用简单修正提升台风强度预报，逼近业务最前沿。
AIFS-TC: A simple correction competitive with the operational frontier for tropical cyclone intensity forecasting
- 基于AIFS-Single模型，通过轻量级后处理修正强度预测
- 12小时到7天预报中，风速与中心气压预测媲美顶尖业务系统
- 全系统由大语言模型数小时内自动生成，适合灾害预警快速研发
人工智能天气模型正革新气象预报。尽管在台风路径预测上已超越物理驱动的数值预报（NWP），但普遍低估强度。本文提出AIFS-TC，对AIFS-Single模型进行简单修正，使其在12小时至7天的预报周期内，最大风速和最低中心气压预测性能达到当前业务前沿水平，包括快速增强事件。整个系统仅用少量自然语言提示，由大语言模型Claude Fable 5在数小时内自主设计构建，由单个领域科学家指导完成。这一成果表明，开源AI模型结合简单廉价的后处理即可达到业务级精度，凸显智能体编程在生命守护型早期预警系统中的快速探索潜力。
原文摘要 · Abstract (English)
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.
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