather will be like up to 7 days in advance. The new method even outperforms other recent attempts at improving weather forecasting. |
Keywords
* Artificial intelligence * Deep learning
