Summary of Exploring Large Language Models For Climate Forecasting, by Yang Wang and Hassan A. Karimi
Exploring Large Language Models for Climate Forecasting
by Yang Wang, Hassan A. Karimi
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 presents a study on using large language models (LLMs), specifically GPT-4, to predict rainfall at short-term (15-day) and long-term (12-month) scales. The paper explores the potential and challenges of applying LLMs for future climate predictions, highlighting their ability to provide conservative forecasts when operating independently. The study’s results indicate that GPT tends to revert to historical averages in the absence of clear trend signals, suggesting directions for enhancing its predictive capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models like GPT-4 can help people understand complex climate data by talking to computers. But scientists want to know if these computers can accurately predict what will happen with the weather. This study looked at how well GPT-4 could guess rainfall patterns, both short-term (15 days) and long-term (12 months). They tested it in different situations, including when experts gave it more information or not. The results show that GPT-4 is good at making predictions, but it tends to be a bit too cautious and might rely on past patterns instead of new trends. |
Keywords
» Artificial intelligence » Gpt
