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| Titel |
Characterization of Tropospheric Emission Spectrometer (TES) CO2 for carbon cycle science |
| VerfasserIn |
S. S. Kulawik, D. B. A. Jones, R. Nassar, F. W. Irion, J. R. Worden, K. W. Bowman, T. Machida, H. Matsueda, Y. Sawa, S. C. Biraud, M. L. Fischer, A. R. Jacobson |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 10, no. 12 ; Nr. 10, no. 12 (2010-06-25), S.5601-5623 |
| Datensatznummer |
250008573
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| Publikation (Nr.) |
 copernicus.org/acp-10-5601-2010.pdf |
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| Zusammenfassung |
| We present carbon dioxide (CO2) estimates from the Tropospheric
Emission Spectrometer (TES) on the EOS-Aura satellite launched in 2004. For
observations between 40° S and 45° N, we find about 1 degree of
freedom with peak sensitivity at 511 hPa. The estimated error is ~10 ppm
for a single target and 1.3–2.3 ppm for monthly averages on spatial
scales of 20°×30°. Monthly spatially-averaged TES data from
2005–2008 processed with a uniform initial guess and prior are compared to
CONTRAIL aircraft data over the Pacific ocean, aircraft data at the Southern
Great Plains (SGP) ARM site in the southern US, and the Mauna Loa and
Samoa surface stations. Comparisons to Mauna Loa data show a correlation of
0.92, a standard deviation of 1.3 ppm, a predicted error of 1.2 ppm, and a
~2% low bias, which is subsequently corrected. Comparisons to SGP
aircraft data over land show a correlation of 0.67 and a standard deviation
of 2.3 ppm. TES data between 40° S and 45° N for 2006–2007 are
compared to surface flask data, GLOBALVIEW, the Atmospheric Infrared Sounder
(AIRS), and CarbonTracker. Comparison to GLOBALVIEW-CO2 ocean surface
sites shows a correlation of 0.60 which drops when TES is offset in
latitude, longitude, or time. At these same locations, TES shows a 0.62 and
0.67 correlation to CarbonTracker at the surface and 5 km, respectively. We
also conducted an observing system simulation experiment to assess the
potential utility of the TES data for inverse modeling of CO2 fluxes.
We find that if biases in the data and model are well characterized, the
averaged data have the potential to provide sufficient information to
significantly reduce uncertainty on annual estimates of regional CO2
sources and sinks. Averaged pseudo-data at 10°×10° reduced
uncertainty in flux estimates by as much as 70% for some tropical regions. |
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