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和海洋中普遍存在的现象，是由重力与分层或层状流体的相互作用而产生的，当风吹过山脉、雷暴和其他强对流系统时，它们就会在大气中被激发。冬季风暴加剧，在大气动量和能量平衡中发挥着重要作用，通过影响急流和气候系统主要特征的变化，对地表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常规观测系统测量，也不能在全球大气模型中正确解析。因此，必须对这些波进行表示。或近似，基于可直接模拟的解析流当前重力波的表示受到计算必要性和观测稀缺性的严重限制，导致短期天气和长期气候的不准确或不确定性。该项目的目标是利用 Loon 高气球的前所未有的观测结果，并使用专门的高分辨率高度计算机模拟和机器学习技术来开发准确的、基于数据的重力波表示。更好的天气和气候模型，从而改善极端天气的短期预测和长期气候变化预测，这具有巨大的社会效益此外，该项目还将支持 3 名博士生、4 名博士后和 10 名本科生暑期培训。研究人员工作大气动力学、气候建模和数据科学的交叉点，从而为下一代科学家的跨学科职业做好准备。该项目将带来两项关键进展，首先，它将开辟一个新的数据源来约束重力波动量传输。 Loon LLC 自 2013 年以来一直在发射超高压气球，通过数千个航班提供极高分辨率的位置、温度和压力观测数据（每 60 秒一次）。观测以约束重力波源和该项目将处理气球测量结果，并与新颖的高分辨率模拟相结合，建立一个公开可用的数据集，为重力波源、传播和破坏的观测受限评估开辟潜在的转型资源。使用机器学习技术来开发重力波动量沉积的计算上可行的表示。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），而忽略基于波的水平传播的数据。将开发 Loon 测量和高分辨率模型、一维和三维数据驱动的表示，以更准确、更有效地表示重力波在天气和气候模型中的影响，这些新颖的表示将在理想化大气模型中实施，以研究重力的作用。温带急流、准两年期振荡（热带平流层风的缓慢变化）和冬季平流层极地涡旋的波动，使我们能够更好地了解它们对大气温室气体浓度增加的反应。该奖项反映了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，NSF 的法定使命被认为值得支持。

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

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

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