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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 5, issue 1
Atmos. Meas. Tech., 5, 99–121, 2012
https://doi.org/10.5194/amt-5-99-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Meas. Tech., 5, 99–121, 2012
https://doi.org/10.5194/amt-5-99-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 11 Jan 2012

Research article | 11 Jan 2012

The ACOS CO2 retrieval algorithm – Part 1: Description and validation against synthetic observations

C. W. O'Dell1, B. Connor2, H. Bösch3, D. O'Brien1, C. Frankenberg4, R. Castano4, M. Christi1, D. Eldering4, B. Fisher4, M. Gunson4, J. McDuffie4, C. E. Miller4, V. Natraj4, F. Oyafuso4, I. Polonsky1, M. Smyth4, T. Taylor1, G. C. Toon4, P. O. Wennberg5, and D. Wunch5 C. W. O'Dell et al.
  • 1Colorado State University, Fort Collins, CO, USA
  • 2BC Consulting, Ltd., Alexandra, New Zealand
  • 3University of Leicester, Leicester, UK
  • 4Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
  • 5California Institute of Technology, Pasadena, CA, USA

Abstract. This work describes the NASA Atmospheric CO2 Observations from Space (ACOS) XCO2 retrieval algorithm, and its performance on highly realistic, simulated observations. These tests, restricted to observations over land, are used to evaluate retrieval errors in the face of realistic clouds and aerosols, polarized non-Lambertian surfaces, imperfect meteorology, and uncorrelated instrument noise. We find that post-retrieval filters are essential to eliminate the poorest retrievals, which arise primarily due to imperfect cloud screening. The remaining retrievals have RMS errors of approximately 1 ppm. Modeled instrument noise, based on the Greenhouse Gases Observing SATellite (GOSAT) in-flight performance, accounts for less than half the total error in these retrievals. A small fraction of unfiltered clouds, particularly thin cirrus, lead to a small positive bias of ~0.3 ppm. Overall, systematic errors due to imperfect characterization of clouds and aerosols dominate the error budget, while errors due to other simplifying assumptions, in particular those related to the prior meteorological fields, appear small.

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