CH₄ retrievals from space‐based solar backscatter measurements: Performance evaluation against simulated aerosol and cirrus loaded scenes

Abstract

[1] Monitoring of atmospheric methane (CH 4 ) concentrations from space-based instruments such as the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) and the Greenhouse Gases Observing Satellite (GOSAT) relies on observations of sunlight backscattered to space by the Earth's surface and atmosphere. Retrieval biases occur due to unaccounted scattering effects by aerosols and thin cirrus that modify the lightpath. Here, we evaluate the accuracy of two retrieval methods that aim at minimizing such scattering induced errors. The lightpath "proxy" method, applicable to SCIAMACHY and GOSAT, retrieves CH 4 and carbon dioxide (CO 2 ) simultaneously and uses CO 2 as a proxy for lightpath modification. The "physics-based" method, which we propose for GOSAT, aims at simultaneously retrieving CH 4 concentrations and scattering properties of the atmosphere. We evaluate performance of the methods against a trial ensemble of simulated aerosol and cirrus loaded scenes. More than 80% of the trials yield residual scattering induced CH 4 errors below 0.6% and 0.8% for the proxy and the physics-based approach, respectively. Very few cases result in errors greater than 2% for both methods. Advantages of the proxy approach are efficient and robust performance yielding more useful retrievals than the physics-based method which reveals some nonconvergent cases. The major disadvantage of the proxy method is the uncertainty of the proxy CO 2 concentration contributing to the overall error budget. Residual errors generally correlate with particle and surface properties and thus might impact inverse modeling of CH 4 sources and sinks.Citation: Butz, A., O. P. Hasekamp, C. Frankenberg, J. Vidot, and I. Aben (2010), CH 4 retrievals from space-based solar backscatter measurements: Performance evaluation against simulated aerosol and cirrus loaded scenes,

A multi-year methane inversion using SCIAMACHY, accounting for systematic errors using TCCON measurements

“…A good example is the study by Wunch et al (2011b) on the use of TCCON data for evaluating systematic errors in ACOS-GOSAT XCO 2 retrievals. For SCIAMACHY XCH 4 , we constructed a model of known retrieval uncertainties on the basis of Butz et al (2010), accounting for spectroscopic errors varying with the sampled air mass and residual aerosol errors varying with the difference in surface albedo between the weak short-wave infra-red (SWIR) CO 2 and CH 4 absorption bands. Optimization of the bias coefficients in the inversion, however, turned out to be rather inefficient in correcting apparent inconsistencies in the optimized fits to surface and total column measurements (not shown).…”

A Singular Value Decomposition framework for retrievals with vertical distribution information from greenhouse gas column absorption spectroscopy measurements

“…The SVD approach described here comes closest to the one applied for satellite methane retrievals (Butz et al, 2010), but performs the retrieval in the principal component basis to eliminate bias originating from the choice of the uninformative prior used (see section 3.5). Components in the reduced dimensional principal component space can be directly assimilated into flux models similar to the way X CO2 is presently assimilated (Basu et al, 2013).…”

“…Borsdorff et al (2014) present a review of the SVD and related methods in the context of trace gas retrievals and the connections to the traditional OE as well as simple profile 15 scaling methods. The SVD method has also been applied to remote sensing for ozone (Hasekamp and Landgraf, 2001) and methane (Butz et al, 2010). Previous work has used the SVD method primarily to regularize the underdetermined retrieval problem, but also for computational efficiency and to eliminate the need for knowledge of the prior distribution.…”

Validation of TANSO-FTS/GOSAT XCO<sub>2</sub> and XCH<sub>4</sub> glint mode retrievals using TCCON data from near-ocean sites

“…Almost all the biases for ocean and land data at all sites are within 0.5 %, and the scatters are within 1.0 %; this means that they meet the precision threshold quality criteria for inverse modelling (34 ppb) together with low bias (10 ppb). Although SRFP and SRPR are both derived from the RemoTeC algorithm, the proxy version (SRPR) has a larger data density than the full physics version (SRFP) because with the latter, a post- filter is applied that sets a threshold on the scattering parameters (Butz et al, 2010) . Averaged over all TCCON sites, the relative bias with 95 % confidence intervals of ocean data is less than that of the land data for NIES (0.02 ± 0.032 % vs.…”