Environmental hazard events such as extra‐tropical cyclones or windstorms that develop in the North Atlantic can cause severe societal damage. Environmental hazard is quantified by the hazard footprint, a spatial area describing potential damage. However, environmental hazards are never directly observed, so estimation of the footprint for any given event is primarily reliant on station observations (e.g., wind speed in the case of a windstorm event) and physical model hindcasts. Both data sources are indirect measurements of the true footprint, and here we present a general statistical framework to combine the two data sources for estimating the underlying footprint. The proposed framework extends current data fusion approaches by allowing structured Gaussian process discrepancy between physical model and the true footprint, while retaining the elegance of how the "change of support" problem is dealt with. Simulation is used to assess the practical feasibility and efficacy of the framework, which is then illustrated using data on windstorm Imogen.

中文翻译：

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与高斯过程进行数据融合以评估环境危害事件

在北大西洋发生的温带气旋或暴风雨等环境危害事件可能会造成严重的社会破坏。环境危害可通过危害足迹（一个描述潜在损害的空间区域）进行量化。但是，从来没有直接观察到环境危害，因此对任何给定事件的足迹估算都主要取决于站点的观测值（例如，暴风雨事件中的风速）和物理模型的后兆。这两个数据源都是对真实足迹的间接度量，在这里，我们提供了一个通用的统计框架，将两个数据源结合起来以估算潜在的足迹。提议的框架通过允许物理模型与实际足迹之间的结构化高斯过程差异扩展了当前的数据融合方法，同时保留了如何处理“支持更改”问题的优雅。仿真用于评估该框架的实际可行性和有效性，然后使用暴风雨Imogen上的数据进行说明。