Measuring Carbon Monoxide With TROPOMI: First Results and a Comparison With ECMWF‐IFS Analysis Data

Borsdorff, Tobias; Brugh, Joost aan de; Hu, Haili; Aben, I.; Hasekamp, Otto; Landgraf, Jochen

doi:10.1002/2018gl077045

Cited by 121 publications

(141 citation statements)

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“…the detection of pollution hotspots and emission estimates. Moreover, the comparison of the TROPOMI and the CAMS-IFS CO datasets indicated a latitudinal difference which represents a problem for the assimilation of the product (Borsdorff et al, 2018b; Inness et al, 2019).In this study, we discuss in detail the open issues of the TROPOMI CO data product and possible mitigation strategies.Section 2 introduces the TROPOMI CO data, the CO validation measurements of the TCCON network and the CO CAMS-IFS 25 data. In Sect.…”

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confidence: 86%

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confidence: 99%

“…The theoretical details for the algorithm are described by Vidot et al (2012); Landgraf et al (2016a, b). For this study, we analyze one year of TROPOMI SWIR measurements from November 2017 to November 2018 using the operational SICOR as used by Borsdorff et al (2018bBorsdorff et al ( , a, 2019.…”

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confidence: 99%

“…The inter-comparison of the TROPOMI CO retrievals with the CO data of the CAMS-IFS model follows the approach as described in Borsdorff et al (2018b), where we interpolated the vertical profiles of the model spatially and temporally to the 30 time and geolocation of the ground pixels of TROPOMI. Then we calculated the total column concentration of CO from the model profiles by multiplying them with corresponding total column averaging kernels of TROPOMI that are provided for each measurement.…”

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Improving the TROPOMI CO data product: update of the spectroscopic database and destriping of single orbits

Borsdorff¹,

Brugh²,

Schneider³

et al. 2019

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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. Using HITRAN 2008 spectroscopic data with an updated water vapor spectroscopy, the CO data product is 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 5 Network (TCCON) show systematic differences of about 6.4 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 CH 4 spectroscopy. 10SEOM-IAS achieves the best spectral fitting quality and reduces the bias between TROPOMI and TCCON to 3.3 ppb while HITRAN 2012 and HITRAN 2016 decrease the bias even further below 1.1 ppb. Here, HITRAN 2012 worsens the fitting quality and furthermore introduces an artificial bias to the TROPOMI CO data product in the tropics caused by the H 2 O spectroscopic data. Moreover, analyzing one year of TROPOMI CO observations, we identified increased striping patterns by about 16 % percent from November 2017 to November 2018. To mitigate this effect, we discuss two destriping methods applied to 15 the CO data a posteriori. A destriping mask calculated per orbit by median filtering of the data in the cross-track direction significantly improves the data quality. However, still better quality is achieved by a Fourier analysis and filtering of the data, which corrects not only for stripe patterns in cross-track direction but also accounts for the variability of stripes along the flight path.The Tropospheric Monitoring Instrument (TROPOMI) is the single payload of the Copernicus Sentinel-5P satellite that was launched by the European Space Agency (ESA) on 13 October 2017. The instrument provides spectral measurements of the solar radiance reflected by Earth and its atmosphere in the ultraviolet-visible (UV-VIS, 270-495 nm), near-infrared (NIR, 675-775 nm), and the shortwave-infrared (SWIR, 2305-2385 nm) (Veefkind et al., 2012). The novelty of the mission is the daily 5 global coverage, the high spatial resolution of 3.5x7 km 2 or 7x7 km 2 depending on spectral range, and the higher signal-to-noise ratio (SNR).One of the primary goals of the mission is to measure the total column concentration of carbon monoxide (CO) in Earth's atmosphere. CO is a trace gas emitted by incomplete combustion (e.g. biomass burning, traffic, and industrial activity) and ...

