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PREEVENTS Track 2: Collaborative Research: More resilient coastal cities and better hurricane forecasts through multi-scale modeling of extreme winds in the urban canopy

预防事件轨道 2：协作研究：通过对城市冠层极端风的多尺度建模，增强沿海城市的复原力和更好的飓风预报

基本信息

批准号：
1663978
负责人：
Jason Knievel
金额：
$ 117.97万
依托单位：
University Corporation For Atmospheric Res
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-01 至 2022-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1663978&HistoricalAwards=false
关键词：
PREEVENTS Track Collaborative Research More

项目摘要

PREEVENTS Track 2: Collaborative ResearchMore resilient coastal cities and better hurricane forecasts through multi-scale modeling of extreme winds in the urban canopyAward 1663978When a hurricane arrives at the coast, what happens to wind near the ground depends on what is covering the ground. Whether that covering is grass, trees, pavement, a scattering of small houses, or a densely packed cluster of skyscrapers will affect the wind?s speed, direction, and gustiness. Such effects can vary from one part of a city to another, even from one side of a building to another. Experts do not understand such variations as well as they would like. Computer models used to forecast weather have become more accurate and detailed over the decades but provide little information about how wind speeds and wind gusts vary across city and suburban landscapes. The goal of this project is to use state-of-the-art computer models to learn more about and to improve our ability to predict how buildings disrupt and modify the wind beneath landfalling hurricanes, from just above the ground to near the tops of buildings. Results will foster better ways to design and locate buildings and other infrastructure near coasts, better ways to forecast hurricane-force winds, better ways to protect lives and property in the face of impending landfall, and better ways to respond immediately afterward. The project will include 1 graduate student to help provide for the next generation of researchers in this area. Through computer simulations during the project?s first phase, a range of standard building types in different layouts will be subjected to strikes from archetypal hurricanes. The simulations will reveal the most important, fundamental physical processes at play on scales that are usually invisible to typical weather forecast models yet are critical to the variation of wind in urban and suburban landscapes. Armed with the knowledge gained in that first phase, during phase II researchers will then improve the components of the industry-standard Weather Research and Forecasting (WRF) Model that are most directly responsible for predicting wind in developed areas. Those components were designed to work best for the lower wind speeds typical of most weather, not for hurricane-force winds. The improved WRF Model from phase II of the project will then be used in phase III to simulate the actual landfall of at least one historical hurricane. Researchers will retrieve records of the hurricane?s weather observations and wind damage to validate the improved model, evaluating how its representation of hurricane-force winds in urban and suburban areas has been enhanced. In phase IV, researchers will review more past hurricanes that made landfall in the U.S. and Mexico to select a subset of storms for additional simulations. Conditions will be perturbed to change the characteristics of those actual storms, generating a collection, often called an ensemble, of synthetic hurricanes that spans a realistically wide range of types. Finally, in phase V of the project, results from phase IV will be combined with socioeconomic information and mathematical equations for wind damage to create maps of how the vulnerability to high winds in hurricanes varies within the cities selected in phase IV.

展望2：合作研究通过对城市树冠中的极端风进行多尺度建模，提高沿海城市的复原力和飓风预报能力奖1663978当飓风到达海岸时，地面附近的风会发生什么取决于地面上覆盖的是什么。无论覆盖的是草地、树木、人行道、零星的小房子，还是密集的摩天大楼群，都会影响风速、方向和阵风吗？S。这样的影响可能会因城市的不同而不同，甚至从建筑物的一边到另一边也是如此。专家们并不像他们所希望的那样理解这样的变化。几十年来，用于预报天气的计算机模型变得更加准确和详细，但提供的关于城市和郊区风速和阵风变化的信息很少。这个项目的目标是使用最先进的计算机模型来更多地了解并提高我们预测建筑物如何扰乱和修改正在登陆的飓风下的风的能力，从地面上到建筑物顶部附近。结果将促进更好地设计和定位沿海附近的建筑和其他基础设施，更好地预测飓风强度，更好地在面临即将登陆时保护生命和财产，以及更好地在登陆后立即做出反应。该项目将包括1名研究生，以帮助培养这一领域的下一代研究人员。通过计算机模拟，在S项目第一阶段，一系列不同布局的标准建筑类型将受到原型飓风的袭击。这些模拟将揭示在尺度上发挥作用的最重要、最基本的物理过程，这些过程通常对典型的天气预报模式不可见，但对城市和郊区景观中的风的变化至关重要。在第一阶段获得的知识的基础上，在第二阶段，研究人员将改进行业标准天气研究和预报(WRF)模式中最直接负责预测发达地区风向的组件。这些组件被设计成在大多数天气中典型的较低风速下工作得最好，而不是飓风强度的风。然后，项目第二阶段改进的WRF模式将在第三阶段用于模拟至少一次历史飓风的实际登陆。研究人员将检索S飓风的天气观测和风灾记录，以验证改进的模型，评估其对城市和郊区飓风风力的表示如何得到增强。在第四阶段，研究人员将回顾更多过去登陆美国和墨西哥的飓风，以选择风暴的一个子集进行额外的模拟。条件将受到干扰，以改变那些实际风暴的特征，产生一组合成飓风，通常被称为集合，跨越现实中广泛的类型。最后，在项目的第五阶段，第四阶段的结果将与社会经济信息和风灾的数学方程结合起来，以创建在第四阶段选定的城市中飓风易受大风影响的脆弱性如何变化的地图。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Targeted artificial ocean cooling to weaken tropical cyclones would be futile

DOI：
10.1038/s43247-022-00519-1
发表时间：
2022-08
期刊：
Communications Earth & Environment
影响因子：
7.9
作者：
J. Hlywiak;D. Nolan
通讯作者：
J. Hlywiak;D. Nolan

The Evolution of Asymmetries in the Tropical Cyclone Boundary Layer Wind Field during Landfall

热带气旋登陆期间边界层风场的不对称演化

DOI：
10.1175/mwr-d-21-0191.1
发表时间：
2022
期刊：
Monthly Weather Review
影响因子：
3.2
作者：
Hlywiak, James;Nolan, David S.
通讯作者：
Nolan, David S.

Evaluation of the Surface Wind Field over Land in WRF Simulations of Hurricane Wilma (2005). Part I: Model Initialization and Simulation Validation

DOI：
10.1175/mwr-d-20-0199.1
发表时间：
2021-03-01
期刊：
MONTHLY WEATHER REVIEW
影响因子：
3.2
作者：
Nolan, David S.;Mcnoldy, Brian D.;Yunge, Jimmy
通讯作者：
Yunge, Jimmy

Addition of Multilayer Urban Canopy Models to a Nonlocal Planetary Boundary Layer Parameterization and Evaluation Using Ideal and Real Cases

DOI：
10.1175/jamc-d-19-0142.1
发表时间：
2020-08
期刊：
Journal of Applied Meteorology and Climatology
影响因子：
3
作者：
E. Hendricks;J. Knievel;Yi Wang
通讯作者：
E. Hendricks;J. Knievel;Yi Wang

Evaluation of the Surface Wind Field over Land in WRF Simulations of Hurricane Wilma (2005). Part II: Surface Winds, Inflow Angles, and Boundary Layer Profiles

WRF 模拟飓风威尔玛 (2005) 中陆地表面风场的评估。

DOI：
10.1175/mwr-d-20-0201.1
发表时间：
2021
期刊：
Monthly Weather Review
影响因子：
3.2
作者：
Nolan, David S.;McNoldy, Brian D.;Yunge, Jimmy;Masters, Forrest J.;Giammanco, Ian M.
通讯作者：
Giammanco, Ian M.