Global height-resolved methane retrievals from the Infrared Atmospheric Sounding Interferometer (IASI) on MetOp
- 1STFC Rutherford Appleton Laboratory, Chilton, UK
- 2National Centre for Earth Observation, Leicester, UK
- 3Earth Observation Science, University of Leicester, Leicester, UK
Abstract. This paper describes the global height-resolved methane (CH4) retrieval scheme for the Infrared Atmospheric Sounding Interferometer (IASI) on MetOp, developed at the Rutherford Appleton Laboratory (RAL). The scheme precisely fits measured spectra in the 7.9 micron region to allow information to be retrieved on two independent layers centred in the upper and lower troposphere. It also uses nitrous oxide (N2O) spectral features in the same spectral interval to directly retrieve effective cloud parameters to mitigate errors in retrieved methane due to residual cloud and other geophysical variables. The scheme has been applied to analyse IASI measurements between 2007 and 2015. Results are compared to model fields from the MACC greenhouse gas inversion and independent measurements from satellite (GOSAT), airborne (HIPPO) and ground (TCCON) sensors. The estimated error on methane mixing ratio in the lower- and upper-tropospheric layers ranges from 20 to 100 and from 30 to 40 ppbv, respectively, and error on the derived column-average ranges from 20 to 40 ppbv. Vertical sensitivity extends through the lower troposphere, though it decreases near to the surface. Systematic differences with the other datasets are typically < 10 ppbv regionally and < 5 ppbv globally. In the Southern Hemisphere, a bias of around 20 ppbv is found with respect to MACC, which is not explained by vertical sensitivity or found in comparison of IASI to TCCON. Comparisons to HIPPO and MACC support the assertion that two layers can be independently retrieved and provide confirmation that the estimated random errors on the column- and layer-averaged amounts are realistic. The data have been made publically available via the Centre for Environmental Data Analysis (CEDA) data archive (Siddans, 2016).