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Activity Number: 260 - SPEED: Environmental Statistics Methods and Applications, Part 2
Type: Contributed
Date/Time: Monday, July 29, 2019 : 3:05 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract #307676
Title: Uncertainty Quantification for Joint Retrieval of Temperature, Humidity, and Cloud States from Satellite Data
Author(s): Jonathan Hobbs*
Companies: Jet Propulsion Laboratory
Keywords: uncertainty quantification; inverse problem; remote sensing; climate

Scientific data products produced from Earth-orbiting satellites include state estimates for several atmospheric quantities of interest. These estimates are based on complex retrieval algorithms that typically involve a physical and/or statistical model with a computational inverse method. This work develops a joint probabilistic model for an atmospheric state that includes clouds, temperature, and humidity. We use simulation to examine the distribution of the retrieval error distribution of these quantities for the Atmospheric Infrared Sounder (AIRS) retrieval. The AIRS retrieval algorithm uses a nonlinear regression in combination with a cloud-clearing procedure, and its error distribution is particularly sensitive to the behavior of clouds at fine spatial resolution. We illustrate the impacts of the retrieval uncertainty on derived quantities of interest in several weather and climate applications.

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

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