统一的0.25度网格基础设施关键极端天气为美国从1979年到2100年。

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Tao Sun, Chad Zanocco, June Flora, Aditi Sheshadri, Ram Rajagopal
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引用次数: 0

摘要

极端天气事件可能严重破坏关键基础设施,引发对电力、交通和基本服务的连锁反应。然而,标准的天气和气候数据集往往缺乏灾害评估所必需的专门变量。我们提供了一个统一的数据集,包括美国各地基础设施关键天气和气候变量,分辨率为0.25°,涵盖1979年至2100年的日或次日间隔。该数据集包括温度、露点、阵风、由雨、雪、冻雨或冰球、闪电和野火指标划分的降水。历史条件(1979-2023)是根据观测和再分析产品合成的,而未来预估来自14个CMIP6全球气候模式(历史、SSP245和SSP585实验)。基于物理和数据驱动的方法用于估计现有模型没有直接提供的变量。通过将这些变量整合到一个统一的数据集中,我们能够对过去和未来时期的天气相关基础设施风险进行一致、高分辨率的评估,支持在能源、交通、水资源、应急管理等领域的广泛应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unified 0.25-degree gridded infrastructure-critical extreme weather for the United States from 1979 to 2100.

Extreme weather events can severely disrupt critical infrastructure, triggering cascading effects on power, transportation, and essential services. However, standard weather and climate datasets often lack specialized variables necessary for hazard assessments. We present a unified dataset of infrastructure-critical weather and climate variables across the United States at 0.25° resolution, covering daily or sub-daily intervals from 1979 to 2100. The dataset includes temperature, dew point, wind gusts, precipitation partitioned by rain, snow, and freezing rain or ice pellets, lightning, and wildfire metrics. Historical conditions (1979-2023) are synthesized from observations and reanalysis products, while future projections are derived from 14 CMIP6 global climate models (historical, SSP245, and SSP585 experiments). Physically based and data-driven methods are used to estimate variables not directly provided by existing models. By integrating these variables into a single unified dataset, we enable consistent, high-resolution assessments of weather-related infrastructure risks across past and future periods, supporting wide-ranging applications in energy, transportation, water resources, emergency management, and beyond.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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