从人工构建到AI驱动的情景涌现：重新思考巨灾风险建模
From Manual Construction to AI-Driven Scenario Emergence: Rethinking Catastrophe Risk Modeling
AI总结：
针对传统巨灾风险模型依赖昂贵人工构建的瓶颈，本文提出TAISE框架，利用AI天气预报模型通过自我迭代生成连贯极端天气序列，以数量级降低计算成本，实现动态全面的巨灾风险评估。
AI中文摘要：
传统的巨灾（CAT）风险模型依赖昂贵的人工构建来生成极端天气情景，这种方法自1990年代以来基本未变。随着气候极端事件加剧，这给整个风险转移链条带来了日益严峻的挑战。本研究提出TAISE框架，该框架重新利用AI天气预报模型，以传统成本的一小部分生成连贯的极端天气序列。通过自我迭代生成，该框架产生连续的全球大气场，极端事件从中涌现。一项概念验证实验表明，与传统方法相比，计算成本降低了一个数量级，同时捕捉到了基于快照的方法所缺失的时间连续性和跨区域相关性。这些发现为巨灾风险量化的民主化以及为保险公司、再保险公司、ILS基金管理者和公共部门风险管理者提供动态、全面的投资组合评估指明了一条路径。
英文摘要：
Traditional catastrophe (CAT) risk models rely on costly manual construction to generate extreme weather scenarios, an approach largely unchanged since the 1990s. As climate extremes intensify, this creates mounting challenges to the entire risk transfer chain. This study proposes the TAISE framework, which repurposes AI weather forecasting models to produce coherent extreme weather sequences at a fraction of traditional costs. Through self-iterative generation, the framework produces continuous global atmospheric fields from which extreme events emerge. A proof-of-concept experiment demonstrates an order-of-magnitude reduction in computational cost compared with conventional methods, while capturing temporal continuity and cross-regional correlations absent in snapshot-based approaches. These findings suggest a pathway toward democratising catastrophe risk quantification and enabling dynamic, comprehensive portfolio assessment for insurers, reinsurers, ILS fund managers and public-sector risk managers.
