GC022-0004
Accelerated Bayesian computation for global imaging spectroscopy

Tuesday, 8 December 2020
Poster
Kelvin Leung1, Jayanth Jagalur-Mohan2, David R Thompson3, Amy J Braverman4, Vijay Natraj3 and Youssef Marzouk5, (1)Massachusetts Institute of Technology, Cambridge, MA, United States, (2)Massachusetts Institute of Technology, Cambridge, United States, (3)Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States, (4)NASA Jet Propulsion Laboratory, Pasadena, CA, United States, (5)Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, Cambridge, MA, United States
Abstract:
The Bayesian approach to inverse problems arising in imaging spectroscopy can quantify uncertainty in retrievals and help elucidate the value of different information sources, but it can be computationally intractable in practice. In many Bayesian inverse problems, however, there exists a low-dimensional likelihood-informed subspace that describes both optimal projections of the data and directions in parameter space that are most informed by the data. We demonstrate how to exploit this subspace for data compression in inverse problems for fitting surface and atmospheric models to imaging spectrometer data. We also explore multiple levels of forward model fidelity, with the goal of developing a Markov chain Monte Carlo (MCMC) retrieval algorithm sufficiently fast for operations.
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