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: 2004572
• 负责人: Edwin Gerber
• 金额: $ 119.49万
• 依托单位: New York University
• 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=2004572&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，是重力与层状流体相互作用的结果。当风穿过山脉、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地涡旋等气候系统关键特征的变率，对地面天气和气候产生直接影响。这些波对天气和气候预报提出了挑战：100米至100公里尺度的波既不能用常规观测系统系统地测量，也不能在全球大气模式中适当地分解。因此，必须根据可直接模拟的已分解气流来表示或近似这些波。由于计算的需要和观测的缺乏，目前重力波的表示受到严重限制，导致短期天气和长期气候预测的不准确或不确定。该项目的目标是利用Loon高空气球的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，数据知情的重力波表示。预计该项目的成果将产生更好的天气和气候模式，从而改进极端天气的短期预报和长期气候变化预测，这具有重大的社会效益。此外，该项目将支持培养3名博士生、4名博士后和10名本科生暑期研究员，在大气动力学、气候建模和数据科学的交叉领域工作，从而为下一代科学家的跨学科职业做好准备。该项目将带来两个关键进展。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输运。Loon LLC自2013年以来一直在发射超压气球，以提供全球互联网覆盖。从数千次飞行中可以获得非常高分辨率的位置、温度和压力观测（每60秒采集一次）。这为约束重力波的来源和传播提供了前所未有的高分辨率观测资源。该项目将处理气球测量数据，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、传播和破裂的观测约束评估开辟一个潜在的转型资源。第二个转换将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。目前基于物理的表示只考虑了波的垂直传播（即，它们是一维的），而忽略了它们的水平传播。利用Loon的测量数据和高分辨率模型，将开发一维和三维数据驱动的表示，以更准确和有效地表示天气和气候模型中重力波的影响。这些新的表征将在理想的大气模式中实现，以研究重力波在温带急流、准两年一次振荡（热带平流层风的缓慢变化）和冬季平流层极地涡旋的变异性中的作用，从而更好地理解它们对大气温室气体浓度增加的响应。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。

项目成果

Revealing the Statistics of Extreme Events Hidden in Short Weather Forecast Data

• DOI: 10.1029/2023av000881
• 发表时间: 2022-06
• 期刊: AGU Advances
• 影响因子: 8.4
• 作者: J. Finkel;E. Gerber;D. Abbot;J. Weare

Tropospheric Expansion Under Global Warming Reduces Tropical Lower Stratospheric Ozone

全球变暖导致对流层扩张减少热带低平流层臭氧

• DOI: 10.1029/2022gl099463
• 发表时间: 2022
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: Match, Aaron;Gerber, Edwin P.
• 通讯作者: Gerber, Edwin P.

The Matsuno–Gill model on the sphere

球体上的松野吉尔模型

• DOI: 10.1017/jfm.2023.369
• 发表时间: 2023
• 期刊: Journal of Fluid Mechanics
• 影响因子: 3.7
• 作者: Shamir, Ofer;Garfinkel, Chaim I.;Gerber, Edwin P.;Paldor, Nathan
• 通讯作者: Paldor, Nathan

Data-Driven Transition Path Analysis Yields a Statistical Understanding of Sudden Stratospheric Warming Events in an Idealized Model

数据驱动的转变路径分析可以在理想化模型中对平流层突然变暖事件产生统计了解

• DOI: 10.1175/jas-d-21-0213.1
• 发表时间: 2023
• 期刊: Journal of the Atmospheric Sciences
• 影响因子: 3.1
• 作者: Finkel, Justin;Webber, Robert J.;Gerber, Edwin P.;Abbot, Dorian S.;Weare, Jonathan
• 通讯作者: Weare, Jonathan