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，这是由于重力与分层或分层的液体的相互作用而成为可能的。当风流通过山脉，雷暴和其他强烈的对流系统以及冬季风暴加剧时，他们会在大气中感到兴奋。重力波在大气的动量和能量平衡中起着重要的作用，通过对气候系统（例如喷气流和平流层极性涡流）的关键特征的影响，对表面天气和气候产生直接影响。这些波浪对天气和气候预测提出了挑战：可以系统地测量100米至100公里的尺度上的波浪，以常规的观察系统进行系统测量，也可以在全球大气模型中正确解决。结果，必须基于可以直接模拟的分辨流量来表示或近似这些波。当前的重力波的表示受到计算必要性和观察稀缺性的严重限制，导致短期天气和长期气候预测的不准确性或不确定性。该项目的目的是利用loon高海拔气球的前所未有的观察结果，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重力波的准确，数据信息的表示。预计该项目的结果将导致更好的天气和气候模型，从而改善了极端天气和长期气候变化项目的短期森林，这些项目具有很大的社会益处。此外，该项目将支持3博士学位的培训。学生，4个博士后和10名本科夏季研究人员在大气动态，气候建模和数据科学的交集中工作，从而为下一代科学家做好了跨学科职业的准备。该项目将带来两个关键的进步。首先，它将打开一个新的数据源，以限制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力气球，以提供全球互联网覆盖范围。数千个航班可获得很高的分辨率位置，温度和压力观测（每60秒）。这为限制重力波源和传播的高分辨率观察提供了前所未有的来源。该项目将处理气球测量值，并与新颖的高分辨率模拟共同建立一个公开可用的数据集，以打开潜在的变革性资源，以观察到重力波源，传播和破裂的观察受到限制的评估。第二个转换将使用机器学习技术来开发重力波的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），而忽略了它们的水平传播。使用基于LOON测量和高分辨率模型的数据，将开发一个和三维数据驱动的表示形式，以更准确，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的大气模型中实施，以研究重力波在诱发喷气流的可变性中的作用，准两年一次的振荡（在热带草皮中风的较慢变化）和冬季稻草的极性涡流，从而更好地理解了对大气中的统计范围的响应。通过基金会的智力优点和更广泛的影响评估标准通过评估来支持。

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