Atmospheric gravity waves (GWs) play an important role in the exchange of momentum between the Earth’s surface and the free atmosphere. Uncertainties in gravity wave momentum transport limit our ability to predict the response of the tropospheric and stratospheric circulation to global warming and impact subseasonal-to-seasonal forecasts. Current state-of-the-art parameterizations are severely limited by computational necessity and the scarcity of observations.

The DataWave project is focused on improving our modeling capability for gravity waves and the large scale circulation, and particularly to lead to novel observationally constrained and data-driven gravity wave parameterization schemes.

Main Objectives

You can read more about how the tasks were split up on our research page and learn about the leaders of each task on our team page.

1: Observation Database

The first objective is to make available a potentially transformational data source from Loon LLC with unprecedented, high-resolution observations of atmospheric conditions across thousands of balloon flights.

2: Machine Learning

The second objective is to use machine learning to develop one- and three- dimensional data-driven gravity wave parametrizations to more accurately and efficiently represent gravity wave momentum fluxes.

Recent Publications

Post-Doc Position at Goethe Universität Frankfurt

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PhD and PostDoc at Rice University

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PhD Position at Goethe Universität Frankfurt

[Filled] Post-Doc Position at LMD/ENS (Paris)

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Recent Publications, Papers, and News

PI highlight: Ed Gerber

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PI highlight: Aditi Sheshadri

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Observed and Modeled Mountain Waves from the Surface to the Mesosphere Near the Drake Passage

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A QBO cookbook: Sensitivity of the Quasi-Biennial Oscillation to resolution, resolved waves, and parameterized gravity waves

Can we improve the realism of gravity wave parameterizations by imposing sources at all altitudes in the atmosphere?

Mountain waves produced by a stratified shear flow with a boundary layer. Part III: Trapped lee waves and horizontal momentum transport

Closed-form discovery of structural errors in models of chaotic systems by integrating Bayesian sparse regression and data assimilation

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