STAMPS@CMU presents:

Post-hoc Uncertainty Quantification for Remote Sensing Observing Systems

by Amy Braverman (Jet Propulsion Laboratory, California Institute of Technology)

Online webinar October 9, 2020 at 1:30-2:30 PM EDT.
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Abstract
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 propose 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.

Bio

Amy Braverman is Principal Statistician at the Jet Propulsion Laboratory, California Institute of Technology. She received her Ph.D. in Statistics from UCLA in 1999. Prior to that she earned an M.A. in Mathematics, also from UCLA (1992), and a B.A. in Economics from Swarthmore College in 1982. From 1983 to 1990, she worked in litigation support consulting for two different firms in Los Angeles. Her research interests include massive data set analysis, spatial and spatio-temporal statistics, data fusion, decision making in complex systems, and uncertainty quantification.