Comparison of adjoint and analytical Bayesian inversion methods for constraining Asian sources of carbon monoxide using satellite (MOPITT) measurements of CO columns

Monika Kopacz,Daniel J Jacob,Daven K Henze,Qiang Zhang,Colette L Heald,David G Streets

doi:10.1029/2007jd009264

Abstract

We apply the adjoint of an atmospheric chemical transport model (GEOS‐Chem CTM) to constrain Asian sources of carbon monoxide (CO) with 2° × 2.5° spatial resolution using Measurement of Pollution in the Troposphere (MOPITT) satellite observations of CO columns in February–April 2001. Results are compared to the more common analytical method for solving the same Bayesian inverse problem and applied to the same data set. The analytical method is more exact but because of computational limitations it can only constrain emissions over coarse regions. We find that the correction factors to the a priori CO emission inventory from the adjoint inversion are generally consistent with those of the analytical inversion when averaged over the large regions of the latter. The adjoint solution reveals fine‐scale variability (cities, political boundaries) that the analytical inversion cannot resolve, for example, in the Indian subcontinent or between Korea and Japan, and some of that variability is of opposite sign which points to large aggregation errors in the analytical solution. Upward correction factors to Chinese emissions from the prior inventory are largest in central and eastern China, consistent with a recent bottom‐up revision of that inventory, although the revised inventory also sees the need for upward corrections in southern China where the adjoint and analytical inversions call for downward correction. Correction factors for biomass burning emissions derived from the adjoint and analytical inversions are consistent with a recent bottom‐up inventory on the basis of MODIS satellite fire data.

Highlights

[2] Inverse modeling is a standard tool for combining observations of atmospheric composition with knowledge of atmospheric processes to derive quantitative constraints on emissions to the atmosphere
The cost function describes the errorweighted mismatch between the observed concentrations, y, and those simulated with the forward model, F(x), as well as the error-weighted mismatch between the true state and the a priori estimate xa
A large state vector x is desirable in applying the inverse method to satellite observations, where one would like to exploit the richness of the data to constrain emissions with high spatial and temporal resolution, limited only by the resolution of the chemical transport model (CTM) used as the forward model

Summary

Introduction

[2] Inverse modeling is a standard tool for combining observations of atmospheric composition with knowledge of atmospheric processes (transport, chemistry) to derive quantitative constraints on emissions to the atmosphere. D04305 and we refer to this here as the ‘‘analytical method.’’ It has been applied extensively for example for inverse modeling of CO2 surface fluxes and CO emissions using observations from surface sites [Bousquet et al, 1999; Bergamaschi et al, 2000; Kasibhatla et al, 2002; Petron et al, 2002] and aircraft [Palmer et al, 2003; Palmer et al, 2006] Computing this analytical solution involves construction of the CTM Jacobian matrix (K = @y/@x) and subsequent multiplication and inversion of matrices with dimensions of dim(x) and dim(y). The ‘‘best case’’ MAP solution reported by Heald et al [2004] used a slightly narrower latitudinal domain (0°– 55°N) and included chi-square filtering of outliers, reducing the total number of observations to

A Posteriori

Implementation of the Adjoint Method

Findings

Conclusions