In order to monitor and estimate global atmospheric sulfur dioxide (SO2) emissions, accurate and sensitive detection of this trace gas from space is highly important. Traditionally the detection of SO2 is achieved using the DOAS technique ,. Although this method has been used in the retrieval of SO2 from satellite for many years, it is accompanied by substantial biases (requiring a post-retrieval offset-correction) and high levels of noise.
Inspired by detection methods applied in the thermal infrared spectral domain, the UV-VIS/DOAS group of BIRA-IASB created a new algorithm for the retrieval of SO2 columns from satellite observations in the ultraviolet spectral range. After careful optimization and testing with an international team of colleagues, the algorithm has now been published in a highlight paper in the journal Atmospheric Chemistry and Physics (ACP, ). The retrieval scheme is based on a measurement error covariance matrix to fully represent the radiance variability over SO2-free areas, so that the SO2 slant column density is the only retrieved parameter of the algorithm. The validity of this approach, named COBRA, was demonstrated on measurements from the TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor (S-5P) satellite. The method significantly reduces both the noise and biases present in the current TROPOMI operational DOAS SO2 retrievals.
The map above shows the global S-5P/TROPOMI SO2 signal, averaged over two and a half years (May 2018 - December 2020). By moving the slider, it becomes immediately clear that the COBRA algorithm provides for a much 'cleaner' map. Much of the biases and areas of false SO2 detection, visible in the DOAS signal, are gone in COBRA. Zooming in over polluted regions reveals weak SO2 sources in the COBRA map that are drowned in noise in the DOAS results.
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