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Collaborative Research: Understanding Tropical Cyclone Evolution in Wind Shear through a Synthesis of Observational Data Sets and Idealized Simulations

合作研究：通过综合观测数据集和理想化模拟了解风切变中的热带气旋演化

基本信息

批准号：
1132646
负责人：
David Nolan
金额：
$ 46.55万
依托单位：
University of Miami
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2011
资助国家：
美国
起止时间：
2011-09-15 至 2016-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1132646&HistoricalAwards=false
关键词：
Collaborative Research Understanding Tropical Cyclone

项目摘要

Tropical cyclone intensity change remains a difficult process to understand. Even beyond the context of real-time forecasting and with the benefit of a more comprehensive analysis, it is still sometimes impossible to explain why, in apparently similar situations, one tropical storm or hurricane will weaken while another will become stronger. Our inability to understand (let alone forecast) these events remains as a potential danger to coastal communities which are vulnerable to the possibility of a rapidly intensifying hurricane making landfall.Intellectual merit: In general, increasing values of environmental wind shear are increasingly less favorable for tropical cyclone intensification. However, cases with unexpected (and unforecasted) intensification are often associated with moderate values of wind shear. The goal of this research is to better understand the relationships between wind shear, tropical cyclone structure, and tropical cyclone intensification, with an emphasis on cases of moderate wind shear. This will be achieved through a synthesis of observational data sets obtained in past and recent field programs as well as analyses of high-resolution numerical model simulations. In particular, a recently developed modeling technique allows for the production of idealized simulations with highly controlled environments that can be specified to be nearly identical to the environments around observed tropical cyclones. These simulations will be further validated against observations from within the cyclones. Once satisfactory agreement between the observed and simulated storms is achieved, the simulations can be used to understand the physical processes that caused intensity and structure change for those cases. Furthermore, the idealized modeling technique allows all aspects of the surrounding environment - the wind, temperature, and humidity profiles, to be varied independently. Thus it will be possible, on a case-by-case basis, to isolate which of these factors have the most control over intensification (or decay) of the storm. This will lead to a new understanding of how the environment surrounding a tropical cyclone modulates its structure and intensity, and to what extent intensity changes are either internally or externally driven.Broader impacts: This project will lead to improvement of our understanding of environmental controls of tropical cyclone intensity and structure change. Greater understanding will lead to improved forecasting, not only through improved model development, but also through the identification of new or improved forecasting parameters based on the environmental soundings. The project will put to use existing datasets as well as lead to the generation and dissemination of new data sets derived from the raw observations obtained from recent field projects. The project will support the education and training of two graduate students.

热带气旋强度变化仍然是一个难以理解的过程。即使在实时预报之外，并借助更全面的分析，有时仍然无法解释为什么在明显相似的情况下，一个热带风暴或飓风会减弱，而另一个却会增强。我们无法理解（更不用说预测）这些事件仍然是一个潜在的危险，沿海社区是脆弱的可能性，迅速加强飓风登陆。知识价值：在一般情况下，环境风切变值的增加越来越不利于热带气旋强化。然而，意想不到的（和未预测的）增强的情况下，往往与中等值的风切变。这项研究的目的是更好地了解风切变、热带气旋结构和热带气旋增强之间的关系，重点是中度风切变的情况。这将通过综合过去和最近的实地方案中获得的观测数据集以及分析高分辨率数值模型模拟来实现。特别是，最近开发的建模技术允许生产的理想化的模拟与高度控制的环境，可以指定为几乎相同的观察到的热带气旋周围的环境。这些模拟将根据气旋内部的观测结果进一步验证。一旦观测到的风暴和模拟的风暴之间达到令人满意的一致性，模拟就可以用来理解这些情况下导致强度和结构变化的物理过程。此外，理想化的建模技术允许周围环境的所有方面-风，温度和湿度分布，可以独立变化。因此，在个案基础上，将有可能分离出这些因素中哪一个对风暴的增强（或减弱）具有最大的控制作用。这将导致一个新的理解如何围绕热带气旋的环境调节其结构和强度，以及在何种程度上强度变化是内部或外部驱动。更广泛的影响：这个项目将导致我们的热带气旋强度和结构变化的环境控制的理解提高。加深了解将导致改进预测，不仅是通过改进模型开发，而且通过根据环境探测确定新的或改进的预测参数。该项目将利用现有的数据集，并最终生成和传播从最近实地项目获得的原始观测数据中产生的新数据集。该项目将支助两名研究生的教育和培训。