STAMPS@CMU presents:

Augmenting a sea of data with dynamics: the global ocean parameter and state estimation problem

by Patrick Heimbach (Oden Institute for Computational Engineering and Sciences, University of Texas at Austin)

Online webinar April 9, 2021 at 1:30-2:30 PM ET.
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Because of the formidable challenge of observing the full-depth global ocean circulation in its spatial detail and the many time scales of oceanic motions, numerical simulations play an essential role in quantifying patterns of climate variability and change. For the same reason, predictive capabilities are confounded by the high-dimensional space of uncertain inputs required to perform such simulations (initial conditions, model parameters and external forcings). Inverse methods optimally extract and blend information from observations and models. Parameter and state estimation, in particular, enables rigorously calibrated and initialized predictive models to optimally learn from sparse, heterogeneous data while satisfying fundamental equations of motion. A key enabling computational approach is the use of derivative information (adjoints and Hessians) for solving nonlinear least-squares optimization problems. Emerging capabilities are the uncertainty propagation from the observations through the model to key oceanic metrics such as equator-to-pole oceanic mass and heat transport. A related use of the adjoint method is the use of the time-evolving dual state as sensitivity kernel for dynamical attribution studies. I will give examples of the power of (i) property-conserving data assimilation for reconstruction, (ii) adjoint-based dynamical attribution, and (iii) the use of Hessian information for uncertainty quantification and observing system design.


Patrick Heimbach is a computational oceanographer at the University of Texas at Austin, with joint appointments in the Jackson School of Geosciences, the Institute for Geophysics, and the Oden Institute for Computational Engineering and Sciences. His research focuses on ocean and ice dynamics and their role in the global climate system. He specializes in the use of inverse methods applied to ocean and ice model parameter and state estimation, uncertainty quantification and observing system design.

Patrick earned his Ph.D. in 1998 from the Max-Planck-Institute for Meteorology and the University of Hamburg, Germany. Among his professional activities, Patrick serves on the National Academy of Sciences’ Ocean Studies Board, the CLIVAR/CliC Northern Ocean Regional Panel, and the US CLIVAR Ocean Uncertainty Quantification working group.