Collaborative Research: Framework: Improving the Understanding and Representation of Atmospheric Gravity Waves using High-Resolution Observations and Machine Learning

合作研究：框架：利用高分辨率观测和机器学习提高对大气重力波的理解和表示

• 批准号: 2004512
• 负责人: M Joan Alexander
• 金额: $ 106.14万
• 依托单位: NorthWest Research Associates, Incorporated
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2004512&HistoricalAwards=false
• 关键词: Collaborative; Research; Framework; Improving; Understanding

Geophysical gravity waves are a ubiquitous phenomenon in Earth’s atmosphere and ocean, made possible by the interaction of gravity with a stratified, or layered fluid. They are excited in the atmosphere when winds flow over mountains, by thunderstorms and other strong convective systems, and when winter storms intensify. Gravity waves play an important role in the momentum and energy balance of the atmosphere, with direct impacts on surface weather and climate through their effect on the variability of key features of the climate system such as the jet streams and stratospheric polar vortices. These waves present a challenge to weather and climate prediction: waves on scales of 100 meters to 100 kilometers can neither be systematically measured with conventional observational systems, nor properly resolved in global atmospheric models. As a result, these waves must be represented, or approximated, based on the resolved flow that can be directly simulated. Current representations of gravity waves are severely limited by computational necessity and the scarcity of observations, leading to inaccuracies or uncertainties in short term weather and long term climate predictions. The objective of this project is to leverage unprecedented observations from Loon high altitude balloons and use specialized high resolution computer simulations and machine learning techniques to develop accurate, data-informed representation of gravity waves. The outcomes of this project are expected to result in better weather and climate models, thus improving short term forecasts of weather extremes and long term climate change projections, which have substantial societal benefits. Furthermore, the project will support the training of 3 Ph.D. students, 4 postdocs, and 10 undergraduate summer researchers to work at the intersection of atmospheric dynamics, climate modeling, and data science, thus preparing the next generation of scientists for interdisciplinary careers.The project will deliver two key advances. First, it will open up a new data source to constrain gravity wave momentum transport in the atmosphere. Loon LLC has been launching super pressure balloons since 2013 to provide global internet coverage. Very high resolution position, temperature, and pressure observations (taken every 60 seconds) are available from thousands of flights. This provides an unprecedented source of high resolution observations to constrain gravity wave sources and propagation. The project will process the balloon measurements and, in concert with novel high resolution simulations, establish a publicly available dataset to open up a potentially transformational resource for observationally constrained assessment of gravity wave sources, propagation, and breaking. The second transformation will be using machine learning techniques to develop computationally feasible representations of momentum deposition by gravity waves. Current physics-based representations only account for vertical propagation of the waves (i.e., they are one dimensional) and ignore their horizontal propagation. Using the data based on the Loon measurements and high resolution models, one and three dimensional data driven representations will be developed to more accurately and efficiently represent the effects of gravity waves in weather and climate models. These novel representations will be implemented in idealized atmospheric models to study the role of gravity waves in the variability of the extratropical jet streams, the Quasi Biennial Oscillation (a slow variation of the winds in the tropical stratosphere) and the polar vortex of the winter stratosphere, enabling better understanding their response to increased atmospheric greenhouse gas concentrations.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.

地球物理重力波是地球大气层和海洋中普遍存在的现象，可能是重力与分层或分层流体的相互作用。 当风吹过山脉，雷暴和其他强对流系统，以及冬季风暴加剧时，它们在大气中被激发。 重力波在大气的动量和能量平衡中发挥着重要作用，通过影响急流和平流层极涡等气候系统关键特征的变化，对地面天气和气候产生直接影响。 这些波对天气和气候预测提出了挑战：100米到100公里尺度的波既不能用传统的观测系统进行系统测量，也不能在全球大气模型中得到适当的解决。因此，这些波必须基于可以直接模拟的解析流来表示或近似。目前重力波的表示受到计算必要性和观测不足的严重限制，导致短期天气和长期气候预测的不准确或不确定性。该项目的目标是利用Loon高空气球前所未有的观测结果，并使用专门的高分辨率计算机模拟和机器学习技术来开发准确的，数据化的重力波表示。预计该项目的成果将产生更好的天气和气候模型，从而改善极端天气的短期预报和长期气候变化预测，这将产生巨大的社会效益。此外，该项目还将支持培养3名博士。学生，4博士后，和10个本科暑期研究人员在大气动力学，气候建模和数据科学的交叉工作，从而为跨学科的职业生涯准备下一代科学家。该项目将提供两个关键的进步。首先，它将开辟一个新的数据源来约束大气中的重力波动量传输。Loon LLC自2013年以来一直在推出超压气球，以提供全球互联网覆盖。非常高分辨率的位置，温度和压力观测（每60秒一次）可从数千次飞行中获得。这提供了一个前所未有的高分辨率观测源，以限制重力波源和传播。该项目将处理气球测量，并与新的高分辨率模拟相结合，建立一个公开的数据集，为重力波源，传播和破碎的观测约束评估开辟一个潜在的转型资源。第二个转变将是使用机器学习技术来开发重力波动量沉积的计算可行表示。当前基于物理的表示仅考虑波的垂直传播（即，它们是一维的）并且忽略它们的水平传播。使用基于Loon测量和高分辨率模型的数据，将开发一维和三维数据驱动表示，以更准确和有效地表示天气和气候模型中重力波的影响。这些新的表示将在理想化的大气模式中实现，以研究重力波在热带急流变化中的作用，准两年振荡（热带平流层中风的缓慢变化）和冬季平流层的极涡，这一奖项反映了NSF的法定使命，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，认为值得支持。

项目成果

Do Nudging Tendencies Depend on the Nudging Timescale Chosen in Atmospheric Models?

微推趋势是否取决于大气模型中选择的微推时间尺度？

• DOI: 10.1029/2022ms003024
• 发表时间: 2022
• 期刊: Journal of Advances in Modeling Earth Systems
• 影响因子: 6.8
• 作者: Kruse, Christopher G.;Bacmeister, Julio T.;Zarzycki, Colin M.;Larson, Vincent E.;Thayer‐Calder, Katherine
• 通讯作者: Thayer‐Calder, Katherine

Quantifying 3D Gravity Wave Drag in a Library of Tropical Convection‐Permitting Simulations for Data‐Driven Parameterizations

• DOI: 10.1029/2022ms003585
• 发表时间: 2023-05
• 期刊: Journal of Advances in Modeling Earth Systems
• 影响因子: 6.8
• 作者: Y. Q. Sun;P. Hassanzadeh;M. Alexander;C. Kruse

Observed and Modeled Mountain Waves from the Surface to the Mesosphere Near the Drake Passage

• DOI: 10.1175/jas-d-21-0252.1
• 发表时间: 2022-01
• 期刊: Journal of the Atmospheric Sciences
• 影响因子: 3.1
• 作者: C. Kruse;M. J. Alexander;L. Hoffmann;A. Niekerk;I. Polichtchouk;J. Bacmeister;L. Holt;R. Plougonven;Petr;ácha;C. Wright;Kaoru Sato;R. Shibuya;S. Gisinger;C. Meyer;Olaf STEINb