Sensitivity analysis of methane emissions derived from SCIAMACHY observations through inverse modelling

Abstract

Abstract. Satellite observations of trace gases in the atmosphere offer a promising method for global verification of emissions and improvement of global emission inventories. Here, an inverse modelling approach based on four-dimensional variational (4D-var) data assimilation is presented and applied to synthetic measurements of atmospheric methane. In this approach emissions and initial concentrations are optimised simultaneously, thus allowing inversions to be carried out on time scales of weeks to months, short compared with the lifetime of methane. Observing System Simulation Experiments (OSSEs) have been performed to demonstrate the feasibility of the method and to investigate the utility of SCIAMACHY observations for methane source estimation. The impact of a number of parameters on the error in the methane emission field retrieved has been analysed. These parameters include the measurement error, the error introduced by the presence of clouds, and the spatial resolution of the emission field. It is shown that 4D-var is an efficient method to deal with large amounts of satellite data and to retrieve emissions at high resolution. Some important conclusions regarding the SCIAMACHY measurements can be drawn: (i) the observations at their estimated precision of 1.5 to 2% will contribute considerably to uncertainty reduction in monthly, subcontinental (~500 km) methane source strengths; (ii) it is essential to take partly cloudy pixels into account in order to achieve sufficient spatial coverage; and (iii) the uncertainty in measured cloud parameters may at some point become the limiting factor, rather than the uncertainty in measured methane.

Comparison of CH4 inversions based on 15 months of GOSAT and SCIAMACHY observations

“…Emission error correlations (off‐diagonal terms of B ) are modeled using a Gaussian function of the distance between grid cells (both in time and space), using spatial and temporal correlation lengths of, respectively, 500km and 1month. A similar 500km horizontal correlation length is used for the a priori CH 4 concentration field, in combination with a vertical correlation determined using the National Meteorological Center (NMC) method [ Parrish and Derber , ; Meirink et al , ].…”

Four‐dimensional variational data assimilation for inverse modeling of atmospheric methane emissions: Analysis of SCIAMACHY observations

“…Spatial correlations are modeled as Gaussian functions of the distance between grid cells, with correlation lengths for the various source categories and L c for the initial concentration field. Temporal correlations are modeled as exponential functions of the time difference, with correlation time scales Vertical correlations of errors in the initial concentration field have been determined with the National Meteorological Center (NMC) method as outlined in Meirink et al [2006].…”

Methane retrievals from Greenhouse Gases Observing Satellite (GOSAT) shortwave infrared measurements: Performance comparison of proxy and physics retrieval algorithms

“…Surface elevation is extracted from the GTOPO30 digital elevation model. Initial guess profiles for methane are provided by a TM4 global transport model run for 2007 [ Meirink et al , 2006]. The initial carbon dioxide profiles come from Carbon Tracker 2010 simulations [ Peters et al , 2007] for the year 2009.…”