We use the GEOS-Chem global 3-D atmospheric chemistry transport
model to interpret
Distribution of the 13 geographical regions for which we
estimate
Space-borne atmospheric column measurements of
Two methods have been used to retrieve
In the following section we describe the space-borne and ground-based
data used in our experiments. In Sect.
The number of GOSAT observations available per month during
2010 over specific geographical regions (Fig.
GOSAT was launched in 2009 by the Japanese Space Agency in
a sun-synchronous orbit with an equatorial local overpass time of
13:00 LT, providing global coverage every
three days
A priori sources of carbon dioxide and methane used in the GEOS-Chem model for 2010.
We provide a brief description of the proxy retrieval algorithm used
for
We use cloud-screening and
As described in Sect.
Monthly a priori emissions for
We use version v9-01-03 of the GEOS-Chem global 3-D atmospheric
chemistry transport model
The
We use an inverse model that finds the maximum a posteriori (MAP)
solution
For our implementation,
We construct
The measurement vector
We construct
The Jacobian matrix,
Figure
Figure
Annual mean GOSAT (top row) and GEOS-Chem model (second row)
Figure
Figure
We use OSSEs, realistic numerical experiments, to characterize the
method we use to estimate simultaneously
We conduct four broad sets of OSSEs: (1) those that use only the
GOSAT
Figure
GOSAT and GEOS-Chem daily mean
Figure
Annual regional flux estimates of
In experiment set (4) (not shown) we assess the impact of a prescribed
observation bias to the GOSAT data on the a posteriori flux estimates;
assuming that the surface data is unbiased or at least can be
identified readily via ongoing calibration/validation activities. We
assume a latitudinally varying bias that is superimposed onto the
“true” atmospheric measurements plus random error
(0.005
As Fig.
Figure
A priori and a posteriori
For
A priori and a posteriori
For
We have interpreted measurements of
Using a series of numerical experiments we showed that the
simultaneous estimation of
Using real data for 2010 we showed that the combination of the GOSAT
The main reasons for using the
We thank Doug Worthy for the Environment Canada data. NOAA ESRL is supported by NOAA's Climate Program Office; and CSIRO research at Cape Grim is supported by the Australian Bureau of Meteorology. A. Fraser and R. Parker were supported by the Natural Environment Research Council National Centre for Earth Observation (NCEO). L. Feng was partly funded by the “Data Assimilation Project – Interfacing EO data with atmospheric and land surface models” ESA contract 4000104980/1-LG. H. Bösch, R. Parker and L. Feng also acknowledge funding by the ESA Climate Change Initiative (GHG-CCI). P. I. Palmer gratefully acknowledges his Royal Society Wolfson Research Merit Award. Edited by: B. N. Duncan