The regional uncertainty of the column-averaged dry air
mole fraction of
The column-averaged dry air mole fraction of
Retrieval of
With the advantage of continuity in space and time, atmospheric
transport model simulation of
In this paper, we focus on the latitude band of 37–42
First, we aim to reveal the regional uncertainty of
The latitude band of 37–42
We collected
Summary of validating results with TCCON, data screening schemes, consideration in scattering and bias corrections for the four retrieval algorithms.
Within the study area, the total numbers of valid GOSAT
Number of single scans from the four GOSAT-
We use GEOS-Chem version 10-01 driven by GEOS-5 and the details of the
main input emissions are as follows: (1) fossil fuel fluxes are taken
from the new emission data set CHRED for the Chinese mainland, we also
use ODIAC version 2013 for comparison with CHRED. (2) The balanced
biosphere
Figure 3 presents the spatial difference of emissions over the Chinese
mainland between CHRED and ODIAC at a horizontal resolution of
Difference of annual total anthropogenic
For each
The annually averaged
Annual mean of
We compared GEOS-Chem
Statistics of comparison between GEOS-Chem
The results from Table 2 show that the bias ranges from
The monthly mean aerosol optical depth (AOD) data were collected from
the NASA Earth Observing System's Multi-angle Imaging
Spectro-Radiometer (MISR) Level 3 Component Global Aerosol Product,
downloaded from the website
Correlation diagrams of GOSAT
The biases (ppm) and their SDs (ppm) of the four
algorithms vs. GEOS-Chem in each cell, where the upper line indicates bias
(the corresponding SDs in parenthesis) for each algorithm vs.
GEOS-Chem and the lower line is the available number of used samples. The
biases, larger than 1
We focus on the difference of each footprint
We used the nested GEOS-Chem
It can be seen from Fig. 5 that the linear fits and the correlations
with GEOS-Chem are better for ACOS and OCFP (
Table 3 shows the biases and number of samples used between each
algorithm and GEOS-Chem in each cell. It can be seen that the biases
of ACOS and SRFP vs. GEOS-Chem in all cells are below 1
We made comparisons of geometrically and temporally
matching pairs
Algorithm correlation diagrams and statistical
characteristics (insets of panels). GOSAT-Y observations were
selected over land within
It can be seen from Fig. 6 that ACOS generally demonstrates the best
agreement with other algorithms (top panel). OCFP generally presents
biases larger than 1.4
The differences (biases) of matching pairs (the number ranging from 11
to 945) of
Differences (ppm) between the two algorithms (column algorithm minus row
algorithm) and the corresponding SD (ppm) for each cell, where values
in parentheses are the corresponding SDs. The differences larger than
1.5
The columns labeled with
The average of the absolute differences (ppm) and SD (ppm) of the target algorithm (in column) matching all other
algorithms for each cell. Values in parentheses are the corresponding
SDs. The differences, which are larger than 1.5
It can be seen from Table 4 that the difference is mostly less than
1
To summarize the quantification and analysis in this section,
We used a combination of sine and cosine trigonometric functions to
statistically fit the seasonal variation of
Firstly, the monthly averaged
The accuracy of fitting
The time series in each cell are acquired for each algorithm using the
above formula Eq. (1). The monthly fitted
The time series from March 2010 to February 2013 in eight
cells from the western cell
Results of fitted seasonal cycle and the corresponding uncertainty of
the fitting results for each cell in the study latitude band for four
algorithms and GEOS-Chem, The symbols “–” means that filtered
results are not available due to large uncertainty judged by
Viewing the attribution of
Comparing
The
We calculated the seasonal averages of the
It can be seen from Fig. 8 (on the left) that the spatio-temporal
patterns from the three algorithms of ACOS, NIES and SRFP are
generally similar, with an increase spreading outward from the center
of each diagram and with the lowest
Compared to the difference to GEOS-Chem (on the right in Fig. 8), the
spatio-temporal pattern of ACOS and SRFP generally demonstrate the
smallest values mostly ranging from
In this section, an investigation was made into the most likely attribution of regional inconsistency, i.e., aerosols and albedo. An additional comparison was also made with the latest released ACOS V7.3, the newer version of ACOS data retrieved by the OCO-2 algorithm, using GEOS-Chem simulations and retrievals from other algorithms including ACOS V3.5, NIES V02.21, OCFP V6.0 and SRFP V2.3.7.
