The application of large language models in meteorology graduate research: current status, impact, and prospects

Clinical Snapshot

Key Findings

• P Value: Not applicable for qualitative study

• Effect Size: Significant enhancement in research efficiency, particularly in code development and literature translation

• Primary Outcome: LLMs primarily applied in literature review, data processing, code development, and academic writing

• Nnt Or Sensitivity: Time savings reported as 'considerable' but not quantified

• Confidence Interval: Not reported

Clinical Application

Not applicable to clinical practice Not relevant to Australian healthcare system - study focuses on meteorological research education Not applicable - this is a meteorology/education study, not clinical research

Abstract

With the rapid development of generative artificial intelligence, large language models (LLMs) have gradually integrated into various fields, demonstrating significant potential, particularly in meteorological research. This study explores the current application, advantages, challenges, and future development trends of LLMs in the scientific work of meteorology graduate students at NUIST. Through surveys and case analysis, the study finds that LLMs are primarily applied in literature review, data processing, code development, and academic writing in meteorological research. The results show that LLMs significantly enhance research efficiency, particularly in code development and literature translation, saving considerable time for graduate students. However, challenges remain in areas such as the accuracy of professional knowledge, creative inspiration, and interdisciplinary integration. The study also reveals concerns over data security, academic integrity, and model limitations when using LLMs. Future applications of LLMs in meteorology need further optimization in terms of professional knowledge accuracy and data processing capabilities. This paper provides both theoretical support and practical guidance for the responsible integration of LLMs into meteorological research and education.