Published: February 7, 2025; Aithor: Julia Sonrisa
Address: 2276, 12th Avenue, New York, NY 10027, United States
Format: Hybrid
Virtual: Zoom link provided upon registration
*Please note that in-person space is limited.*
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Abstract: Remote sensing data provide a vast trove of information for studying the Earth system and climate. These data are often treated as if they are “truth”, but they are the results of complex inferential algorithms and are subject to uncertainty. In fact, the process of inferring the behavior of geophysical variables from noisy spectra observed from space, is itself an inverse problem. There is at present no universally agreed upon method for quantifying uncertainties in remote sensing data, and a wide variety of mostly ad hoc techniques are employed. Here I describe in general how these data sets are produced, the nature of the inverse problem that is embedded, and a probabilistic framework we have developed with an eye towards operational use. The latter demands that we address computational challenges we expect to face with NASA’s next generation of Earth observing satellites: the Earth System Observatory. Finally, I will describe an approach to “spatial” UQ that not only provides uncertainties on a footprint-by-footprint basis, but also preserves between-footprint uncertainty relationships.
Bio: Dr. Amy Braverman is a Senior Research Scientist at the Jet Propulsion Laboratory in Pasadena, CA. She holds a Ph.D. in Statistics from UCLA, and came to JPL as a post-doctoral scholar in 1999. Prior to graduate school, she was a Research Director at Micronomics, Inc. in Los Angeles where she led teams preparing exhibits for complex civil litigation. Dr. Braverman worked on various NASA missions in various capacities over her 25 years at the Lab, first in designing data reduction methods for massive remote sensing data sets, and later expanding to address general statistical methodology and applications issues related to remote sensing. In 2012 she began working intensely on uncertainty quantification (UQ), and has developed practical methods for UQ in high-throughput, operational inverse problems of interest to NASA and JPL. She is the past Chair of the SIAM Activity Group on Uncertainty Quantification, aiming to bridge the gap between traditional math-based UQ and statistics. Dr. Braverman is a Fellow of the American Statistical Association the recipient of the NASA Exceptional Public Service Medal for her efforts to bring rigorous UQ to the NASA science enterprise. She especially enjoys working with post-docs, graduate students, and academic colleagues to solve new statistical research problems relevant to Earth and Space sciences.
Time: 12:00-1:30 pm EST
Free!
Detailed information and discussion of the event.