The above quantification and analyses indicate that good
agreement is generally achieved among the four data sets in the eastern cells,
with three out of four GOSAT-
The spatial and temporal characteristics of shortwave broadband
(300–3000
The temporal and spatial patterns of black sky shortwave
broadband (300–3000
As shown in Fig. 9, albedo shows small temporal variation with
a decreasing trend from west to east. In contrast to albedo, AOD
follows a clear seasonal pattern with a higher level in spring and
summer than in autumn and winter. The uplift of AOD in spring and
summer is due to the higher frequency of Asian sand and dust storms
for cells west of 105
We discussed the influences of albedo and AOD on
Figure 10 shows that dm
Scatter plots of the differences (dm
Figure 11 additionally demonstrates the influence of albedo and AOD on
the SD (SD) of
The increasing trends of SD with both albedo and AOD can be seen from
Fig. 11. The mean SD is 1.3
Scatter plots of the SD (SD) of
From the above quantification and analyses displayed in previous sections, the
pairwise differences between OCFP and other algorithms are found to be
0.5
The regional pairwise difference between NIES and other algorithms is
up to 1.6
With the satellite-observed spectrum used for simultaneously retrieving water and clouds, ACOS sets the initial aerosol types and AOD based on a priori information from aerosol reanalysis data. SRFP in comparison, handles aerosol based on a comprehensive characterization of aerosol properties, including aerosol number density, size distribution and aerosol height. Both of the above two mechanisms function well as ACOS and SRFP are generally demonstrated to provide relatively better performance.
Noticing that all algorithms differ in simulating scattering in the
atmosphere, such as in the aerosol models, the influence of scattering
on retrieved
We also utilized ACOS V7.3 (
Compared to the previous version, ACOS V3.5, ACOS V7.3 increases the
average by approximately 0.2
The comparison results further demonstrate inconsistency of
Summaries of our analyses for uncertainty of X
Although TCCON has been widely accepted as the standard for validation
of satellite-based
Summarizing the performance of four algorithms (ACOS, NIES, OCFP and
SRFP) in each cell based on the above quantification and analysis from
comparisons with GEOS-Chem, pairwise differences between algorithms
and agreement in time series among algorithms, we can obtain the
following general results : (1) The consistency among algorithms is
better in the east than in the west as the absolute difference from
pairwise comparisons presents 0.7–1.1
The results of our analysis, indicating that the discrepancies among
algorithms are the smallest in eastern cells, which are the strongest
anthropogenic emitting source regions in China, implies that the
uncertainty of
ACOS V3.5 data are available both from the Goddard Data Center (GES-DISC, 2016) and JPL's CO
The time series of data points from ACOS V7.3 during the period from March 2010 to February 2013. Different symbols in each panel represent the left longitude of the cell into which a data point falls.
We made cross-comparisons between ACOS V7.3 and other data sets. The
available data points of ACOS V7.3 are shown from March 2010 to
February 2013 in Fig. A1. In cells west of 90
The comparison results in the cells are shown in Table A1. No bias was
found in ACOS V7.3 from GEOS-Chem with a SD of 1.6
Differences between ACOS V7.3 and the other five data sets utilized (including GEOS-Chem and the four other algorithms, namely: ACOS V3.5, NIES, OCFP and SRFP) in each cell (subtraction from ACOS V7.3). Values in parentheses are the corresponding SDs. The differences larger than 1.5 ppm are highlighted in bold.
The authors declare that they have no conflict of interest.
This research was supported by the National Research Program on
Global Changes and Adaptation: “Big data on global changes: data
sharing platform and recognition” (grant no. 2016YFA0600303,
2016YFA0600304). We are grateful for: NIES products from NIES GOSAT
Project; albedo data from Beijing Normal University;