Abstract
Generative AI, such as OpenAI's GPT-4V large-language model, has rapidly entered mainstream discourse. Novel capabilities in image processing and natural-language communication may augment existing forecasting methods. Large language models further display potential to better communicate weather hazards in a style honed for diverse communities and different languages. This study evaluates GPT-4V's ability to interpret meteorological charts and communicate weather hazards appropriately to the user, despite challenges of hallucinations, where generative AI delivers coherent, confident, but incorrect responses. We assess GPT-4V's competence via its web interface ChatGPT in two tasks: (1) generating a severe-weather outlook from weather-chart analysis and conducting self-evaluation, revealing an outlook that corresponds well with a Storm Prediction Center human-issued forecast; and (2) producing hazard summaries in Spanish and English from weather charts. Responses in Spanish, however, resemble direct (not idiomatic) translations from English to Spanish, yielding poorly translated summaries that lose critical idiomatic precision required for optimal communication. Our findings advocate for cautious integration of tools like GPT-4V in meteorology, underscoring the necessity of human oversight and development of trustworthy, explainable AI.
Abstract (translated)
生成式 AI，如 OpenAI 的 GPT-4V 大语言模型，已经迅速进入主流论述。图像处理和自然语言通信的新功能可能增强现有的预测方法。大语言模型进一步表现出更好地沟通各种社区和语言的天气危险的能力。这项研究评估了 GPT-4V 通过其 Web 界面 ChatGPT 解释气象图表并适当地向用户传达天气危险的能力，尽管存在幻觉，生成式 AI 提供了连贯、自信、但错误的回应。我们通过 GPT-4V 的 Web 界面 ChatGPT 对其进行了评估： （1）从气象图表分析中生成严重天气展望并进行自我评估，揭示出与风暴预测中心人类发布的预报相符的展望； （2）从气象图表中生产西班牙语和英语的警示摘要。然而，西班牙语的回答似乎更像是从英语到西班牙语的直接翻译，导致译文准确性差，关键的惯用语 precision 丢失，影响了 optimal communication 的最佳效果。 我们的研究建议在气象学中谨慎使用类似 GPT-4V 的工具，强调在气象学中人类监督和开发可信赖、可解释的 AI 的必要性。
URL
https://arxiv.org/abs/2404.15166
