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

Abstract

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.

The operational cloud retrieval algorithms from TROPOMI on board Sentinel-5 Precursor

“…The OCRA and ROCINN cloud parameters will be used for enhancing the accuracy of the operational TROPOMI/S5P trace gas products total ozone (Loyola et al, 2017), formaldehyde, and sulfur dioxide (Theys et al, 2016). OCRA and ROCINN will be also used for the generation of operational cloud products from the geostationary Copernicus atmospheric composition mission Sentinel-4 (S4).…”

“…Furthermore, errors on retrieved NO 2 columns can be significantly reduced using cloud parameters from combined UV-VIS and NIR spectral regions (van Deelen et al, 2008) as obtained from OCRA and ROCINN_CAL. More recent studies have shown that, for the smaller GOME-2 pixels, CAL retrieval produces more reliable cloud information than that from CRB (Sihler et al, 2015), not only with regard to the accuracy of the cloud parameters themselves but also with respect to the effect of cloud parameter uncertainties on total ozone accuracy (Loyola et al, 2017).…”

“…In a similar manner, OCRA and ROCINN results will be used in the following operational TROPOMI/S5P trace gas retrieval products: total ozone (Loyola et al, 2017), tropospheric ozone (Heue et al, 2016), formaldehyde, and sulfur dioxide (Theys et al, 2017).…”

Updated SO<sub>2</sub> emission estimates over China using OMI/Aura observations

“…The anthropogenic SO 2 profile required in the AMF calculation has been extracted from the Intermediate Model of the Global and Annual Evolution of Species, IMAGESv2, global tropospheric chemistry transport model (Stavrakou et al, 2013, and references therein) on a daily basis and for the overpass time of OMI. All details on the BIRA OMI SO 2 algorithm can be found in Theys et al (2015), updated recently in Theys et al (2017) in preparation for TROPOspheric Monitoring Instrument (TROPOMI) instrument. The dataset has already been employed in different studies: in van der A et al (2017) in order to estimate the effectiveness of current air quality policies for SO 2 and NO x emissions in China; in Koukouli et al (2016) in order to quantify the anthropogenic SO 2 load over China using different satellite instruments and algorithms; and in Schmidt et al (2015) in order to study the 2014-2015 Bárðarbunga-Veiðivötn fissure eruption in Iceland, among others.…”

“…The quality of these three pieces of information ensures the accuracy of the updated, a posteriori, SO 2 emissions estimates. Since the mathematical formulism also requires quantifiable error estimates on these three input parameters, using the new OMI/Aura Royal Belgian Institute for Space Aeronomy (BIRA) SO 2 dataset (Theys et al, , 2017 ensures that the satellite observations used here are fully characterized in this manner. In Sects.…”