Professor Matsuo's research aims to advance the science and engineering of forecasting, as applied to the Earth’s atmosphere from the ground to near-Earth space environments, while developing fundamental understanding of the predictability of a coupled Earth-Geospace system. Prediction of constantly changing environmental conditions requires a systematic integration of observations with a first-principles models using data assimilation. Data assimilation reduces uncertainties in initial conditions and drivers, extending the predictive capability of numerical models, and is used for designing of future missions and targeting of observations to maximize scientific returns of observing systems. Professor Matsuo's research also focuses on methodological problems, including the development of 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 data.
keywords
Atmospheric sciences and space physics, Predictability of geophysical dynamical systems, Data assimilation, Statistical (Machine) Learning
