Prediction serves as the ultimate test of our scientific understanding of geophysical systems.聽Accurate forecasting of near-Earth space environmental conditions is critical to radio communication, navigation, positioning, and satellite tracking. Effective numerical prediction of the region鈥檚 conditions allows us to better protect important space assets and related systems in the event of natural hazards.聽
Geospace Data Science Lab aims to advance the science and engineering of forecasting, as applied to the Earth鈥檚 atmosphere from the ground to near-Earth space environments,聽while developing fundamental聽understanding of the predictability of a coupled system of the whole atmosphere, ionosphere, and magnetosphere.聽Prediction of constantly changing environmental conditions, affected by both space and terrestrial weather, requires a聽systematic integration of observations with a first-principles models using data assimilation.聽聽Data assimilation reduces uncertainties in initial conditions and聽drivers, extending the predictive capability of numerical models of聽near-Earth space environments.聽聽The聽data assimilation and聽ensemble-based probabilistic modeling framework聽being developed can be used for聽designing of future missions and targeting of observations to maximize scientific returns of observing systems.聽Geospace Data Science Lab also focuses on methodological problems, including the development of scalable data assimilation methods for high-dimensional problems, inversion and machine learning techniques to extract relevant geophysical information from large volumes of heterogeneous remote sensing and in-situ sensor data.