How well can inverse analyses of high-resolution satellite data resolve
heterogenous methane fluxes?
Abstract
We perform Observation System Simulation Experiments (OSSEs) with the
GEOS-Chem adjoint model to test how well methane emissions over North
America can be resolved using measurements from the TROPOspheric
Monitoring Instrument (TROPOMI) and similar high-resolution satellite
sensors. We focus analysis on the impacts of i) spatial errors in the
prior emissions, and ii) model transport errors. Along with a standard
scale-factor (SF) optimization we conduct a set of inversions using
alternative formalisms that aim to overcome limitations in the SF-based
approach that arise for missing sources. We show that 4D-Var analysis of
the TROPOMI data can improve monthly emission estimates at 25 km even
with a spatially biased prior or model transport errors (42–93%
domain-wide bias reduction; R increases from 0.51 up to 0.73). However,
when both errors are present, no single inversion framework can
successfully improve both the overall bias and spatial distribution of
fluxes relative to the prior on the 25 km model grid. In that case, the
ensemble-mean optimized fluxes have a domain-wide bias of 77 Gg/d
(comparable to that in the prior), with spurious source adjustments
compensating for the transport errors. Increasing observational coverage
through longer-timeframe inversions does not significantly change this
picture. An inversion formalism that optimizes emission enhancements
rather than scale factors exhibits the best performance for identifying
missing sources, while an approach combining a uniform background
emission with the prior inventory yields the best performance in terms
of overall spatial fidelity—even in the presence of model transport
errors. However, the standard SF optimization outperforms both of these
for the magnitude of the domain-wide flux. For the common scenario in
which prior errors are non-random, approximate posterior error reduction
calculations for the inversions reflect the sensitivity to observations
but have no spatial correlation with the actual emission improvements.
This demonstrates that such information content analysis can be used for
general observing system characterization but does not describe the
spatial accuracy of the posterior emissions or of the actual emission
improvements. Findings here highlight the need for careful evaluation of
potential missing sources in prior emission datasets and for robust
accounting of model transport errors in inverse analyses of the methane
budget.