Collaborative Research: An Object-Oriented Approach to Assess the Rainfall Evolution of Tropical Cyclones in Varying Moisture Environments

协作研究：一种面向对象的方法来评估不同湿度环境下热带气旋的降雨演变

• 批准号: 2011981
• 负责人: Stephanie Zick
• 金额: $ 18.57万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2011981&HistoricalAwards=false
• 关键词: Collaborative; Research; Object; Oriented; Approach

Tropical storms and hurricanes can produce more than five feet of rain as occurred in 2017 during Hurricane Harvey. Climate models indicate increasing temperatures and atmospheric moisture in the future, factors which can lead to stronger storms that produce more rain. To better understand how moisture is tied to rainfall in tropical systems, it is essential to assess the structures that produce rain within the storm – the rainbands – and the impact of moisture on those rainbands. This project will use geographic methods to measure storm structure and analyze how atmospheric moisture (also referred to as humidity) affects that structure. Rainbands will be analyzed in dozens of tropical storms using ground-based radar and polar-orbiting satellite data. Metrics that quantify the shape, size, and evolution of rainbands are applied to these observations. By comparing rainband evolution in different moisture environments, this research will describe how rainband structural changes occur and the environmental moisture regimes that lead to high rain rates. Through collaboration with scientists from the National Oceanic and Atmospheric Administration (NOAA), this project’s results will enable assessment of how accurately hurricane model forecasts depict rainband structure, an assessment that will help improve hurricane rainfall predictions. In addition to funding graduate student research, each investigator will simultaneously teach a course that provides hands-on training in state-of-the art methods and includes collaborative learning opportunities for students to discuss research among the three universities.This project will integrate geographic and meteorological methods to investigate two fundamental research questions about tropical cyclone (TC) size and structure: (1) How skillful are satellite and modeling datasets in representing cloud and precipitation structure and which three-dimensional object-based metrics best quantify these structures? (2) How does large-scale environmental moisture impact TC rainband development and rainfall production? Despite research that details the importance of environmental moisture at the synoptic-scale and within the TC inner core, few studies have combined radar, satellite, and modeling data to examine the influence of variable moisture on synoptic and mesoscale processes that impact TC size and structure (e.g, ventilation and shear-induced asymmetric circulations). This research will provides crucial insight into model TC forecasts. By employing a novel shape-identification algorithm that is scalable across datasets with multiple spatial resolutions, this project will identify rainbands and tracks changes in rainband configuration to then identify how rainbands, and TC spatial extent more generally, are impacted by the TC’s moisture environment. The results from these analyses will be used to establish a multi-scale conceptual model of TC size and structure based on large-scale environmental moisture. Finally, object-based metrics will be applied to evaluate rainfall forecasts from current operational and experimental models by collaborating with the Hurricane Research Division of 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.

热带风暴和飓风可以产生超过5英尺的降雨，就像2017年飓风哈维那样。气候模型显示，未来气温和大气湿度会上升，这些因素可能导致更强的风暴，产生更多的降雨。为了更好地理解热带系统中湿度是如何与降雨联系在一起的，有必要评估在风暴中产生降雨的结构——雨带——以及湿度对这些雨带的影响。该项目将使用地理方法来测量风暴结构，并分析大气湿度（也称为湿度）如何影响该结构。将利用地面雷达和极轨卫星数据分析几十个热带风暴中的雨带。将雨带的形状、大小和演变量化的指标应用于这些观测。通过比较不同湿度环境下雨带的演变，本研究将描述雨带结构变化是如何发生的，以及导致高降雨率的环境湿度状况。通过与美国国家海洋和大气管理局（NOAA）的科学家合作，该项目的结果将有助于评估飓风模型预测对雨带结构的准确程度，这一评估将有助于改进飓风降雨预测。除了资助研究生的研究外，每位研究者还将同时教授一门课程，提供最先进方法的实践培训，并为学生提供在三所大学之间讨论研究的合作学习机会。该项目将整合地理和气象方法来调查两个关于热带气旋（TC）大小和结构的基本研究问题：(1)卫星和建模数据集在表示云和降水结构方面有多熟练，以及哪种基于物体的三维指标最能量化这些结构？(2)大尺度环境湿度如何影响TC雨带发展和降雨产生？尽管有研究详细说明了天气尺度和TC内核内环境湿度的重要性，但很少有研究将雷达、卫星和模拟数据结合起来，研究可变湿度对影响TC大小和结构的天气和中尺度过程的影响（例如，通风和剪切诱导的不对称环流）。这项研究将为模型TC预测提供重要的见解。通过采用一种新颖的形状识别算法，该算法可以跨多个空间分辨率的数据集进行扩展，该项目将识别雨带并跟踪雨带配置的变化，然后确定雨带和TC空间范围如何受到TC湿度环境的影响。这些分析结果将用于建立基于大尺度环境湿度的多尺度TC尺寸和结构概念模型。最后，通过与美国国家海洋和大气管理局飓风研究部门的合作，将基于对象的指标应用于评估来自当前操作和实验模型的降雨预报。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。