A comparative analysis of retrieval algorithms for GOSAT SWIR data processing: comparison against TCCON and transport model, cross-comparison, and effects of atmospheric light scattering
Abstract
Retrieval of precise and accurate trace gas abundances from radiance spectra of reflected sunlight from space requires accurately calculating the atmospheric light path. This report focuses on the effects of atmospheric light scattering on spectroscopic space-based observations of carbon dioxide (CO2). We summarize the results from six algorithms (ACOS B2.9, NIES 02.xx, NIES PPDF-D, NIES PPDF-S, RemoTeC, and UoL FP: 3G), which retrieve column-averaged dry air mole fractions of CO2 (XCO2) from spectra obtained by the Greenhouse Gases Observing SATellite (GOSAT) TANSO-FTS from June 2009 to March 2011. First, we compare data products from each algorithm with ground-based remote sensing observations by Fourier transform spectrometers of the Total Carbon Column Observing Network (TCCON). Our GOSAT-TCCON coincidence criteria select satellite observations over land within a 5° radius of 11 TCCON sites. We have compared the GOSAT-TCCON XCO2 standard deviation, correlation and determination coefficients, global and station-to-station biases, as well as the number of coincident observations for each algorithm. Next, the impact of atmospheric light scattering on XCO2 retrievals from each data product is estimated using the photon path length probability density function (PPDF) method. Approximately 25% of GOSAT soundings processed by the various retrieval algorithms were found to be contaminated by atmospheric light scattering, primarily due to increased optical path lengths over Northern hemispheric TCCON sites from May-September of each year. These studies, and the comparison of the satellite data with a global atmospheric transport model, suggest that most of the algorithms tend to overestimate aerosol amounts, resulting in an underestimation of XCO2 over bright surfaces. We discuss the utility of GOSAT XCO2 retrievals for improving flux inversions, focusing on the global and regional bias variability of each algorithm. Insights from this report will enable further atmospheric light scattering algorithm development.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2012
- Bibcode:
- 2012AGUFM.A41N..07O
- Keywords:
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- 0300 ATMOSPHERIC COMPOSITION AND STRUCTURE