TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications

Veefkind, J. P.; Aben, I.; McMullan, Kevin; Förster, Harry; Vries, J. de; Otter, Gerard; Claas, J.; Eskes, Henk; Haan, J. F. de; Kleipool, Q.; Weele, M. van; Hasekamp, Otto; Hoogeveen, Ruud W. M.; Landgraf, Jochen; Snel, Ralph; Tol, Paul; Ingmann, Paul; Voors, Robert; Kruizinga, Bob; Vink, Ramon; Visser, H.; Levelt, P. F.

doi:10.1016/j.rse.2011.09.027

Cited by 1,629 publications

(1,363 citation statements)

References 29 publications

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“…The algorithm also includes an error calculation and retrieval characterization module (Sect. 3) that computes the averaging kernels (Eskes and Boersma, 2003), which characterize the vertical sensitivity of the measurement and which are required for comparison with other types of data (Veefkind et al, 2012).…”

Section: Algorithm Descriptionmentioning

confidence: 99%

“…It has a major contribution to the SCDE and it can be estimated from typical S/N values of S-5P in UV band 3 (800-1000, according to Veefkind et al, 2012). This translates to typical SCD random errors of about 0.3-0.5, 5 and 60 DU for window 1, 2 and 3, respectively.…”

Section: N Theys Et Al: S-5p So 2 Algorithm Theoretical Basis Errormentioning

confidence: 99%

“…The 7-year-lifetime Sentinel-5p sensor TROPOMI (Veefkind et al, 2012) will fly on a polar low-Earth orbit with a wide swath of 2600 km. The TROPOMI instrument is a push-broom imaging spectrometer similar in concept to OMI.…”

Section: Introductionmentioning

confidence: 99%

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Sulfur dioxide retrievals from TROPOMI onboard Sentinel-5 Precursor: algorithm theoretical basis

et al. 2017

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Abstract. The TROPOspheric Monitoring Instrument (TROPOMI) onboard the Copernicus Sentinel-5 Precursor (S-5P) platform will measure ultraviolet earthshine radiances at high spectral and improved spatial resolution (pixel size of 7 km × 3.5 km at nadir) compared to its predecessors OMI and GOME-2. This paper presents the sulfur dioxide (SO 2 ) vertical column retrieval algorithm implemented in the S-5P operational processor UPAS (Universal Processor for UV/VIS Atmospheric Spectrometers) and comprehensively describes its various retrieval steps. The spectral fitting is performed using the differential optical absorption spectroscopy (DOAS) method including multiple fitting windows to cope with the large range of atmospheric SO 2 columns encountered. It is followed by a slant column background correction scheme to reduce possible biases or across-track-dependent artifacts in the data. The SO 2 vertical columns are obtained by applying air mass factors (AMFs) calculated for a set of representative a priori profiles and accounting for various parameters influencing the retrieval sensitivity to SO 2 . Finally, the algorithm includes an error analysis module which is fully described here. We also discuss verification results (as part of the algorithm development) and future validation needs of the TROPOMI SO 2 algorithm.

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Section: Algorithm Descriptionmentioning

confidence: 99%

Section: N Theys Et Al: S-5p So 2 Algorithm Theoretical Basis Errormentioning

confidence: 99%

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Sulfur dioxide retrievals from TROPOMI onboard Sentinel-5 Precursor: algorithm theoretical basis

et al. 2017

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“…The use of non-scanning optics like those of OMI will be continued in new instruments, in particular TROPOspheric Monitoring Instrument (TropOMI) on Sentinel-5 (Veefkind et al, 2012), to be launched in 2016. For TropOMI, a cloud masking feature is anticipated from Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi-NPP (Schueler et al, 2002).…”

Section: Resultsmentioning

confidence: 99%

How big is an OMI pixel?

