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 (SO₂) 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 SO₂ 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 SO₂ 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 SO₂. 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 SO₂ algorithm.

The TROPOspheric Monitoring Instrument (TROPOMI), launched in October 2017 on board the Sentinel-5 Precursor (S5P) satellite, monitors the composition of the Earth's atmosphere at an unprecedented horizontal resolution as fine as 3.5 × 5.5 km². This paper assesses the performances of the TROPOMI formaldehyde (HCHO) operational product compared to its predecessor, the OMI (Ozone Monitoring Instrument) HCHO QA4ECV product, at different spatial and temporal scales. The parallel development of the two algorithms favoured the consistency of the products, which facilitates the production of long-term combined time series. The main difference between the two satellite products is related to the use of different cloud algorithms, leading to a positive bias of OMI compared to TROPOMI of up to 30 % in tropical regions. We show that after switching off the explicit correction for cloud effects, the two datasets come into an excellent agreement. For medium to large HCHO vertical columns (larger than 5 × 10¹⁵ molec. cm⁻²) the median bias between OMI and TROPOMI HCHO columns is not larger than 10 % (< 0.4 × 10¹⁵ molec. cm⁻²). For lower columns, OMI observations present a remaining positive bias of about 20 % (< 0.8 × 10¹⁵ molec. cm⁻²) compared to TROPOMI in midlatitude regions. Here, we also use a global network of 18 MAX-DOAS (multi-axis differential optical absorption spectroscopy) instruments to validate both satellite sensors for a large range of HCHO columns. This work complements the study by Vigouroux et al. (2020), where a global FTIR (Fourier transform infrared) network is used to validate the TROPOMI HCHO operational product. Consistent with the FTIR validation study, we find that for elevated HCHO columns, TROPOMI data are systematically low (−25 % for HCHO columns larger than 8 × 10¹⁵ molec. cm⁻²), while no significant bias is found for medium-range column values. We further show that OMI and TROPOMI data present equivalent biases for large HCHO levels. However, TROPOMI significantly improves the precision of the HCHO observations at short temporal scales and for low HCHO columns. We show that compared to OMI, the precision of the TROPOMI HCHO columns is improved by 25 % for individual pixels and by up to a factor of 3 when considering daily averages in 20 km radius circles. The validation precision obtained with daily TROPOMI observations is comparable to the one obtained with monthly OMI observations. To illustrate the improved performances of TROPOMI in capturing weak HCHO signals, we present clear detection of HCHO column enhancements related to shipping emissions in the Indian Ocean. This is achieved by averaging data over a much shorter period (3 months) than required with previous sensors (5 years) and opens new perspectives to study shipping emissions of VOCs (volatile organic compounds) and related atmospheric chemical interactions.

Over the last four decades, space-based nadir observations of sulfur dioxide (SO₂) proved to be a key data source for assessing the environmental impacts of volcanic emissions, for monitoring volcanic activity and early signs of eruptions, and ultimately mitigating related hazards on local populations and aviation. Despite its importance, a detailed picture of global SO₂ daily degassing is difficult to produce, notably for lower-tropospheric plumes, due largely to the limited spatial resolution and coverage or lack of sensitivity and selectivity to SO₂ of current (and previous) nadir sensors. We report here the first volcanic SO₂ measurements from the hyperspectral TROPOspheric Monitoring Instrument (TROPOMI) launched in October 2017 onboard the ESA’s Sentinel-5 Precursor platform. Using the operational processing algorithm, we explore the benefit of improved spatial resolution to the monitoring of global volcanic degassing. We find that TROPOMI surpasses any space nadir sensor in its ability to detect weak degassing signals and captures day-to-day changes in SO₂ emissions. The detection limit of TROPOMI to SO₂ emissions is a factor of 4 better than the heritage Aura/Ozone Monitoring Instrument (OMI). Here we show that TROPOMI SO₂ daily observations carry a wealth of information on volcanic activity. Provided with adequate wind speed data, temporally resolved SO₂ fluxes can be obtained at hourly time steps or shorter. We anticipate that TROPOMI SO₂ data will help to monitor global volcanic daily degassing and better understand volcanic processes and impacts.

