用大模型分析2699篇论文，揭示降水模拟中九种微物理参数化的使用规律与偏差。
Using large language models to produce literature reviews: Usages and systematic biases of microphysics parametrizations in 2699 publications
- 用GPT-4 Turbo自动提取2699篇论文中的参数化配置与性能数据。
- 七种参数化普遍存在降水高估，尤其在中国、东南亚等地。
- 方法可推广至其他领域，快速挖掘海量文献中的科学规律。
大型语言模型为开展密集型科研任务提供了新可能，使以往难以实现的系统性文献审视成为现实。本文利用大模型对2699篇与天气研究和预报模式（WRF）微物理参数化相关的文献进行系统分析，旨在揭示其在降水模拟中的使用方式与系统性偏差。数据源自Web of Science与Scopus检索结果，采用GPT-4 Turbo从论文文本中提取模型配置与性能信息。结果显示，全球范围内最常用的九类微物理参数化（Lin、Ferrier、WRF Single-Moment、Goddard Cumulus Ensemble、Morrison、Thompson、WRF Double-Moment）中，2020年前以一阶参数化为主，之后转向双阶参数化；七种参数化普遍高估降水，尤其在中国、东南亚、美国西部及中非地区；而Lin、Ferrier与Goddard参数化则在多数区域低估降水。该方法可为其他研究者提供基于AI挖掘海量文献的新路径。
原文摘要 · Abstract (English)
Large language models afford opportunities for using computers for intensive tasks, realizing research opportunities that have not been considered before. One such opportunity could be a systematic interrogation of the scientific literature. Here, we show how a large language model can be used to construct a literature review of 2699 publications associated with microphysics parametrizations in the Weather and Research Forecasting (WRF) model, with the goal of learning how they were used and their systematic biases, when simulating precipitation. The database was constructed of publications identified from Web of Science and Scopus searches. The large language model GPT-4 Turbo was used to extract information about model configurations and performance from the text of 2699 publications. Our results reveal the landscape of how nine of the most popular microphysics parameterizations have been used around the world: Lin, Ferrier, WRF Single-Moment, Goddard Cumulus Ensemble, Morrison, Thompson, and WRF Double-Moment. More studies used one-moment parameterizations before 2020 and two-moment parameterizations after 2020. Seven out of nine parameterizations tended to overestimate precipitation. However, systematic biases of parameterizations differed in various regions. Except simulations using the Lin, Ferrier, and Goddard parameterizations that tended to underestimate precipitation over almost all locations, the remaining six parameterizations tended to overestimate, particularly over China, southeast Asia, western United States, and central Africa. This method could be used by other researchers to help understand how the increasingly massive body of scientific literature can be harnessed through the power of artificial intelligence to solve their research problems.
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