发表机构
University of Cambridge; European Centre for Medium-Range Weather Forecasts（剑桥大学; 欧洲中期天气预报中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出AIFS-TC修正方法，基于开源AIFS-Single模型，可在12小时至7天时效内媲美业务前沿的热带气旋强度预报性能，且由Claude Fable 5自主设计，凸显智能体编码在预警系统研发中的潜力。
AI 中文摘要
AI气象模型正推动气象预报领域的变革。尽管这类模型在热带气旋（TC）路径预报上表现优于基于物理的数值天气预报（NWP），但在强度预报上存在严重低估问题。本文提出AIFS-TC，这是对AIFS-Single模型的一种简单修正，在12小时至7天的预报时效内，其最大风速和最低中心气压预报性能可与业务前沿水平相媲美，且该性能在快速增强事件中同样成立。值得注意的是，整个系统由大型语言模型Claude Fable 5在数小时内自主设计构建，仅需一名领域科学家通过少量自然语言提示引导。利用开源AI预报模型AIFS-Single及相对简单、低成本的后处理即可达到业务前沿水平，这对热带气旋科学研究具有重要意义，也表明智能体编码是其他领域救生预警系统快速探索与进步的可行途径。
英文摘要
AI weather models are in the process of revolutionising weather forecasting. While these models have been shown to achieve superior performance to physics-based NWP in forecasting tropical cyclone (TC) tracks, they tend to dramatically underestimate intensity. Here we present AIFS-TC, a simple correction to the AIFS-Single model that is competitive with the operational state-of-the-art for forecasting maximum wind speed and minimum central pressure at lead times of 12 h to seven days. This performance also holds for rapid intensification events. Notably, the entire system was autonomously designed and built by a large language model (Claude Fable 5) in a few hours, directed through a small number of natural-language prompts by a single domain scientist. That the operational frontier can be reached with an open-source AI forecast model (AIFS-Single) and relatively simple, cheap post-processing is significant for TC science, and points to agentic coding as a route to rapid exploration and progress in life-saving early-warning systems in other domains.
Comments6 pages, 5 figures, 2 tables
