Preprints
https://doi.org/10.5194/amt-2022-241
https://doi.org/10.5194/amt-2022-241
 
26 Aug 2022
26 Aug 2022
Status: this preprint is currently under review for the journal AMT.

Exploring bias in OCO-3 Snapshot Area Mapping mode via geometry, surface, and aerosol effects

Emily Bell1, Thomas E. Taylor1, Aronne Merrelli2, Christopher W. O'Dell1, Robert R. Nelson3, Matthäus Kiel3, Annmarie Eldering3, Robert Rosenberg3, and Brendan Fisher3 Emily Bell et al.
  • 1Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, 80521, USA
  • 2Department of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
  • 3Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109, USA

Abstract. The Atmospheric Carbon Observations from Space (ACOS) retrieval algorithm has been delivering operational column-averaged carbon dioxide dry-air mole fraction (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. Past analysis has shown that while the ACOS retrieval meets mission precision requirements of 0.1–0.5 % in XCO2, residual biases and some sources of error remain unaccounted for (Wunch et al., 2017; Worden et al., 2017; Torres et al., 2019). Forward model and other errors can lead to systematic biases in the retrieved XCO2, which are often correlated with these additional retrieved parameters. The characterization of such biases is particularly essential to urban- and local-scale emissions studies, where it is critical to accurately distinguish source signals relative to background concentrations (Nassar et al., 2017; Kiel et al., 2021). In this study we explore algorithm-induced biases through the use of simulated OCO-3 Snapshot Area Mapping (SAM) mode observations, which offer a unique window into these biases with their wide range of viewing geometries over a given scene. We focus on a small percentage of SAMs in the OCO-3 vEarly product which contain artificially strong across-swath XCO2 biases spanning several parts per million, related to observation geometry. We investigate the causes of swath bias by using the timing and geometry of real OCO-3 SAMs to retrieve XCO2 from custom simulated L1b radiance spectra. By building relatively simple scenes and testing a variety of parameters, we find that aerosol is the primary driver of swath bias, with a complex combination of viewing geometry and aerosol optical properties contributing to the strength and pattern of the bias. Finally, we seek to understand successful mitigation of swath bias in the OCO-3 version 10 data product. Results of this study may be useful in uncovering other remaining sources of XCO2 bias, and may help minimize similar retrieval biases for both present missions (GOSAT, GOSAT-2, OCO-2, OCO-3, TanSat) and future missions (e.g. MicroCARB, GeoCarb, GOSAT-GW, CO2M).

Emily Bell et al.

Status: open (until 15 Oct 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2022-241', Anonymous Referee #1, 23 Sep 2022 reply
  • RC2: 'Comment on amt-2022-241', Anonymous Referee #2, 25 Sep 2022 reply

Emily Bell et al.

Emily Bell et al.

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Short summary
A small percentage of data from the Orbiting Carbon Observatory - 3 (OCO-3) instrument have been shown to have a geometry-related bias in the earliest public data release. This work shows that the bias is due to a complex interplay of aerosols and viewing geometry, and is largely mitigated in the latest data version through improved bias correction and quality filtering.