Unique Turbulence Dynamics in Hurricane Boundary Layers and Improving Their Parameterizations in Numerical Weather Prediction Models

飓风是迄今为止美国历史上损失最大的自然灾害，造成了数十亿美元的损失。海洋变暖和气候变化可以通过增加未来主要飓风的频率和强度来加剧热带气旋的破坏。仅最近的四次飓风——卡特里娜、桑迪、玛丽亚和哈维——就造成了超过4500亿美元的损失和大约5000人死亡。因此，科学界必须更好地了解和预测飓风动力学及其湍流风，以便有效地减轻其经济后果。虽然湍流在飓风演变中起着重要的作用，但它既没有被完全理解，也没有在飓风流动中被参数化。考虑到未来飓风对人类的显著影响，以及缺乏可靠的湍流尺度模型来模拟这种旋转气流，一个高保真度的飓风模型现在是必不可少的。本项目旨在通过数值天气预报（NWP）模式和观测的结合来解决这一知识差距，推动对飓风湍流的理解，并开发实用的方法来改进NWP模式的飓风预报。该研究为湍流理论和飓风流模拟开辟了新领域。特别是，项目的驱动假设是“由于飓风边界层的旋转和大的罗斯比数（离心/科里奥利力），飓风边界层的湍流动力学与典型大气边界层有显著不同；因此，现有的NWPs湍流模式限制了飓风预报的准确性。”这一假设将通过回答以下开放的研究问题来验证：1)飓风如何调节HBL的特征混合长度尺度和湍流动力学？2)与典型的ABLs相比，NWPs中HBL的水平和垂直湍流通量应该如何参数化？为了回答这些问题，将采用高保真大涡模拟（LESs）、NWPs和观测的独特组合。初步结果支持了该项目的中心假设，证明了与典型ABLs相比，HBLs的湍流结构和能谱有显著不同，并且在改变当前湍流模型后，NWP的飓风预报有了实质性的改进。因此，解决上述问题将通过阐明飓风中不同的湍流机制来推进物理和动态气象学领域。该项目其他值得注意的预期成果包括高分辨率HBLs的广泛数据集，专门为真实飓风设计的具有旋转校正的新型基于物理的湍流闭包，以及改进的飓风模拟数据集。该奖项反映了美国国家科学基金会的法定使命，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。

Hurricanes have been the costliest natural disaster in US history thus far by causing billions of dollars in damage. Ocean warming and climate change can exacerbate tropical cyclone destruction by increasing the frequency and intensity of future major hurricanes. Only four recent hurricanes — Katrina, Sandy, Maria, and Harvey — resulted in more than $450B in damages and about 5,000 fatalities. Thus, it is imperative for the scientific community to better understand and forecast hurricane dynamics and its turbulent winds in order to effectively mitigate their economic ramifications. Although turbulence plays a significant role in hurricane evolution, it is neither thoroughly understood nor parameterized in hurricane flows. Given the remarkable impacts of future hurricanes on humans and the lack of a reliable turbulence scale model for such rotating flows, a high-fidelity hurricane model is now essential. This project aims to address this knowledge gap using a combination of numerical weather prediction (NWP) models and observations to thrust forward the understanding of hurricane turbulence, and to develop practical methodologies for improving hurricane forecasts in NWP models.The research provides pathways to new frontiers in turbulence theory and modeling of hurricane flows. In particular, the driving hypothesis of the project is “turbulence dynamics in hurricane boundary layers (HBLs) are significantly different from typical atmospheric boundary layers (ABLs) due to rotation in HBLs and their large Rossby number (centrifugal/Coriolis force); therefore, existing turbulence models in NWPs limit the accuracy of hurricane forecasts.” This hypothesis will be tested by answering these open research questions 1) How do hurricanes modulate the characteristic mixing length scales and turbulence dynamics in the HBL? and 2) How should the horizontal and vertical turbulent fluxes of an HBL be parameterized in NWPs compared to typical ABLs? To answer these questions, a unique combination of high-fidelity large-eddy simulations (LESs), NWPs, and observations will be employed. The preliminary results support the project’s central hypothesis by demonstrating remarkably different turbulence structures and energy spectra in HBLs when compared to typical ABLs, and substantial improvements in NWP’s hurricane forecasts when current turbulence models are altered. Hence, addressing the above questions will advance the field of physical and dynamic meteorology by elucidating the distinctive turbulence mechanisms in hurricanes compared to conventional much-studied ABLs. Other notable expected outcomes of the project include an extensive dataset of high-resolution LESs of HBLs, new physics-based turbulence closures with rotation correction that are specifically designed for real hurricanes, and a dataset of improved hurricane simulations.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.