The accurate determination of the location, height, and loading of sulfur dioxide (SO₂) plumes emitted by volcanic eruptions is essential for aviation safety. The SO₂ layer height is also one of the most critical parameters with respect to determining the impact on the climate. Retrievals of SO₂ plume height have been carried out using satellite UV backscatter measurements, but, until now, such algorithms are very time-consuming. We have developed an extremely fast yet accurate SO₂ layer height retrieval using the Full-Physics Inverse Learning Machine (FP_ILM) algorithm. This is the first time the algorithm has been applied to measurements from the TROPOMI instrument onboard the Sentinel-5 Precursor platform. In this paper, we demonstrate the ability of the FP_ILM algorithm to retrieve SO₂ plume layer heights in near-real-time applications with an accuracy of better than 2 km for SO₂ total columns larger than 20 DU. We present SO₂ layer height results for the volcanic eruptions of Sinabung in February 2018, Sierra Negra in June 2018, and Raikoke in June 2019, observed by TROPOMI.

Sensitive and accurate detection of sulfur dioxide (SO₂) from space is important for monitoring and estimating global sulfur emissions. Inspired by detection methods applied in the thermal infrared, we present here a new scheme to retrieve SO₂ columns from satellite observations of ultraviolet back-scattered radiances. The retrieval is based on a measurement error covariance matrix to fully represent the SO₂-free radiance variability, so that the SO₂ slant column density is the only retrieved parameter of the algorithm. We demonstrate this approach, named COBRA, on measurements from the TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor (S-5P) satellite. We show that the method reduces significantly both the noise and biases present in the current TROPOMI operational DOAS SO₂ retrievals. The performance of this technique is also benchmarked against that of the principal component algorithm (PCA) approach. We find that the quality of the data is similar and even slightly better with the proposed COBRA approach. The ability of the algorithm to retrieve SO₂ accurately is further supported by comparison with ground-based observations. We illustrate the great sensitivity of the method with a high-resolution global SO₂ map, considering 2.5 years of TROPOMI data. In addition to the known sources, we detect many new SO₂ emission hotspots worldwide. For the largest sources, we use the COBRA data to estimate SO₂ emission rates. Results are comparable to other recently published TROPOMI-based SO₂ emissions estimates, but the associated uncertainties are significantly lower than with the operational data. Next, for a limited number of weak sources, we demonstrate the potential of our data for quantifying SO₂ emissions with a detection limit of about 8 kt yr⁻¹, a factor of 4 better than the emissions derived from the Ozone Monitoring Instrument (OMI). We anticipate that the systematic use of our TROPOMI COBRA SO₂ column data set at a global scale will allow missing sources to be identified and quantified and help improve SO₂ emission inventories.

The paper introduces the first TROPOMI-based sulfur dioxide (SO₂) emissions estimates for point sources. A total of about 500 continuously emitting point sources releasing about 10 kt yr⁻¹ to more than 2000 kt yr⁻¹ of SO₂, previously identified from Ozone Monitoring Instrument (OMI) observations, were analyzed using TROPOMI (TROPOspheric Monitoring Instrument) measurements for 1 full year from April 2018 to March 2019. The annual emissions from these sources were estimated and compared to similar estimates from OMI and Ozone Mapping Profiling Suite (OMPS) measurements. Note that emissions from many of these 500 sources have declined significantly since 2005, making their quantification more challenging. We were able to identify 274 sources where annual emissions are significant and can be reliably estimated from TROPOMI. The standard deviations of TROPOMI vertical column density data, about 1 Dobson unit (DU, where 1 DU $=\mathrm{2.69}\times {\mathrm{10}}^{\mathrm{16}}$ molecules cm⁻²) over the tropics and 1.5 DU over high latitudes, are larger than those of OMI (0.6–1 DU) and OMPS (0.3–0.4 DU). Due to its very high spatial resolution, TROPOMI produces 12–20 times more observations over a certain area than OMI and 96 times more than OMPS. Despite higher uncertainties of individual TROPOMI observations, TROPOMI data averaged over a large area have roughly 2–3 times lower uncertainties compared to OMI and OMPS data. Similarly, TROPOMI annual emissions can be estimated with uncertainties that are 1.5–2 times lower than the uncertainties of annual emissions estimates from OMI. While there are area biases in TROPOMI data over some regions that have to be removed from emission calculations, the absolute magnitude of these are modest, typically within ±0.25 DU, which can be comparable with SO₂ values over large sources.