NG012
Uncertainty Quantification and Error Characterization in Remote Sensing
Session ID#: 76526
Session Description:
This session is devoted to a broad overview of applications, methodologies, and lessons-learned in the remote sensing community about uncertainty quantification and error characterization. Remote sensing instruments, whether they be space-borne, air-borne, or ground-based, directly measure spectra. Retrieval algorithms are required to infer physical and chemical quantities of interest, but neither measurements nor retrieval algorithms are perfect. This gives rise to uncertainty. Without rigorous quantification of uncertainty, users can't evaluate statistical significance of conclusions they draw. On the other hand, many retrieval experts concentrate on the related problem of error characterization. This session will explore relationships between the two concepts, and how both can be quantified. Given the variety of measurements, retrieval approaches, and scientific and application questions to be addressed, we hope this session will serve as a nexus for the exchange of ideas across the spectrum of remote sensing problems in Earth, planetary, and space science.
Index Terms:
1640 Remote sensing [GLOBAL CHANGE]
1986 Statistical methods: Inferential [INFORMATICS]
1990 Uncertainty [INFORMATICS]
3275 Uncertainty quantification [MATHEMATICAL GEOPHYSICS]
Primary Convener: Amy J Braverman, Jet Propulsion Laboratory, Pasadena, CA, United States
Conveners: Natalya A Kramarova, NASA Goddard Space Flight Center, Greenbelt, MD, United States and Viktoria Sofieva, Finnish Meteorological Institute, Helsinki, Finland
Primary Liaison: Amy J Braverman, NASA Jet Propulsion Laboratory, Pasadena, CA, United States
Towards Unified Error Reporting (TUNER) (Invited) (489610)
Nathaniel J Livesey, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States, Thomas von Clarmann, Karlsruhe Institute of Technology, Karlsruhe, Germany and D A Degenstein, University of Saskatchewan, Department of Physics & Engineering Physics, Saskatoon, SK, Canada
A framework for quantifying uncertainties in remote sensing retrievals (492593)
Amy J Braverman1, Jonathan Hobbs1, Joaquim Teixeira1 and Michael R Gunson2, (1)Jet Propulsion Laboratory, Pasadena, CA, United States, (2)Jet Propulsion Lab, Pasadena, CA, United States
Surrogate-Based Bayesian Inference for Source Localization (Invited) (500323)
Ralph Smith, North Carolina State University Raleigh, Raleigh, NC, United States, Paul Miles, Sandia National Laboratories, Albuquerque, United States and Jared Cook, North Carolina State University, Raleigh, NC, United States
Uncertainty Quantification for ECOSTRESS Evapotranspiration (507060)
Kerry Cawse-Nicholson1, Amy J Braverman2, Emily Lei Kang3, Joshua Fisher1, Gregory Hillard Halverson2 and Miaoqi Li3, (1)Jet Propulsion Laboratory, Pasadena, CA, United States, (2)NASA Jet Propulsion Laboratory, Pasadena, CA, United States, (3)University of Cincinnati Main Campus, Cincinnati, OH, United States
Uncertainty characterization of ACE-FTS data products (507648)
Patrick Sheese1, Kaley A Walker1 and Chris D. Boone2, (1)University of Toronto, Department of Physics, Toronto, ON, Canada, (2)University of Waterloo, Department of Chemistry, Waterloo, ON, Canada
Characterizing Error in the Verification Procedure of the ICESat-2 ATLAS Instrument’s Level-1B Product (537113)
Catherine Gosmeyer1,2, Megan R Bock2, Jeffrey Lee3 and Anthony J Martino2, (1)ADNET Systems Inc., Greenbelt, MD, United States, (2)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (3)SGT, Wallops Flight Facility, Wallops Island, VA, United States
Uncertainty Quantification for NASA's Microwave Limb Sounder (544294)
Maggie Johnson1, Nathaniel J Livesey1 and Amy J Braverman2, (1)Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States, (2)NASA Jet Propulsion Laboratory, Pasadena, CA, United States
Characterizing the Effects of Inhomogeneous Scene Illumination on the Retrieval of Greenhouse Gases from a Geostationary Platform (544438)
Jeffrey Nivitanont1, Sean Crowell1, Chris O'Dell2, Eric Burgh3, Gregory McGarragh2, Denis O'Brien4 and Berrien Moore III1, (1)University of Oklahoma, Norman, OK, United States, (2)Cooperative Institute for Research in the Atmosphere, Fort Collins, CO, United States, (3)Lockheed Martin Advanced Technology Center, Palo Alto, United States, (4)Cooperative Institute for Research in the Atmosphere, Parkville, Australia
Lessons Learned in Simulation-Based Uncertainty Quantification for Satellite Retrievals (575402)
Jonathan Hobbs1, Amy J Braverman1, Ali Behrangi2, Eric J Fetzer1, Kelsey Foster3, Huikyo Lee4, James McDuffie1, Hai Nguyen1, Maya Shen5 and Joaquim Teixeira1, (1)Jet Propulsion Laboratory, Pasadena, CA, United States, (2)University of Arizona, Department of Hydrology and Atmospheric Sciences, Tucson, AZ, United States, (3)Stanford University, Stanford, United States, (4)Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States, (5)University of California Berkeley, Berkeley, CA, United States
Assessing the impact of uncertainty in Orbiting Carbon Observatory-2 estimates of CO2 concentration. (590221)
Joaquim Teixeira1, Amy J Braverman1, Jonathan Hobbs2 and Michael R Gunson3, (1)NASA Jet Propulsion Laboratory, Pasadena, CA, United States, (2)Jet Propulsion Laboratory, Pasadena, CA, United States, (3)Jet Propulsion Lab, Pasadena, CA, United States
Characterizing Uncertainties in Nearly 50 Year Record of Ozone from SBUV series of Instruments (616040)
Pawan K Bhartia1, Stacey M Frith2, Natalya A Kramarova3 and Jerald R Ziemke2, (1)NASA Goddard Space Flight Center, Earth Sciences Division- Atmospheres MC614, Greenbelt, MD, United States, (2)NASA Goddard SFC, Greenbelt, MD, United States, (3)NASA Goddard Space Flight Center, Greenbelt, MD, United States
Uncertainty quantification in aerosol properties retrieved by optimal estimation: a testbed using General Retrieval of Aerosol and Surface Properties (GRASP) (629774)
Meredith Franklin, University of Southern California, Los Angeles, CA, United States, Azar Ghahari, Boston University, Boston, United States, Michael Turmon, JPL, Pasadena, CA, United States, Michael J Garay, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States, Amy J Braverman, NASA Jet Propulsion Laboratory, Pasadena, CA, United States and Huikyo Lee, Jet Propulsion Laboratory, Caltech, Altadena, CA, United States
Error-Uncertainty Analysis of the TOMS Version 9 Total Ozone Record from the OMI and OMPS Mapping Instruments (632469)
David P Haffner1, Natalya A Kramarova2, Jerald R Ziemke3, Richard D McPeters3, Pawan K Bhartia2, Kang Hyun Baek4, Gordon J Labow2 and Stacey M Frith3, (1)Science Systems and Applications, Inc., Lanham, MD, United States, (2)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (3)NASA Goddard SFC, Greenbelt, MD, United States, (4)Pusan National University, Busan, Korea, Republic of (South)
