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AGS-PRF: Improving Tropical Cyclone Risk Assessment by Incorporating Storm Size Physics

AGS-PRF: Improving Tropical Cyclone Risk Assessment by Incorporating Storm Size Physics
AGS-PRF:通过结合风暴大小物理学改进热带气旋风险评估
批准号:
1331362
负责人:
Daniel Chavas
金额:
$8.6万
依托单位:
依托单位国家:
美国
项目类别:
Fellowship Award
财政年份:
2013
资助国家:
美国
项目状态:
已结题
起止时间:
2013-11-01 至 2015-10-31

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中文摘要
翻译
该博士后奖学金提供资金,通过将基于物理的热带气旋大小信息纳入现有风险建模框架,加强当前热带气旋(TC)风险评估方法。人们对台风的大小缺乏基本的了解,这导致无法充分量化台风登陆造成的损害风险。该项目的总体目标是对观测到的TC大小分布以及单个风暴在起源和生命周期中的大小进行熟练的物理解释,以便将其整合到TC风险评估框架中。该项目将综合利用观测、高分辨率天气预报模型输出、理想化建模研究和理论,阐明观测到的TC大小分布的物理基础。结果将用于发展风暴大小物理,并将其纳入全面的TC风险评估。将通过对观测和天气预报模式中的大小预测者的技能进行统计核查来评估实现这些目标的进展情况。通过更好地评估和潜在地减轻短期(如应急管理)和长期(如基础设施规划)的损害风险,项目将产生更广泛的社会影响。该项目还将加强科学家和风险管理者之间的合作努力,并支持一位职业生涯早期的科学家。
英文摘要
This postdoctoral fellowship award provides funding to enhance current tropical cyclone (TC) risk assessment methodology by incorporating physics-based TC size information into existing risk modeling frameworks. There is a lack of fundamental understanding of TC size and that leads to an inability to fully quantify the risk of damage from landfalling TCs. The overarching goal of the project will be to demonstrate a skillful physics-based explanation of the observed distribution of TC size, as well as of the size of an individual storm at genesis and during its lifecycle, for integration into a TC risk assessment framework. The project will make use of a combination of observations, high-resolution weather forecast model output, idealized modeling studies, and theory to elucidate the physics underlying the observed distribution of TC size. The results will then be used to develop and incorporate storm size physics into comprehensive TC risk assessments. Progress towards these objectives will be assessed by statistical verification of the skill of the size predictors in observations and weather forecast models. The broader societal impacts of the project will be through better assessment and potentially mitigation of damage risk in both the short (e.g. emergency management) and long-term (e.g. infrastructure planning). The project will also enhance collaborative efforts between scientists and risk managers and support an early-career scientist.
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