Title: Level 2 UQ: Enabling Simulation-Based Uncertainty Quantification for Remote Sensing Retrievals
Presenting Author: Jonathan Hobbs
Organization: Jet Propulsion Laboratory
Co-Author(s): Ali Behrangi, Amy Braverman, Eric Fetzer1, Kyo Lee, Hai Nguyen

Abstract:
The data processing pipelines for NASA's Earth-orbiting satellites typically include retrieval algorithms that transform Level 1 radiances into Level 2 estimates of geophysical quantities of interest. Each retrieval strategy presents unique challenges, but most methods involve a physical and/or statistical model with a computational inverse method. Various choices in implementing a retrieval algorithm, along with the inherent variability in the satellite data, will impact the distribution of retrieval errors with respect to the true state. The statistical properties of a retrieval algorithm can be studied in depth by implementing Monte Carlo simulation experiments of the satellite observing system using ensembles of realistic true states. This work develops software that facilitates post-processing simulation experiments and diagnosing retrieval algorithm performance through several algorithm figures of merit. We illustrate these tools with the Orbiting Carbon Observatory-2 (OCO-2) and Atmospheric Infrared Sounder (AIRS) retrieval algorithms.