CEE Seminar (ZOOM): Physically-informed Inverse Problems in Satellite Hydrology

Abstract: Satellite observations of the Earth’s radiation in microwave bands play an indispensable role, not only for unraveling the impacts of climate change on the global water cycle but also for accurate prediction of droughts and extreme storms. On the one hand, satellite remote sensing of the water cycle involves inversion of radiative transfer equations that link the amount of water in land and atmosphere to radiation intensity, observed by spaceborne radiometers at the top of the atmosphere. On the other hand, improved predictability of the hydrologic cycle requires optimal integration of satellite observations with the outputs of Earth System Models (ESMs). Ebtehaj presents recent advances in physically informed inversion of land emission models for remote sensing of soil moisture and snowpack from space. Furthermore, he presents a new paradigm for satellite data assimilation over a Riemannian manifold equipped with the Wasserstein metric. Unlike classic approaches over the Euclidean space, this new framework can formally penalize geophysical biases in a non-Gaussian state space. The advantages of this approach for improved geophysical forecasts are demonstrated using chaotic dynamical systems, representing atmospheric circulation.

Bio: Ardeshir is an assistant professor in the department of civil, environmental, and geo-engineering at the University of Minnesota. Prior to joining  the University of Minnesota, he has worked as a postdoctoral research fellow at Georgia Institute of Technology for two years with Professor Rafael Bras. He received his doctoral degree in civil engineering from Saint Anthony Falls Laboratory at the University of Minnesota in 2013, under the supervision of Professor Efi Foufoula-Georgiou, where he also received a master's degree in mathematics, supervised by Professor Gilad Lerman. In his last year at the University of Minnesota, he served as a NASA Earth and Space Science Fellow (NESSF) and as a University of Minnesota Doctoral Dissertation Fellow (DDF). His research experience includes hydrologic data sciences and inverse problems in hydro-meteorological systems, physical hydrology and land-atmosphere interactions, stochastic hydro-meteorology, and environmental risk and extreme value analysis. In the past few years, his major area of research has been focused on hydrologic remote sensing, data assimilation and land-atmosphere interactions. He is a recipient of the NASA New (Early Career) Investigator Program (NIP) Award in Earth Science in 2018.