Collaborative Research: Sea-state-dependent drag parameterization through experiments and data-driven modeling

合作研究：通过实验和数据驱动建模进行与海况相关的阻力参数化

• 批准号: 2404368
• 负责人: Kianoosh Yousefi
• 金额: $ 30.21万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-04-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2404368&HistoricalAwards=false
• 关键词: Collaborative; Research; Sea; state; dependent

The ocean covers nearly 70% of the Earth’s surface and plays a dominant role in the global climate. At the ocean interface, surface waves and their resulting dynamics regulate the transfers of momentum and scalars between the atmosphere and ocean and are thus fundamental in shaping the sea states and weather patterns, exerting a direct impact on many aspects of human life. Although we know surface waves must be fully integrated into weather and climate forecast models, we do not yet fully understand the fundamental processes that couple the surface waves with turbulent flows above and below the ocean surface. A better understanding of wind stress modulations by surface waves is required to reduce uncertainties and develop accurate predictive models. This project aims at advancing the current understanding of wind stress over ocean waves using combined high-resolution imaging and numerical simulations. The outcome of this work will result in tangible broader impacts and societal benefits beyond the scientific community. It will incorporate findings into educational materials for a comprehensive three-day air-sea interaction workshop.This collaborative project will integrate laboratory measurements of wind-wave interactions with a high-fidelity digital twin model of the laboratory system to develop a data-driven model for sea-surface drag. The specific objectives of the project are to (1) understand skin friction modulations induced by surface waves, (2) evaluate pressure drag through a high-fidelity digital twin model, and (3) develop a sea-state-dependent total surface drag parameterization. Laboratory measurements will provide an accurate description of surface skin friction drag but fall short when it comes to pressure forces. The digital twin model will augment the experimental setup by providing pressure forces. This integrated approach will provide unique insight into wave-induced modulations of the total wind stress (sum of tangential and pressure stresses at the air-water interface) under a range of wind-wave conditions. A data-driven sea-state-dependent surface flux parameterization will be developed by examining these modulations, leveraging recent advancements in machine learning technology. The model will be tailored for large-eddy simulations of wind over ocean wavefields in strongly forced conditions. This approach is expected to significantly advance the fundamental understanding of air-sea fluxes and lead to improved parameterizations of wind stress over the ocean.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.

海洋覆盖了地球表面的近70%，在全球气候中起着主导作用。在海洋界面，表面波及其产生的动态调节大气和海洋之间的动量和标量的转移，因此是塑造海洋状态和天气模式的根本，对人类生活的许多方面产生直接影响。虽然我们知道表面波必须完全集成到天气和气候预测模型中，但我们还没有完全理解将表面波与海洋表面上下的湍流耦合的基本过程。需要更好地了解表面波对风应力的调制，以减少不确定性并建立准确的预测模型。该项目旨在利用高分辨率成像和数值模拟相结合的方法，推进目前对海浪上风应力的理解。这项工作的成果将在科学界之外产生更广泛的影响和社会效益。该项目将把研究结果纳入为期三天的海气相互作用综合讲习班的教材，这一合作项目将把风浪相互作用的实验室测量结果与实验室系统的高保真数字孪生模型相结合，以开发一个由数据驱动的海面阻力模型。该项目的具体目标是（1）了解表面波引起的表面摩擦调制，（2）通过高保真数字孪生模型评估压力阻力，（3）开发一个依赖于海况的总表面阻力参数化。实验室测量将提供表面摩擦阻力的准确描述，但在压力方面却不尽如人意。数字孪生模型将通过提供压力来增强实验装置。这种综合的方法将提供独特的洞察波引起的调制的总风应力（切向应力和压力的总和在空气-水界面）在一系列的风浪条件下。将通过检查这些调制，利用机器学习技术的最新进展，开发数据驱动的海况依赖表面通量参数化。该模型将专门用于在强强迫条件下对海洋波场上的风进行大涡模拟。这一方法预计将大大推进海气通量的基本理解，并导致改进的海洋风应力参数化。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。