气候风险建模平台 CLIMADA 中的影响函数校准模块

Lukas Riedel, Chahan M. Kropf, Timo Schmid
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引用次数: 0

摘要

影响函数可模拟暴露在天气和气候灾害中的人员和资产的脆弱性。根据概率危害事件集或天气预报,可以分别计算相关风险或影响。由于影响函数很难在更大的时空尺度上确定,因此通常使用过去事件的危害、暴露和影响数据对其进行校准。我们介绍了一个模块,用于使用贝叶斯优化等成熟的校准技术,根据这些数据校准影响函数。该模块作为气候风险建模平台 CLIMADA 的 Python 子模块 climada.util.calibrate(Aznar-Siguan 等,2023 年)实现,并完全集成到其工作流程中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Module for Calibrating Impact Functions in the Climate Risk Modeling Platform CLIMADA
Impact functions model the vulnerability of people and assets exposed to weather and climate hazards. Given probabilistic hazard event sets or weather forecasts, they enable the computation of associated risks or impacts, respectively. Because impact functions are difficult to determine on larger spatial and temporal scales of interest, they are often calibrated using hazard, exposure, and impact data from past events. We present a module for calibrating impact functions based on such data using established calibration techniques like Bayesian optimization. It is implemented as Python submodule climada.util.calibrate of the climate risk modeling platform CLIMADA (Aznar-Siguan et al., 2023), and fully integrates into its workflow.
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