References

ACP

Atmospheric Chemistry and Physics

ACP

1680-7324

Copernicus GmbH

Göttingen, Germany

10.5194/acp-6-3517-2006

Measuring atmospheric CO2 from space using Full Spectral Initiation (FSI) WFM-DOAS

Barkley

M. P.

¹ Frieß

¹ ³ Monks

P. S.

EOS, Space Research Centre, Department of Physics & Astronomy, University of Leicester, Leicester, LE1 7RH, UK

Department of Chemistry, University of Leicester, Leicester, LE1 7RH, UK

now at: Institute of Environmental Physics, Heidelberg, Germany

30 08 2006

6 11 3517 3534

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Satellite measurements of atmospheric CO2 concentrations are a rapidly evolving area of scientific research which can help reduce the uncertainties in the global carbon cycle fluxes and provide insight into surface sources and sinks. One of the emerging CO2 measurement techniques is a relatively new retrieval algorithm called Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS) that has been developed by Buchwitz et al. (2000). This algorithm is designed to measure the total columns of CO2 (and other greenhouse gases) through the application to spectral measurements in the near infrared (NIR), made by the SCIAMACHY instrument on-board ENVISAT. The algorithm itself is based on fitting the logarithm of a model reference spectrum and its derivatives to the logarithm of the ratio of a measured nadir radiance and solar irradiance spectrum. In this work, a detailed error assessment of this technique has been conducted and it has been found necessary to include suitable a priori information within the retrieval in order to minimize the errors on the retrieved CO2 columns. Hence, a more flexible implementation of the retrieval technique, called Full Spectral Initiation (FSI) WFM-DOAS, has been developed which generates a reference spectrum for each individual SCIAMACHY observation using the estimated properties of the atmosphere and surface at the time of the measurement. Initial retrievals over Siberia during the summer of 2003 show that the measured CO2 columns are not biased from the input a priori data and that whilst the monthly averaged CO2 distributions contain a high degree of variability, they also contain interesting spatial features.

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