Preprints
https://doi.org/10.5194/acp-2021-294
https://doi.org/10.5194/acp-2021-294

  17 May 2021

17 May 2021

Review status: this preprint is currently under review for the journal ACP.

A Sulfur Dioxide Covariance-Based Retrieval Algorithm (COBRA): application to TROPOMI reveals new emission sources

Nicolas Theys1, Vitali Fioletov2, Can Li3,4, Isabelle De Smedt1, Christophe Lerot1, Chris McLinden2, Nickolay Krotkov3, Debora Griffin2, Lieven Clarisse5, Pascal Hedelt6, Diego Loyola6, Thomas Wagner7, Vinod Kumar7, Antje Innes8, Roberto Ribas8, François Hendrick1, Jonas Vlietinck1, Hugues Brenot1, and Michel Van Roozendael1 Nicolas Theys et al.
  • 1Royal Belgian Institute for Space Aeronomy (BIRA-IASB), Brussels, Belgium
  • 2Air Quality Research Division, Environment and Climate Change Canada, Toronto, Canada
  • 3Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 4Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
  • 5Université libre de Bruxelles (ULB), Spectroscopy, Quantum Chemistry and Atmospheric Remote Sensing (SQUARES), C. P. 160/09, Brussels, Belgium
  • 6Institut für Methodik der Fernerkundung (IMF), Deutsches Zentrum für Luft und Raumfahrt (DLR), Oberpfaffenhofen, Germany
  • 7Max Planck Institute for Chemistry (MPIC), Hahn-Meitner-Weg 1, 55128 Mainz, Germany
  • 8European Centre for Medium-Range Weather Forecast (ECMWF), Shinfield Park, Reading, RG2 9AX, UK

Abstract. 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 also further supported by comparison with ground-based observations. We illustrate the great sensitivity of the method with a high-resolution global SO2 map, considering two and a half 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 identifying and quantifying missing sources, and help improving SO2 emission inventories.

Nicolas Theys et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-294', Anonymous Referee #1, 25 Jul 2021
  • RC2: 'Comment on acp-2021-294', Neil Harris, 30 Jul 2021

Nicolas Theys et al.

Nicolas Theys et al.

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Short summary
We present a new algorithm to retrieve sulfur dioxide from space UV measurements. We apply the technique to TROPOMI high resolution measurements and demonstrate the high sensitivity of the approach to weak SO2 emissions worldwide with an unprecedented limit of detection of 8 kt yr-1. This result has broad implications for atmospheric science studies dealing with improving emission inventories, identifying and quantifying missing sources, in the context of air quality and climate.
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