ABSTRACT

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.

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