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

热带风暴和飓风可以产生超过5英尺的降雨量，就像2017年哈维飓风期间发生的那样。气候模型表明，未来气温和大气湿度将会增加，这些因素可能会导致更强的风暴，从而产生更多的降雨。为了更好地了解热带系统中水分与降雨的关系，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。将使用地面雷达和极地轨道卫星数据分析数十个热带风暴的雨带。对雨带的形状、大小和演变进行量化的指标被应用于这些观测。通过比较不同湿度环境下的雨带演变，这项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，该项目的结果将能够评估飓风模型预测在多大程度上准确地描述雨带结构，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，每个研究人员还将同时教授一门课程，提供最先进的方法实践培训，并为学生提供讨论三所大学之间研究的合作学习机会。该项目将结合地理和气象方法来调查关于热带气旋(TC)大小和结构的两个基本研究问题：(1)卫星和模拟数据集在表示云和降水结构方面的技巧如何，以及哪些基于三维对象的度量最能量化这些结构？(2)大尺度环境湿度如何影响TC雨带的发展和降雨产生？尽管有研究详细说明了天气尺度和热带气旋核心内环境湿度的重要性，但很少有研究结合雷达、卫星和模式资料来检验变化的湿度对影响热带气旋大小和结构(例如，通风和切变诱导的非对称环流)的天气和中尺度过程的影响。这项研究将为模式TC预报提供至关重要的见解。通过采用一种新的形状识别算法，该算法可在多个空间分辨率的数据集上进行扩展，该项目将识别雨带和雨带配置的变化，然后识别雨带，以及更广泛的TC空间范围，如何受到TC的水分环境的影响。这些分析结果将被用来建立基于大尺度环境湿度的TC大小和结构的多尺度概念性模式。最后，将通过与美国国家海洋和大气管理局飓风研究部的合作，将基于对象的衡量标准应用于评估当前运行和实验模型的降雨量预测。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.