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Environmental controls over tropical cyclone formation

对热带气旋形成的环境控制

基本信息

批准号：
2309929
负责人：
Chanh Kieu
金额：
$ 35.71万
依托单位：
Indiana University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2023
资助国家：
美国
起止时间：
2023-06-01 至 2026-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2309929&HistoricalAwards=false
关键词：
Environmental controls over tropical cyclone

项目摘要

Tropical cyclones (TCs, also known as hurricanes in the North Atlantic basin) are among the major extreme weather events that pose a high risk to the economy and society every year. Despite great progress in studying TC development over the last several decades, our current understanding of TC processes is still inadequate. A direct consequence of this lack of understanding of TC formation is our limited capability in predicting TC formation in practice. Thus, improving the understanding of TC formation processes and TC subsequent development is of significance for both the advance of science and social safety and risk management. Using numerical simulations along with recent machine learning techniques, this project introduces a new approach to TC research that can help unfold the dominant mechanisms underlying TC formation in different ocean basins. Unlike the traditional vortex tracking methods in the current TC research and operation, the approach in this project offers an alternative way to search for new environmental factors governing TC formation. By exploring the practicality of these machine learning methods for TC research, this project will not only introduce an innovative way of studying tropical cyclogenesis but also help train a new generation of workforce in the area of artificial intelligence applications for atmospheric research. This project combines several new modelling and machine learning methods to investigate two key scientific questions: 1) what are the key environmental factors that control different TC formation pathways in different ocean basins, and 2) whether global tropical cyclogenesis possesses an episodic development, and if so, what environmental factors determine the frequency of this global TC formation. Using a combination of model simulations, physical diagnostic analyses, and machine learning algorithms, this project will look into major environmental conditions and their dynamical constraints in the tropical atmosphere that are insufficiently represented in the current TC genesis potential index framework. These environmental constraints will help determine different favorable conditions for TC formation and how to use these factors for studying the shift of TC climatology in the past and future climates. The outcomes from this project will also bring more insights into the physical mechanisms behind the maximum capacity of the tropical atmosphere in generating TCs annually in our current Earth’s atmosphere.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.

热带气旋（TC，也称为北大西洋盆地的飓风）是每年对经济和社会构成高风险的主要极端天气事件之一。尽管在过去的几十年里，对TC发展的研究取得了很大进展，但我们目前对TC过程的理解仍然不足。对TC形成缺乏了解的直接后果是我们在实际中预测TC形成的能力有限。因此，提高对TC形成过程和后续发展的认识对于科学进步和社会安全以及风险管理都具有重要意义。利用数值模拟沿着最新的机器学习技术，该项目引入了一种新的TC研究方法，可以帮助揭示不同海洋盆地TC形成的主导机制。与目前TC研究和运行中的传统涡旋跟踪方法不同，本项目的方法提供了一种寻找控制TC形成的新环境因素的替代方法。通过探索这些机器学习方法在热带气旋研究中的实用性，该项目不仅将引入研究热带气旋生成的创新方法，还将帮助培训人工智能应用于大气研究领域的新一代劳动力。该项目结合了几种新的建模和机器学习方法来研究两个关键的科学问题：1）控制不同海洋盆地中不同TC形成途径的关键环境因素是什么，以及2）全球热带气旋生成是否具有情景发展，如果是这样，什么环境因素决定了这种全球TC形成的频率。利用模型模拟，物理诊断分析和机器学习算法的组合，该项目将研究热带大气中的主要环境条件及其动力学约束，这些条件在当前的TC成因潜力指数框架中没有得到充分的体现。这些环境约束将有助于确定不同的有利条件，TC的形成，以及如何利用这些因素来研究TC的气候变化的过去和未来的气候。该项目的成果也将使我们更深入地了解热带大气在当前地球大气中每年产生TC的最大能力背后的物理机制。该奖项反映了NSF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。