A221-0002
Exploring Geometry and Aerosol Effects of OCO Target and Small Area Map Measurements via Simulation Studies

Wednesday, 16 December 2020
Poster
Emily Bell1, Thomas Taylor2, Aronne J Merrelli3, Chris O'Dell4, Robert R Nelson2 and Annmarie Eldering5, (1)Colorado State University, Fort Collins, CO, United States, (2)NASA Jet Propulsion Laboratory, Pasadena, CA, United States, (3)University of Wisconsin Madison, Madison, WI, United States, (4)Cooperative Institute for Research in the Atmosphere, Fort Collins, CO, United States, (5)NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States
Abstract:
The Atmospheric Carbon Observations from Space (ACOS) retrieval algorithm has been delivering operational column-averaged carbon dioxide (XCO2) data for the Orbiting Carbon Observatory (OCO) missions since 2014. The ACOS Level 2 Full Physics (L2FP) algorithm retrieves a number of parameters, including aerosol and surface properties, in addition to atmospheric CO2. Forward model and other errors can lead to systematic biases in the retrieved XCO2, which are often correlated with these additional retrieved parameters. In this study we explore such algorithm-induced biases via simulations of Target and Small Area Map (SAM) observations, which are particularly prone to biases via the changing viewing geometry throughout the observation over a particular scene. The timing and geometry of real targets (from OCO-2) and SAMs (from OCO-3) are used to create simulated L1b radiance spectra and L2 full physics retrievals. By beginning with perfectly simple scenes and gradually adding realistic complexity (clouds and aerosols, forward model errors, etc.), we attempt to confirm the hypothesis that these biases are a natural result of an imperfect forward model. We further discuss how to minimize these biases for both present missions (GOSAT, OCO-2, OCO-3, TanSat) and future missions (e.g. MicroCARB) that may be subject to this kind of bias.
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