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: 2404369
• 负责人: Marco Giometto
• 金额: $ 29.96万
• 依托单位: Columbia University
• 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=2404369&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.

海洋覆盖了近70%的地球表面，在全球气候中发挥着主导作用。在海洋界面，表面波及其产生的动力学调节着大气和海洋之间的动量和标量传输，因此是塑造海洋状况和天气模式的基本因素，对人类生活的许多方面产生直接影响。尽管我们知道表面波必须完全融入天气和气候预报模式，但我们还没有完全了解将表面波与海洋表面上下的湍流耦合起来的基本过程。需要更好地了解表面波对风应力的调制，以减少不确定性并开发准确的预测模型。该项目旨在利用高分辨率成像和数值模拟相结合的方法，促进目前对海浪上的风应力的理解。这项工作的结果将在科学界之外产生切实的、更广泛的影响和社会效益。它将把研究结果纳入为期三天的综合海气相互作用工作坊的教育材料中。这个合作项目将把实验室测量的风浪相互作用与实验室系统的高保真数字孪生模式结合起来，开发一个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式评估压力阻力，以及(3)发展依赖于海况的总表面阻力参数。实验室测量将提供表面表面摩擦阻力的准确描述，但当涉及到压力时，测量结果将达不到要求。数字孪生模型将通过提供压力来增强实验装置。这一综合方法将提供独特的洞察在各种风浪条件下由波浪引起的总风应力(空气-水界面的切向应力和压力应力的总和)的调制。将利用机器学习技术的最新进展，通过检查这些调制来开发数据驱动的依赖于海况的表面通量参数化。该模型将为强强迫条件下海洋波浪场上的大涡模拟量身定做。这一方法预计将显著促进对海-气通量的基本理解，并导致改善海洋风应力的参数化。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。