Summary of Super Resolution on Global Weather Forecasts, by Lawrence Zhang et al.
Super Resolution On Global Weather Forecasts
by Lawrence Zhang, Adam Yang, Rodz Andrie Amor, Bryan Zhang, Dhruv Rao
First submitted to arxiv on: 17 Sep 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 paper addresses the challenge of improving weather forecasting, a crucial task for various applications. Current models struggle with accuracy as the forecast period increases due to the chaotic nature of weather phenomena. Classical methods rely on complex physics-based and numerical techniques but require large datasets and are computationally expensive. The advent of deep learning and publicly available high-quality weather data enables the application of machine learning methods for estimating complex systems. Deep learning models have comparable accuracy to industry-standard numerical models and are becoming more prevalent due to their adaptability. This paper focuses on improving global weather prediction by increasing spatial resolution, specifically performing super-resolution (SR) on GraphCast temperature predictions, aiming to achieve 0.5-degree precision, a significant improvement from the current 1-degree accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Weather forecasting is crucial for daily activities and disaster response planning. However, predicting the weather is challenging because many factors influence the outcome. Current methods require a lot of data and are time-consuming to update when the environment changes. Luckily, advancements in deep learning and publicly available data enable machine learning methods to predict weather patterns. Deep learning models have similar accuracy to traditional methods and are becoming more popular due to their adaptability. This paper aims to improve global weather prediction by increasing its precision, making it a valuable tool for various applications. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Precision » Super resolution » Temperature
