Collaborative Research: Observations and Modeling of Primary and Secondary Gravity Waves at all Altitudes over the Andes

大气重力波（GWs）的结果时，空气团受到干扰，并超过其平衡位置，造成振荡运动。这些通常是由地形或雷暴引发的。全球Ws对于在整个大气中传输和分配能量和动量至关重要，影响各种过程。在不同类型的重力波中，由高空风再调整和湍流引起的次级重力波（SGW）尤为重要。然而，他们的一代是这一现象最不为人所知的方面。该项目计划将观测（卫星和地面）与最先进的数值模型相结合，以提高对重力波的认识。这项工作的目的是增进对以下方面的了解：（a）初级和次级重力波动力学;（B）大气和其他地球物理流体中发生的湍流和不稳定过程。这些知识可以应用于改进天气和气候模型，从而产生更准确的预测，这对商业航空有利。 该项目将为STEM教育做出贡献，为一名研究生、两名博士后研究人员和一名早期职业科学家提供支持。该项目旨在通过使用新的观察和机器学习方法来解决与GW动态相关的引人注目的科学问题。计划在这次调查中使用的多仪器数据包括：1）美国宇航局Aqua卫星上的平流层大气红外探测仪，2）Na激光雷达，中间层温度测绘仪，航空航天夜光成像仪，以及安第斯山脉激光雷达观测站的流星雷达，以及3）火地岛上的可比仪器。一种新的机器学习方法沿着与传统的天气模型计划用于这项调查。这个项目将提供一个定量的了解（a）的作用和重要性GW打破SGW的生成，（B）不稳定性和湍流动力学，（c）GW和SGW迫使MLT。这些研究也将促进对SGW源及其特性的理解。最后，机器学习的努力可能会打开一个新的窗口，改善全球大气模型中的主要和次要GW的参数化，其中大部分GW的影响不能直接解决。这个奖项反映了NSF的法定使命，并已被认为是值得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的支持。

Atmospheric Gravity waves (GWs) result when air parcels get disturbed and overshoot their equilibrium position causing oscillatory motions. These are often triggered by the topography, or thunderstorms. GWs are essential for transporting and distributing energy and momentum throughout the atmosphere, influencing various processes. Among the different types of GWs, secondary gravity waves (SGW) (caused by upper-altitude wind readjustments and turbulence) are particularly important. However, their generation is the least understood aspect of this phenomenon. This project plans to integrate observations (satellite as well as ground-based) and state-of-the-art numerical models to improve the understanding of gravity waves. The effort aims to advance the understanding of (a) both primary and secondary gravity waves dynamics, (b) turbulence and instability processes that occur in the atmosphere and other geophysical fluids. This knowledge can be applied to improve weather and climate models, resulting in more accurate predictions that are beneficial for commercial aviation. This project will contribute to STEM education providing support to a graduate student, two postdoctoral researchers and an early career scientist. The project aims to address compelling science questions related to GW dynamics by using novel observations and machine learning methods. Multi-instrument data planned to be used in this investigation include: 1) the Atmospheric Infrared Sounder aboard NASA's Aqua satellite for the stratosphere, 2) the Na lidar, Mesosphere Temperature Mapper, Aerospace nightglow imager, and meteor radar at the Andes Lidar Observatory, and 3) comparable instruments on Tierra del Fuego. A new machine learning approach along with conventional weather models are planned to be used for this investigation. This project will provide a quantitative understanding of the (a) roles and importance of GW breaking in SGW generation, (b) instability and turbulence dynamics, and (c) GW and SGW forcing of the MLT. These studies will also advance the understanding of SGW sources and their characteristics. Finally, the machine learning efforts may open a new window for improved parameterizations of primary and secondary GWs in global atmospheric models where the bulk of GW effects cannot be resolved directly.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.