Application of OMI, SCIAMACHY, and GOME-2 satellite SO2 retrievals for detection of large emission sources
Article first published online: 4 OCT 2013
©2013. American Geophysical Union. All Rights Reserved.
Journal of Geophysical Research: Atmospheres
Volume 118, Issue 19, pages 11,399–11,418, 16 October 2013
How to Cite
2013), Application of OMI, SCIAMACHY, and GOME-2 satellite SO2 retrievals for detection of large emission sources, J. Geophys. Res. Atmos., 118, 11,399–11,418, doi:10.1002/jgrd.50826., et al. (
- Issue published online: 30 OCT 2013
- Article first published online: 4 OCT 2013
- Accepted manuscript online: 14 SEP 2013 08:53AM EST
- Manuscript Accepted: 7 SEP 2013
- Manuscript Revised: 5 SEP 2013
- Manuscript Received: 5 APR 2013
- remote sensing;
- sulfur dioxide;
 Retrievals of sulfur dioxide (SO2) from space-based spectrometers are in a relatively early stage of development. Factors such as interference between ozone and SO2 in the retrieval algorithms often lead to errors in the retrieved values. Measurements from the Ozone Monitoring Instrument (OMI), Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY), and Global Ozone Monitoring Experiment-2 (GOME-2) satellite sensors, averaged over a period of several years, were used to identify locations with elevated SO2 values and estimate their emission levels. About 30 such locations, detectable by all three sensors and linked to volcanic and anthropogenic sources, were found after applying low and high spatial frequency filtration designed to reduce noise and bias and to enhance weak signals to SO2 data from each instrument. Quantitatively, the mean amount of SO2 in the vicinity of the sources, estimated from the three instruments, is in general agreement. However, its better spatial resolution makes it possible for OMI to detect smaller sources and with additional detail as compared to the other two instruments. Over some regions of China, SCIAMACHY and GOME-2 data show mean SO2 values that are almost 1.5 times higher than those from OMI, but the suggested spatial filtration technique largely reconciles these differences.