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Grey Nearing: "Toward Confirmatory Data Analytics for Process-Based Hydrology Models."

Abstract: I take a machine learning and information-theoretic perspective to the problem of evaluating complex systems models. This talk will outline a set of hypothesis tests, methods for uncertainty quantification, and diagnostic procedures for using large data sets to identify and quantify particular areas for potential improvement to highly coupled dynamical systems models. Machine learning is used to set performance benchmarks, and also to extract diagnostic information. I’ll provide several examples related to hydrology and land surface modeling.

Bio: Grey Nearing received his PhD in 2013 from the Hydrology and Water Resources Department at the University of Arizona. He was a contractor in the Hydrological Sciences Lab at NASA Goddard Space Flight Center for four years. For the past two years, Grey has held a half-time appointment at the National Center for Atmospheric Research. He started as an assistant professor in the Department of Geological Sciences at the University of Alabama at the beginning of this semester.

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