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[1] To improve global measurements of atmospheric sulfur dioxide (SO 2 ), we have developed a new technique, called the linear fit (LF) algorithm, which uses the radiance measurements from the Ozone Monitoring Instrument (OMI) at a few discrete ultraviolet wavelengths to derive SO 2 , ozone, and effective reflectivity simultaneously. We have also developed a sliding median residual correction method for removing both the along-and cross-track biases from the retrieval results. The achieved internal consistencies among the LF-retrieved geophysical parameters clearly demonstrate the success of this technique. Comparison with the results from the Band Residual Difference technique has also illustrated the drastic improvements of this new technique at high SO 2 loading conditions. We have constructed an error equation and derived the averaging kernel to characterize the LF retrieval and understand its limitations. Detailed error analysis has focused on the impacts of the SO 2 column amounts and their vertical distributions on the retrieval results. The LF algorithm is robust and fast; therefore it is suitable for near realtime application in aviation hazards and volcanic eruption warnings. Very large SO 2 loadings (>100 DU) require an off-line iterative solution of the LF equations to reduce the retrieval errors. Both the LF and sliding median techniques are very general so that they can be applied to measurements from other backscattered ultraviolet instruments, including the series of Total Ozone Mapping Spectrometer (TOMS) missions, thereby offering the capability to update the TOMS long-term record to maintain consistency with its OMI extension.

[1] Satellite sulfur dioxide (SO 2 ) measurements from the Ozone Monitoring Instrument (OMI) satellite sensor, averaged over a period of several years, were compared with emissions inventories for major US sources. Low-and highspatial frequency filtration was applied to OMI data to reduce the noise and bias to enhance and reveal weak SO 2 signals that are otherwise not readily apparent. Averaging a large number of individual observations enables the study of SO 2 spatial distributions near larger SO 2 emissions sources with an effective resolution superior to that of an individual OMI observation and even to obtain rough estimates of the emissions level from those sources. It is demonstrated that individual sources (or multiple sources within 50 km) with annual SO 2 emissions greater than about 70 kT y −1 produce a statistically significant signal in 3-year averaged OMI data. A correlation of 0.93 was found between OMI SO 2 integrated around the source and the annual SO 2 emission rate for the sources greater than 70 kT y −1 . OMI SO 2 data also indicate a 40% decline in SO 2 values over the largest US coal power plants between 2005-2007 and 2008-2010, a value that is consistent with the reported 46% reduction in annual emissions due to the implementation of new SO 2 pollution control measures over this period. Citation:

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.