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

• 批准号: 1132576
• 负责人: John Molinari
• 金额: $ 44.4万
• 依托单位: SUNY at Albany
• 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 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1132576&HistoricalAwards=false
• 关键词: Collaborative; Research; Understanding; Tropical; Cyclone

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.

热带气旋强度的变化仍然是一个难以理解的过程。即使在实时预报的背景下，借助更全面的分析，有时仍然不可能解释为什么在明显相似的情况下，一个热带风暴或飓风将减弱，而另一个将变得更强。我们无法理解(更不用说预测)这些事件仍然是沿海社区的潜在危险，这些社区容易受到迅速增强的飓风登陆的可能性的影响。智力优势：总的来说，环境风切变值的增加越来越不利于热带气旋的加强。然而，出现意想不到(和不可预测的)加强的情况通常与中等风切变值有关。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加强之间的关系，重点是中等风切变的情况。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对高分辨率数值模式模拟的分析来实现。特别是，最近开发的一种建模技术允许在高度受控的环境下产生理想的模拟，这些环境可以被指定为与观测到的热带气旋周围的环境几乎相同。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取得了令人满意的一致，模拟就可以用来理解导致这些情况下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、温度和湿度剖面-独立变化。因此，在个案的基础上，将有可能分离出这些因素中的哪一个对风暴的加剧(或衰减)具有最大的控制作用。这将使我们对热带气旋周围的环境如何调节其结构和强度，以及强度变化在多大程度上是由内部或外部驱动的有了新的认识。广泛的影响：这一项目将有助于提高我们对热带气旋强度和结构变化的环境控制的理解。更多的了解将导致改进预测，不仅通过改进模型开发，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，并根据最近的实地项目获得的原始观测数据生成和传播新的数据集。该项目将支持两名研究生的教育和培训。