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.

地球物质波是大气和海洋，通过分层或分层流体的相互作用使它们在大气中激发，而雷暴和其他强的对流系统以及冬季暴风雨来加剧。气候系统的关键特征的可变性，例如喷气流和地层极性涡流，这些波浪对天气和气候预测构成了挑战：100米至100公里的尺度上的波可以通过常规观察系统来测量100米至100公里结果，必须代表这些波浪或近似的直接模拟的代表。 G。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，从而为跨学科职业的下一个科学家准备。 LLC自2013年以来一直在提供全球互联网（每60秒）。为波浪源的观察量评估和破坏的第二个转换愿意，将机器学习技术用于发育可行的代表，从基于数据的测量值和tigh分辨率模型可以充实地表示这些新型的seric模型中的重力波。冬天更好地理解了他们的呼应。该奖项反映了NSF'Stututs值得通过使用Toundatual功绩和ER影响审查标准的评估来获得支持。

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