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.
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.