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在2017年哈维飓风期间产生超过五英尺的降雨。气候模型表明将来温度和大气水分升高，可能导致更强烈的风暴会导致更多降雨。为了更好地了解热带系统中的水分与降雨息息相关，必须评估在暴风雨中产生降雨的结构（雨带）以及水分对这些雨带的影响。项目将使用地理方法来测量风暴结构并分析大气水分（也称为湿度）如何影响该结构。使用地面雷达和极地轨道卫星数据，将在数十个热带风暴中分析雨带。量化雨带的形状，大小和演变的指标被应用于这些观察结果。通过比较不同水分环境中的雨带的演变，这项研究将描述雨带结构变化以及导致降雨速率高的环境水分状态。通过与国家海洋和大气管理局（NOAA）的科学家合作，该项目的结果将能够评估森林森林人描绘雨带结构的准确模型，该评估将有助于改善飓风降雨的预测。除了为学生的研究提供资金外，每个研究者还将简单地教一门课程，该课程提供最先进的方法，包括学生在三所大学之间讨论研究的协作学习机会。该项目将整合地理和气象学方法，以整合两个基本研究问题，以研究两种基本研究问题，这些问题是热带凝聚力和结构的三个熟练和结构：（1）卫星界和模型：（1）基于对象的指标可以最好地量化这些结构？ （2）大规模环境水分如何影响TC雨带的发展和降雨产生？尽管研究详细介绍了在天气尺度和TC内核中环境水分的重要性，但很少有研究将雷达，卫星和建模数据结合在一起，以检查可变水分对影响TC大小和结构的可变水分对TC大小和结构的影响（例如，型和剪切诱导的腹腔诱导的循环）。这项研究将为TC森林提供至关重要的见解。通过采用一种具有多个空间分辨率的数据集可扩展的新型形状识别算法，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的湿度环境的影响。这些分析的结果将用于建立基于大规模环境水分的TC大小和结构的多尺度概念模型。最后，将通过与NOAA飓风研究部合作来评估当前运营和实验模型的降雨森林。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响标准通过评估来评估的支持。