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Activity Number: 280 - Climate Statistics: Studies on the Physics and Impacts of Climate Change Using Data Science
Type: Topic Contributed
Date/Time: Tuesday, August 1, 2017 : 8:30 AM to 10:20 AM
Sponsor: Section on Physical and Engineering Sciences
Abstract #323317 View Presentation
Title: Uncertainty Quantification for Remote Sensing Data
Author(s): Amy Braverman* and Jonathan Hobbs
Companies: Jet Propulsion Laboratory and Jet Propulsion Laboratory
Keywords: remote sensing ; uncertainty quantification ; big data ; Monte Carlo simulation ; Gaussian mixtures

Satellite remote sensing instruments do not observe geophysical quantities of interest directly. They observe radiances in bands of the electromagnetic spectrum that are sensitive to those quantities. Geophysical data sets produced by NASA and other space agencies are the results of complex algorithms that infer geophysical state from observed radiances using ``retrieval" algorithms. The processing must keep up with the downlinked data flow, and this necessitates computational compromises that affect the accuracies of retrieved estimates. The algorithms are also limited by imperfect knowledge of physics and of ancillary inputs that are required. All of this contributes to uncertainties that are generally not rigorously quantified. In this talk we discuss a practical framework for uncertainty quantification that can be applied to a variety of remote sensing retrieval algorithms. Ours is a statistical approach that uses Monte Carlo simulation to approximate the sampling distribution of the retrieved estimates. We will discuss the strengths and weaknesses of this approach and provide some real-life examples from JPL missions.

Authors who are presenting talks have a * after their name.

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