Preprints
https://doi.org/10.5194/gmd-2021-238
https://doi.org/10.5194/gmd-2021-238

Submitted as: model experiment description paper 19 Aug 2021

Submitted as: model experiment description paper | 19 Aug 2021

Review status: this preprint is currently under review for the journal GMD.

How well can inverse analyses of high-resolution satellite data resolve heterogeneous methane fluxes? Observation System Simulation Experiments with the GEOS-Chem adjoint model (v35)

Xueying Yu1, Dylan B. Millet1, and Daven K. Henze2 Xueying Yu et al.
  • 1Department of Soil, Water, and Climate, University of Minnesota, Saint Paul, Minnesota 55108, United States
  • 2Department of Mechanical Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States

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.

Xueying Yu et al.

Status: open (until 14 Oct 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Xueying Yu et al.

Model code and software

Code Updates of GEOS-Chem Adjoint v35 for TROPOMI Methane 4D-Var Inversion Xueying Yu, Dylan B. Millet, Daven K. Henze https://doi.org/10.13020/g5xc-nj81

Xueying Yu et al.

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Short summary
We conduct Observing System Simulation Experiments to test how well inverse analyses of high-resolution satellite data from sensors such as TROPOMI can quantify methane emissions. Inversions can improve monthly flux estimates at 25 km even with a spatially biased prior or model transport errors, but results are strongly degraded when both are present. We further evaluate a set of alternate formalisms to overcome limitations of the widely used scale-factor approach that arise for missing sources.