Methane observations from the Greenhouse Gases Observing SATellite: Comparison to ground-based TCCON data and model calculations
Article first published online: 6 AUG 2011
Copyright 2011 by the American Geophysical Union.
Geophysical Research Letters
Volume 38, Issue 15, August 2011
How to Cite
2011), Methane observations from the Greenhouse Gases Observing SATellite: Comparison to ground-based TCCON data and model calculations, Geophys. Res. Lett., 38, L15807, doi:10.1029/2011GL047871., et al. (
- Issue published online: 6 AUG 2011
- Article first published online: 6 AUG 2011
- Manuscript Accepted: 27 JUN 2011
- Manuscript Revised: 21 JUN 2011
- Manuscript Received: 20 APR 2011
 We report new short-wave infrared (SWIR) column retrievals of atmospheric methane (XCH4) from the Japanese Greenhouse Gases Observing SATellite (GOSAT) and compare observed spatial and temporal variations with correlative ground-based measurements from the Total Carbon Column Observing Network (TCCON) and with the global 3-D GEOS-Chem chemistry transport model. GOSAT XCH4 retrievals are compared with daily TCCON observations at six sites between April 2009 and July 2010 (Bialystok, Park Falls, Lamont, Orleans, Darwin and Wollongong). GOSAT reproduces the site-dependent seasonal cycles as observed by TCCON with correlations typically between 0.5 and 0.7 with an estimated single-sounding precision between 0.4–0.8%. We find a latitudinal-dependent difference between the XCH4 retrievals from GOSAT and TCCON which ranges from 17.9 ppb at the most northerly site (Bialystok) to −14.6 ppb at the site with the lowest latitude (Darwin). We estimate that the mean smoothing error difference included in the GOSAT to TCCON comparisons can account for 15.7 to 17.4 ppb for the northerly sites and for 1.1 ppb at the lowest latitude site. The GOSAT XCH4 retrievals agree well with the GEOS-Chem model on annual (August 2009 – July 2010) and monthly timescales, capturing over 80% of the zonal variability. Differences between model and observed XCH4 are found over key source regions such as Southeast Asia and central Africa which will be further investigated using a formal inverse model analysis.