Improving the TROPOMI CO data product: update of the spectroscopic database and destriping of single orbits
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Published:2019-10-15
Issue:10
Volume:12
Page:5443-5455
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ISSN:1867-8548
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Container-title:Atmospheric Measurement Techniques
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language:en
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Short-container-title:Atmos. Meas. Tech.
Author:
Borsdorff TobiasORCID, aan de Brugh Joost, Schneider Andreas, Lorente AlbaORCID, Birk Manfred, Wagner Georg, Kivi RigelORCID, Hase Frank, Feist Dietrich G.ORCID, Sussmann Ralf, Rettinger Markus, Wunch DebraORCID, Warneke Thorsten, Landgraf Jochen
Abstract
Abstract. On 13 October 2017, the Tropospheric Monitoring Instrument
(TROPOMI) was launched on the Copernicus Sentinel-5
Precursor satellite in a sun-synchronous orbit. One of the
mission's operational data products is the total column
concentration of carbon monoxide (CO), which was released
to the public in July 2018. The current TROPOMI CO
processing uses the HITRAN 2008 spectroscopic data with
updated water vapor spectroscopy and produces a CO data
product compliant with the mission requirement of 10 %
precision and 15 % accuracy for single soundings.
Comparison with ground-based CO observations of the Total
Carbon Column Observing Network (TCCON) show systematic
differences of about 6.2 ppb and single-orbit
observations are superimposed by a significant striping
pattern along the flight path exceeding 5 ppb. In this
study, we discuss possible improvements of the CO data
product. We found that the molecular spectroscopic data
used in the retrieval plays a key role for the data
quality where the use of the Scientific Exploitation of
Operational Missions – Improved Atmospheric Spectroscopy
Databases (SEOM-IAS) and the HITRAN 2012 and 2016 releases
reduce the bias between TROPOMI and TCCON due to improved
CH4 spectroscopy. SEOM-IAS achieves the best
spectral fit quality (root-mean-square, rms,
differences between the simulated and measured spectrum)
of 1.5×10-10 mol s−1 m−2 nm−1 sr−1 and reduces the bias between TROPOMI and TCCON to
3.4 ppb, while HITRAN 2012 and HITRAN 2016 decrease the
bias even further below 1 ppb. HITRAN 2012 shows the
worst fit quality (rms = 2.5×10-10 mol s−1 m−2 nm−1 sr−1) of the tested cross sections
and furthermore introduces an artificial bias of about
-1.5×1017 molec cm−2 between TROPOMI CO and
the CAMS-IFS model in the Tropics caused by the H2O
spectroscopic data. Moreover, analyzing 1 year of
TROPOMI CO observations, we identified increased striping
patterns by about 16 % percent from November 2017 to
November 2018. For that, we defined a measure γ,
quantifying the relative pixel-to-pixel variation in CO in the
cross-track and along-track directions.
To mitigate this effect, we discuss two
destriping methods applied to the CO data a posteriori.
A destriping mask calculated per orbit by median filtering
of the data in the cross-track direction significantly
reduced the stripe pattern from γ=2.1 to γ=1.6.
However,
the destriping can be further improved, achieving γ=1.2 by
deploying a Fourier analysis and filtering of the
data, which not only corrects for stripe patterns in the
cross-track direction but also accounts for the
variability of stripes along the flight path.
Publisher
Copernicus GmbH
Subject
Atmospheric Science
Reference36 articles.
1. Blumenstock, T., Hase, F., Schneider, M., García, O. E., and
Sepúlveda, E.: TCCON data from Izana (ES), Release GGG2014.R1,
https://doi.org/10.14291/tccon.ggg2014.izana01.r1, 2017. a 2. Boersma, K. F., Eskes, H. J., Dirksen, R. J., van der A, R. J., Veefkind, J. P., Stammes, P., Huijnen, V., Kleipool, Q. L., Sneep, M., Claas, J., Leitão, J., Richter, A., Zhou, Y., and Brunner, D.: An improved tropospheric NO2 column retrieval algorithm for the Ozone Monitoring Instrument, Atmos. Meas. Tech., 4, 1905–1928, https://doi.org/10.5194/amt-4-1905-2011, 2011. a 3. Borsdorff, T., Tol, P., Williams, J. E., de Laat, J., aan de Brugh, J., Nédélec, P., Aben, I., and Landgraf, J.: Carbon monoxide total columns from SCIAMACHY 2.3  µm atmospheric reflectance measurements: towards a full-mission data product (2003–2012), Atmos. Meas. Tech., 9, 227–248, https://doi.org/10.5194/amt-9-227-2016, 2016. a, b 4. Borsdorff, T., aan de Brugh, J., Hu, H., Hasekamp, O., Sussmann, R., Rettinger, M., Hase, F., Gross, J., Schneider, M., Garcia, O., Stremme, W., Grutter, M., Feist, D. G., Arnold, S. G., De Mazière, M., Kumar Sha, M., Pollard, D. F., Kiel, M., Roehl, C., Wennberg, P. O., Toon, G. C., and Landgraf, J.: Mapping carbon monoxide pollution from space down to city scales with daily global coverage, Atmos. Meas. Tech., 11, 5507–5518, https://doi.org/10.5194/amt-11-5507-2018, 2018a. a, b, c, d, e, f, g, h 5. Borsdorff, T., de Brugh, J. A., Hu, H., Aben, I., Hasekamp, O., and Landgraf,
J.: Measuring Carbon Monoxide With TROPOMI: First Results and a Comparison
With ECMWF-IFS Analysis Data, Geophys. Res. Lett., 45, 2826–2832,
https://doi.org/10.1002/2018GL077045,
2018b. a, b, c, d
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