et al. 2016

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Abstract. The Ozone Monitoring Instrument (OMI) is a push-broom imaging spectrometer, observing solar radiation backscattered by the Earth's atmosphere and surface. The incoming radiation is detected using a static imaging CCD (charge-coupled device) detector array with no moving parts, as opposed to most of the previous satellite spectrometers, which used a moving mirror to scan the Earth in the acrosstrack direction. The field of view (FoV) of detector pixels is the solid angle from which radiation is observed, averaged over the integration time of a measurement. The OMI FoV is not quadrangular, which is common for scanning instruments, but rather super-Gaussian shaped and overlapping with the FoV of neighbouring pixels. This has consequences for pixel-area-dependent applications, like cloud fraction products, and visualisation.The shapes and sizes of OMI FoVs were determined preflight by theoretical and experimental tests but never verified after launch. In this paper the OMI FoV is characterised using collocated MODerate resolution Imaging Spectroradiometer (MODIS) reflectance measurements. MODIS measurements have a much higher spatial resolution than OMI measurements and spectrally overlap at 469 nm. The OMI FoV was verified by finding the highest correlation between MODIS and OMI reflectances in cloud-free scenes, assuming a 2-D super-Gaussian function with varying size and shape to represent the OMI FoV. Our results show that the OMPIX-COR product 75FoV corner coordinates are accurate as the full width at half maximum (FWHM) of a super-Gaussian FoV model when this function is assumed. The softness of the function edges, modelled by the super-Gaussian exponents, is different in both directions and is view angle dependent.The optimal overlap function between OMI and MODIS reflectances is scene dependent and highly dependent on time differences between overpasses, especially with clouds in the scene. For partially clouded scenes, the optimal overlap function was represented by super-Gaussian exponents around 1 or smaller, which indicates that this function is unsuitable to represent the overlap sensitivity function in these cases. This was especially true for scenes before 2008, when the time differences between Aqua and Aura overpasses was about 15 min, instead of 8 min after 2008. During the time between overpasses, clouds change the scene reflectance, reducing the correlation and influencing the shape of the optimal overlap function.

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“…In the absence of clouds, these bands contain information on the aerosol height (Wang et al, 2012). The algorithm developed at KNMI within the TROPOMI/Sentinel-5 Precursor programme (Veefkind et al, 2012) is based on the optimal estimation method and aims to derive the aerosol-layer height (Sanders and de Haan, 2013). This method has also been applied to Greenhouse gases Observing SATellite (GOSAT) and GOME-2 data within the ongoing ESA AeroPro study, in support of the Sentinel-4 development.…”

Section: Gome-2 Knmi Algorithmmentioning

confidence: 99%

Comparison of dust-layer heights from active and passive satellite sensors

et al. 2018

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Abstract. Aerosol-layer height is essential for understanding the impact of aerosols on the climate system. As part of the European Space Agency Aerosol_cci project, aerosollayer height as derived from passive thermal and solar satellite sensors measurements have been compared with aerosollayer heights estimated from CALIOP measurements. The Aerosol_cci project targeted dust-type aerosol for this study. This ensures relatively unambiguous aerosol identification by the CALIOP processing chain. Dust-layer height was estimated from thermal IASI measurements using four different algorithms (from BIRA-IASB, DLR, LMD, LISA) and from solar GOME-2 (KNMI) and SCIAMACHY (IUP) measurements. Due to differences in overpass time of the various satellites, a trajectory model was used to move the CALIOPderived dust heights in space and time to the IASI, GOME-2 and SCIAMACHY dust height pixels. It is not possible to construct a unique dust-layer height from the CALIOP data. Thus two CALIOP-derived layer heights were used: the cumulative extinction height defined as the height where the CALIOP extinction column is half of the total extinction column, and the geometric mean height, which is defined as the geometrical mean of the top and bottom heights of the dust layer. In statistical average over all IASI data there is a general tendency to a positive bias of 0.5-0.8 km against CALIOP extinction-weighted height for three of the four algorithms assessed, while the fourth algorithm has almost no bias. When comparing geometric mean height there is a shift of −0.5 km for all algorithms (getting close to zero for the three algorithms and turning negative for the fourth). The standard deviation of all algorithms is quite similar and ranges between 1.0 and 1.3 km. When looking at different conditions (day, night, land, ocean), there is more detail in variabilities (e.g. all algorithms overestimate more at night than during the day). For the solar sensors it is found that on average SCIAMACHY data are lower by −1.097 km (−0.961 km) compared to the CALIOP geometric mean (cumulative extinction) height, and GOME-2 data are lower by −1.393 km (−0.818 km).

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TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications

Cited by 1,629 publications

References 29 publications

Sulfur dioxide retrievals from TROPOMI onboard Sentinel-5 Precursor: algorithm theoretical basis

Sulfur dioxide retrievals from TROPOMI onboard Sentinel-5 Precursor: algorithm theoretical basis

How big is an OMI pixel?

Comparison of dust-layer heights from active and passive satellite sensors

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