Seminar:
Post-hoc Uncertainty Quantification for Remote Sensing Observing Systems
Thursday, March 12, 2020
3:30PM – 5PM
POB 6.304
Amy Braverman
The ability of spaceborne remote sensing data to address important Earth and climate science problems rests crucially on how well the underlying geophysical quantities can be inferred from these observations. Remote sensing instruments measure parts of the electromagnetic spectrum and use computational algorithms to infer the unobserved true physical states. However, the accompanying uncertainties, if they are provided at all, are usually incomplete. There are many reasons why including but not limited to unknown physics, computational artifacts and compromises, unknown uncertainties in the inputs, and more.
In this talk I will describe a practical methodology for uncertainty quantification of physical state estimates derived from remote sensing observing systems. The method we pro-pose combines Monte Carlo simulation experiments with statistical modeling to approximate conditional distributions of unknown true states given point estimates produced by imperfect operational algorithms. Our procedure is carried out post-hoc; that is, after the operational processing step because it is not feasible to redesign and rerun operational code. I demonstrate the procedure using four months of data from NASA’s Orbiting Carbon Observatory-2 mission, and compare our results to those obtained by validation against data from the Total Carbon Column Observing Network where it exists.
This is joint work with Jonathan Hobbs, Joaquim Teixeira, and Michael Gunson at the Jet Propulsion Laboratory, California Institute of Technology
Email: Amy.Braverman@jpl.nasa.gov
Bio
Dr. Amy Braverman is a Principal Statistician at the Jet Propulsion Laboratory in Pasadena, California. She received her doctorate in statistics from the University of California, Los Angeles (UCLA), a masters in Mathematics from UCLA, and a B.A. degree in economics from Swarthmore College, Swarthmore, PA, in 1982.
Her research interests include information-theoretic approaches for the analysis of massive data sets, data fusion methods for combining heterogeneous, spatial and spatio-temporal data, and statistical methods for the evaluation and diagnosis of climate models, particularly by comparison to observational data. Dr. Braverman focuses on the use of remote sensing data, and has designed and analyzed new Level 3 data products for MISR and other NASA missions.
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