Catastrophe models estimate risk at the intersection of hazard, exposure, and vulnerability. Each of these areas requires diverse sources of data, which are very often incomplete, inconsistent, or missing altogether. The poor quality of the data is a source of epistemic uncertainty, which affects the vulnerability models as well as the output of the catastrophe models. This article identifies the different sources of epistemic uncertainty in the data, and elaborates on strategies to reduce this uncertainty, in particular through identification, augmentation, and integration of the different types of data. The challenges are illustrated through the Florida Public Hurricane Loss Model (FPHLM), which estimates insured losses on residential buildings caused by hurricane events in Florida. To define the input exposure, and for model development, calibration, and validation purposes, the FPHLM teams accessed three main sources of data: county tax appraiser databases, National Flood Insurance Protection (NFIP) portfolios, and wind insurance portfolios. The data from these different sources were reformatted and processed, and the insurance databases were separately cross-referenced at the county level with tax appraiser databases. The FPHLM hazard teams assigned estimates of natural hazard intensity measure to each insurance claim. These efforts produced an integrated and more complete set of building descriptors for each policy in the NFIP and wind portfolios. The article describes the impact of these uncertainty reductions on the development and validation of the vulnerability models, and suggests avenues for data improvement. Lessons learned should be of interest to professionals involved in disaster risk assessment and management.

巨灾模型估算了危害，暴露和脆弱性相交处的风险。这些区域中的每一个都需要不同的数据源，这些数据源通常是不完整，不一致或完全丢失的。数据质量差是认知不确定性的来源，这会影响脆弱性模型以及巨灾模型的输出。本文确定了数据中认知不确定性的不同来源，并详细说明了减少这种不确定性的策略，尤其是通过识别，扩充和集成不同类型的数据。佛罗里达公共飓风损失模型（FPHLM）说明了这些挑战，该模型估计了佛罗里达飓风事件造成的住宅建筑物保险损失。要定义输入的曝光度并进行模型开发，为了进行校准和验证目的，FPHLM团队访问了三个主要数据源：县税收评估员数据库，国家洪水保险保护（NFIP）投资组合和风险投资组合。重新格式化和处理了来自这些不同来源的数据，并在县级与税务评估员数据库分别交叉引用了保险数据库。FPHLM危害小组为每个保险索赔分配了自然危害强度度量的估计值。这些努力为NFIP和风能投资组合中的每项政策生成了一套完整，更完整的建筑描述符。本文介绍了这些不确定性降低对漏洞模型的开发和验证的影响，并提出了改进数据的途径。