S5P TROPOMI NO<sub>2</sub> slant column retrieval: method, stability, uncertainties and comparisons with OMI

Abstract

Abstract. The Tropospheric Monitoring Instrument (TROPOMI), aboard the Sentinel-5 Precursor (S5P) satellite, launched on 13 October 2017, provides measurements of atmospheric trace gases and of cloud and aerosol properties at an unprecedented spatial resolution of approximately 7×3.5 km2 (approx. 5.5×3.5 km2 as of 6 August 2019), achieving near-global coverage in 1 d. The retrieval of nitrogen dioxide (NO2) concentrations is a three-step procedure: slant column density (SCD) retrieval, separation of the SCD in its stratospheric and tropospheric components, and conversion of these into vertical column densities. This study focusses on the TROPOMI NO2 SCD retrieval: the retrieval method used, the stability of the SCDs and the SCD uncertainties, and a comparison with the Ozone Monitoring Instrument (OMI) NO2 SCDs. The statistical uncertainty, based on the spatial variability of the SCDs over a remote Pacific Ocean sector, is 8.63 µmol m−2 for all pixels (9.45 µmol m−2 for clear-sky pixels), which is very stable over time and some 30 % less than the long-term average over OMI–QA4ECV data (since the pixel size reduction TROPOMI uncertainties are ∼8 % larger). The SCD uncertainty reported by the differential optical absorption spectroscopy (DOAS) fit is about 10 % larger than the statistical uncertainty, while for OMI–QA4ECV the DOAS uncertainty is some 20 % larger than its statistical uncertainty. Comparison of the SCDs themselves over the Pacific Ocean, averaged over 1 month, shows that TROPOMI is about 5 % higher than OMI–QA4ECV, which seems to be due mainly to the use of the so-called intensity offset correction in OMI–QA4ECV but not in TROPOMI: turning that correction off means about 5 % higher SCDs. The row-to-row variation in the SCDs of TROPOMI, the “stripe amplitude”, is 2.15 µmol m−2, while for OMI–QA4ECV it is a factor of ∼2 (∼5) larger in 2005 (2018); still, a so-called stripe correction of this non-physical across-track variation is useful for TROPOMI data. In short, TROPOMI shows a superior performance compared with OMI–QA4ECV and operates as anticipated from instrument specifications. The TROPOMI data used in this study cover 30 April 2018 up to 31 January 2020.

NOx Emissions Reduction and Rebound in China Due to the COVID‐19 Crisis

“…TROPOMI is a spectrometer combining a high spectral resolution with high spatial resolution (3.5 × 5.5 km 2 at nadir for the NO 2 observations), low noise, and a daily global coverage. Despite the much smaller footprints, the spectral fits of the individual TROPOMI groud pixels have 30% smaller noise than those from the earlier Ozone Monitoring Instrument (OMI), and the average values agree well within 5% (van Geffen et al, 2020).…”

Abrupt decline in tropospheric nitrogen dioxide over China after the outbreak of COVID-19

“…We use the version 4.0 NASA OMI standard NO 2 products (17). We use the version 1.0.0 TROPOMI Level 2 offline NO 2 data products for 2019 and the version 1.1.0 data for 2020 (18). OMI and TROPOMI measurements are aggregated to resolutions of 0.25° × 0.25° and 0.05° × 0.05°, respectively.…”

NOx Emission Reduction and Recovery during COVID-19 in East China

“…The Royal Dutch Meteorological Institute (KNMI) TM5-MP-DOMINO NO 2 retrieval algorithm employs the differential optical absorption spectroscopy (DOAS) to derive NO 2 slant column densities (SCDs) using visible wavelengths between 400 and 496 nm [22,24]. The tropospheric portion of total SCDs are then separated and converted to tropospheric vertical column densities (VCDs) using air mass factors (AMFs), which are calculated by the Doubling-Adding KNMI (DAK) radiative transfer model (v3.2) with a priori NO 2 profiles from a chemistry transport model (CTM) TM5-MP [22].…”

“…The overall uncertainty of tropospheric NO 2 VCDs was estimated to be approximately 0.5 × 10 15 molecules/cm 2 in systematic bias plus a 20-50% relative random error [22]. For detailed discussions on the uncertainties, we would refer readers to previous studies [22,[24][25][26]. The TM5-MP-DOMINO NO 2 retrieval algorithm implicitly accounts for the impacts of aerosols on retrieval, whereas the other TROPOMI NO 2 retrieval product, POMINO-TROPOMI [27], explicitly models aerosols in the retrieval algorithm.…”