Highlights topics of discussion on remote sensing during Day 1 of Program on Mathematical and Statistical Methods for Climate and the Earth System Opening Workshop.

1. Discussion: Session on Remote Sensing
Amy Braverman
Jet Propulsion Laboratory, California Institute of Technology
August 22, 2017
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2. Noel’s talk: broad overview of the role of statistical principles in remote
sensing
Dan’s talk: NASA’s remote sensing data processing and distribution
infrastructure
Matthias’ talk: framework for spatial statistics suitable for massive and
distributed data
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3. Noel’s: hierarchy of science questions paralleled by hierarchy of
processing
Dan’s talk: hierarchy of constraints imposed by processing infrastructure
Matthias’ talk: hierarchy of solutions?
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4. How do we connect these concepts in a way that is specific enough to
suggest tangible research to pursue?
Big constraint: can’t move (all) data
Points to a need to flexibly trade-off inferential quality against cost
(computational and transportation, etc.)
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5. Matthias: example of new statistical methods that explicitly recognize the
need to trade-off cost vs. inferential quality.
General topic for the Working Group: develop/study methods that can be
implemented within a large-scale data processing framework
Requires understanding uncertainty-cost trade space
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6. Some type of problems:
1. Estimating and modelling distributions in space and time with massive
observational data volumes, and “massively complex" structure
2. Get away from Gaussians
3. Approximate sufficient statistics? Work by Petruta Caragea and Richard
Smith
4. Faster MCMC and quasi Monte Carlo methods; multi-level Monte Carlo
(Giles, 2015)
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7. Many thanks for JPL’s Center for Data Science and Technology and Caltech’s
Center for Data Driven Discovery for supporting these activities.
©2017 California Institute of Technology. Government sponsorship
acknowledged.
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