ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Exploration of the Applications of Large Language Models in Weather Forecasting
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Abstract:
This study aims to explore the potential applications of large language models (LLMs) in weather forecasting and the challenges they face. By analyzing the use of LLMs in scenarios such as meteorological knowledge retrieval, foundational forecasting models, diagnostic analysis, tool invocation, and text generation, the study finds that LLMs have great potential in enhancing the accuracy of weather forecasts and the intelligence of meteorological services. LLMs provide powerful assistance to forecasters by efficiently processing vast amounts of meteorological knowledge, integrating cross-domain multi-source information, and generating customized forecast products. However, LLMs still have limitations in areas such as the spatiotemporal understanding of atmospheric motion, bias, and hallucinations, which can be addressed through techniques such as data cleaning, bias correction and fine-tuning, and retrieval-augmented generation. By constructing high-quality meteorological corpora, optimizing benchmark testing frameworks, and integrating external tools, the effectiveness of LLMs in weather forecasting can be further enhanced. Overall, LLMs bring new technological opportunities to the meteorological field, but their widespread application still requires ongoing exploration and improvement in areas such as corpus quality, model optimization, and human-machine collaboration.
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