Accurate forecasting of near-Earth space environmental conditions is critical to radio communication, navigation, positioning, and satellite tracking. Effective numerical prediction of the region’s conditions will allow us to better protect important space assets and related systems in the event of natural hazards. The space-atmosphere interaction region, composed of the thermosphere and ionosphere, is constantly affected by space weather and terrestrial weather forcing. 


Geospace Data Science Lab aims to advance the science and engineering of forecasting, as applied to the whole atmosphere from the ground to near-Earth space environments, by systematically integrating Earth and geospace observations with a first-principles model. One of research thrusts is a comprehensive inquiry into the predictability of the space-atmosphere interaction region by developing a new probabilistic modeling paradigm applicable to the forced dissipative nonlinear dynamics exhibited by the topmost layer of the atmosphere. The second research thrust is to assess observation impacts on predictability through analysis of data assimilation and ensemble forecasting experiments. The third research thrust focuses on methodological problems, including the development of scalable methods for high-dimensional data assimilation problems, inversion and machine learning techniques to extract relevant geophysical information from large volumes of heterogeneous remote sensing data.