RAPID: 2018 Hurricane Season -- Assessing the Role of Antecedent Land State on Hurricane Post-landfall Rainfall

RAPID：2018 年飓风季节——评估先前陆地状态对飓风登陆后降雨量的作用

• 批准号: 2228004
• 负责人: Dev Niyogi
• 金额: $ 13.03万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2022-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2228004&HistoricalAwards=false
• 关键词: RAPID; 2018; Hurricane; Season; Assessing

Hurricane Florence dropped an extraordinary amount of rainfall on parts of the eastern United States, particularly in coastal North Carolina. It has been theorized that wet land conditions can provide a moisture feedback that sustains landfalling hurricanes and tropical storms. The research team will collect data and perform analyses to determine whether this mechanism played a role in the progression of Florence and how it may impact other tropical systems. The end goal of the research is to improve the forecasts of landfalling hurricanes and the various hazards they pose. The project will also help to train the next generation of scientists.This rapid-response award will seek to determine the role of land-surface change during a tropical cyclone landfall on resultant rainfall characteristics. Hurricane Florence will serve as a case study to address the so-called "Brown Ocean Effect" where wet land provides moisture feedback to sustain landfalling tropical systems. In this project, the researchers will develop an observational analysis of rainfall and land state during the Florence time period, develop a synthesis of the antecedent and post-landfall land conditions with a focus on the moisture transport and rainfall/water budget, conduct numerical experiments using WRF to test the hypothesis regarding the role of antecedent land state in affecting the inland rainfall from Florence, and compare Hurricane Florence to other similar systems to help refine the conceptual processes involved. The time-sensitivity of the project lies with the collection of datasets and the distribution of analyses to the scientific community and collaborators from National Oceanic and Atmospheric Administration (NOAA).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

佛罗伦萨飓风在美国东部，特别是在北卡罗来纳州沿海地区降低了大量的降雨量。 从理论上讲，潮湿的陆地条件可以提供水分反馈，从而维持飓风和热带风暴。 研究团队将收集数据并进行分析，以确定这种机制是否在佛罗伦萨的发展以及如何影响其他热带系统中发挥作用。 该研究的最终目标是改善登陆飓风及其构成的各种危害的预测。 该项目还将有助于培训下一代科学家。这一快速响应奖将旨在确定在热带气旋登陆过程中土地表面变化对导致降雨特征的作用。 佛罗伦萨飓风将作为一个案例研究，以解决所谓的“棕色海洋效应”，其中湿土地可提供水分反馈以维持登陆的热带系统。 在该项目中，研究人员将在佛罗伦萨时间期间对降雨和土地状态进行观察性分析，开发出综合前期和地面后的土地状况，重点是进行水分运输和降雨/水预算，使用WRF进行数值实验，以测试与佛罗伦萨降临型局部降临的相似概念的假设，以影响佛罗伦萨的其他概念，并影响佛罗伦萨的其他概念，并反映出佛罗伦萨的其他概念的作用。涉及的过程。 该项目的时间敏感性在于数据集的收集以及分析向科学界和来自国家海洋和大气管理局的合作者（NOAA）（NOAA）的分配。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估来通过评估来支持的。

项目成果

Influence of Land Cover and Soil Moisture based Brown Ocean Effect on an Extreme Rainfall Event from a Louisiana Gulf Coast Tropical System

• DOI: 10.1038/s41598-019-53031-6
• 发表时间: 2019-10
• 期刊: Scientific Reports
• 影响因子: 4.6
• 作者: U. Nair;E. Rappin;E. Foshee;Warren J. Smith;R. Pielke;R. Mahmood;J. Case;C. Blankenship;Marshall Shepherd;J. Santanello;D. Niyogi

Improved Simulation of Monsoon Depressions and Heavy Rains From Direct and Indirect Initialization of Soil Moisture Over India

• DOI: 10.1029/2020jd032400
• 发表时间: 2020-07
• 期刊: Journal of Geophysical Research: Atmospheres
• 作者: K. Osuri;R. Nadimpalli;Kumar Ankur;H. Nayak;U. C. Mohanty;A. Das;D. Niyogi

Harnessing risk-informed data for disaster and climate resilience

• DOI: 10.1016/j.pdisas.2022.100254
• 发表时间: 2022-10-08
• 期刊: PROGRESS IN DISASTER SCIENCE
• 影响因子: 6.3
• 作者: Fakhruddin，Bapon;Kirsch-Wood，Jenty;Frolova，Nina
• 通讯作者: Frolova，Nina

Impact of Urban Representation on Simulation of Hurricane Rainfall

• DOI: 10.1029/2023gl104078
• 发表时间: 2023-11
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: Pratiman Patel;Kumar Ankur;S. Jamshidi;Alka Tiwari;R. Nadimpalli;N. Busireddy;Samira Safaee;K. Osuri;S. Karmakar;Subimal Ghosh;Daniel Aliaga;James Smith;Frank Marks;Zong‐Liang Yang;D. Niyogi