报 告 人：Dr. Sue Ellen Haupt，Research Applications LaboratoryNational Center for Atmospheric Research
报告题目：Evolving Realizations with Assimilation:Blending Observations with Models
报告时间：9月17日（周四）下午4：00
报告地点：静远楼1508学术报告厅
Abstract: Although models can provide a best estimate of the future state of a geophysical fluid, due to sensitivity to initial conditions, it is intractable to predict the precise future state. In particular, predicting a particular realization of an evolving flow field requires knowledge of the current state of that field and assimilation of observations into the model. An example is modeling atmospheric transport and dispersion of a contaminant when the observation is of the transported contaminant, a problem that exemplifies the issue of uncertain turbulent flow. We will discuss the inner vs. the outer variability and how both can be recovered with judicious use of the observations. In this case, the problem is compounded by the fact that the field observed is a tracer that is advected and mixed by the flow field, but does not directly alter the flow field. This one-way coupled system presents a challenge: one must first infer the changes in the flow field from observations of the contaminant, then assimilate that data to recover both the advecting flow and information on the subgrid processes that provide the mixing. This work demonstrates using a genetic algorithm to optimize the match between the observed flow and the model. Given contaminant sensor measurements and a transport and dispersion model, one can back-calculate unknown source and meteorological parameters. In this case, we demonstrate the dynamic recovery of unknown meteorological variables, including the transport variables that comprise the “outer variability” (wind speed and wind direction) and the dispersion variables that comprise the “inner variability” (contaminant spread). The optimization problem is set up in an Eulerian grid space, where the comparison of the concentration field variable between the predictions and the observations forms the cost function. The transport and dispersion parameters, which are determined from the optimization, are in Lagrangian space. We then discuss the broader applicability of this general approach, specifically blending physical modelling, ground truth observations, and artificial intelligence methods to optimize the match between the two and argue that this approach can be advantageous for discovering information about dynamical/physical processes.
Dr. Haupt 个人简介：Dr. Haupt is a Senior Scientist at the National Center for Atmospheric Research and Director of the Weather Systems and Assessment Program within NCAR’s Research Applications Laboratory, where she oversees renewable energy, artificial intelligence, surface transportation, societal impact, fire weather applications, and international aviation programs. She previously headed the Department of Atmospheric and Oceanic Physics at the Applied Research Laboratory of The Pennsylvania State University where she remains an Adjunct Professor of Meteorology. She earned her Ph.D. in Atmospheric Science from the University of Michigan (1988), M.S. in Mechanical Engineering from Worcester Polytechnic Institute (1984), M.S. in Engineering Management from Western New England College (1982), B.S. in Meteorology from Penn State (1978), and did a postdoctoral fellowship with the Advanced Study Program of NCAR. She has also been on the faculty of the University of Colorado/Boulder; the U.S. Air Force Academy (visiting); University of Nevada, Reno; and Utah State University and previously worked for the New England Electric System and GCA Corporation.Dr. Haupt’s research includes work in renewable energy, boundary layer meteorology, large scale geophysical fluid dynamics, dynamical systems, numerical methods, artificial intelligence methods, uncertainty quantification, and computational fluid dynamics. Her specialty is in applying novel numerical techniques to problems in the environmental sciences in both basic and applied research. She enjoys teaching and mentoring scientists and graduate students as well as developing and directing research programs and projects.Dr. Haupt is a founding director of the World Energy and Meteorology Council and has helped organize the three Conferences on Energy and Meteorology, including teaching portions of the preconference workshops. She recently chaired the Committee on Artificial Intelligence Applications to Environmental Science of the American Meteorological Society, and teaches the portions of short courses offered by that committee on using genetic algorithms for environmental science problems. She currently serves on the AMS Energy Committee and recently served on the AMS Board on Economic and Enterprise Development. She is also a member of the American Geophysical Union, American Solar Energy Society, Society of Women Engineers (served as faculty advisor for student groups for five years), American Society of Mechanical Engineers, Chi Epsilon Pi, and Phi Mu Epsilon. She co-authored Practical Genetic Algorithms (Wiley and Sons 1998, second edition 2004, Arabic edition 2011); is primary editor for Applications of Artificial Intelligence Methods in the Environmental.