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confidence: 86%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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Improving the TROPOMI CO data product: update of the spectroscopic database and destriping of single orbits

Borsdorff¹,

Brugh²,

Schneider³

et al. 2019

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“…We used data from 14 orbits of TROPOMI retrieved between 11 and 19 November 2017 that covered the Northern part of India. As in the study of Borsdorff et al (2018a) on the first TROPOMI CO results, we used XCO values that were retrieved using the operational algorithm SICOR (Landgraf et al, 2016). TROPOMI data were filtered for clear sky observations, and 15 cloudy sky observations with a cloud top height < 5000 m and an aerosol optical thickness >0.…”

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What caused the extreme CO concentrations during the 2017 high pollution episode in India?

Dekker¹,

Houweling²,

Pandey³

et al. 2018

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The TROPOspheric Monitoring Instrument (TROPOMI), launched 13 October 2017, measures carbon monoxide (CO) concentrations in the Earth's atmosphere since early November 2017. In the first measurements, TROPOMI was able to measure CO concentrations of the high pollution event in India of November 2017. In this paper we studied the extent of the pollution in India, comparing the TROPOMI CO with modelled data from the Weather Research and Forecast model (WRF) to identify the most important sources contributing to the high pollution, both at ground-level and in the total column. We 5 investigated the period between 11 and 19 November 2017. We found that residential and commercial combustion was a much more important source of CO pollution than the post-monsoon crop burning during this period, which is in contrast to what media suggested and some studies on aerosol emissions found. Also, the high pollution was not limited to Delhi and its direct neighbourhood but the accumulation of pollution extended over the whole Indo-Gangetic Plain (IGP) due to the unfavourable weather conditions in combination with extensive emissions. From the TROPOMI data and WRF simulations, we observed 10 a build-up of CO during 11-14 November and a decline in CO after the 15 th of November. The meteorological situation, characterized by low wind speeds and shallow atmospheric boundary layers, was most likely the primary explanation for the temporal accumulation and subsequent dispersion of regionally emitted CO in the atmosphere, emphasizing the important role of atmospheric dynamics. Due to its rapidly growing population and economy, India is expected to encounter similar pollution events more often in future post-monsoon and winter seasons unless significant policy measures are taken to reduce residential 15 and commercial emissions.

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The carbon cycle of southeast Australia during 2019/2020: Drought, fires and subsequent recovery

Byrne

Liu

Lee

et al. 2021

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Extreme drought and heat events can result in single-year carbon losses equal to many years of carbon sequestration (Bastos et al., 2014;Ciais et al., 2005). Hot-dry conditions can directly suppress both gross primary productivity (GPP) and ecosystem respiration (TER), with greater suppression of GPP leading to carbon loss (Reichstein et al., 2007;Sippel et al., 2018). These conditions also dry fuels, increase litterfall, and elevate levels of tree mortality, all of which may trigger additional carbon losses by means of wildfire (Abram et al., 2020; D. M. J. S. Bowman et al., 2009). Impacted ecosystems often experience legacy effects that can impact the carbon cycling for years after the extreme events have passed (Batllori et al., 2020;Frank et al., 2015;Lindenmayer et al., 2021). Southeast Australia (Figure 1) has a highly variable climate (Harris & Lucas, 2019;King et al., 2020), and frequently experiences both drought and fire. In fact, this susceptibility to fire has been a key factor in the evolution of the regional flora and fauna, acting as a process of disturbance and also regeneration (Bowman, 2000;

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Measuring Carbon Monoxide With TROPOMI: First Results and a Comparison With ECMWF‐IFS Analysis Data

Cited by 121 publications

References 18 publications

Improving the TROPOMI CO data product: update of the spectroscopic database and destriping of single orbits

Improving the TROPOMI CO data product: update of the spectroscopic database and destriping of single orbits

What caused the extreme CO concentrations during the 2017 high pollution episode in India?

The carbon cycle of southeast Australia during 2019/2020: Drought, fires and subsequent recovery

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