Retrieval of large volcanic SO₂ columns from the Aura Ozone Monitoring Instrument: Comparison and limitations

Abstract

[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.

Using Himawari-8, estimation of SO2 cloud altitude at Aso volcano eruption, on October 8, 2016

“…Low Earth orbit satellites provide high-spectral resolution data (Cooke et al 2014). For example, Yang et al (2007) developed retrieval of SO 2 from the Ozone Monitoring Instrument (OMI). However, since they are Earth orbit satellites, there are very few chances of observations of ash clouds immediately after an eruption.…”

Observations of volcanic SO<sub>2</sub> from MLS on Aura

“…In the along-track direction the pixels are 13 km across. The algorithm used to derive total column SO 2 from OMI measurements is described by Yang et al (2007). It is suitable for most conditions but underestimates the total column where that column is very large.…”

“…Nadir sounding provides good horizontal resolution but little or no vertical resolution. Both thermal emission in the infrared (IR) (Clarisse et al, 2012) and backscattered sunlight in the ultraviolet (UV) (Yang et al, 2007) can be used. Note that SO 2 layer heights may also be retrieved from hyper-spectral UV observations when the columns are sufficiently large .…”

Sulfur dioxide retrievals from TROPOMI onboard Sentinel-5 Precursor: algorithm theoretical basis

“…Over the last decades, a host of satellite-based UV-visible instruments have been used for the monitoring of anthropogenic and volcanic SO 2 emissions. Total vertical column density (VCD) of SO 2 has been retrieved with the sensors TOMS (Krueger, 1983), GOME (Eisinger and Burrows, 1998;Thomas et al, 2005;Khokar et al, 2005), SCIAMACHY (Afe et al, 2004), OMI (Krotkov et al, 2006;Yang et al, 2007Yang et al, , 2010Li et al, 2013;Theys et al, 2015), GOME-2 Bobrowski et al, 2010;Nowlan et al, 2011;Rix et al, 2012;Hörmann et al, 2013) and OMPS . In particular, the Ozone Monitoring Instrument (OMI) has largely demonstrated the value of satellite UV-visible remote sensing (1) in monitoring volcanic plumes in near-real time (Brenot et al, 2014) and changes in volcanic degassing at the global scale , and references therein) and (2) in detecting and quantifying large anthropogenic SO 2 emissions, weak or N. Theys et al: S-5P SO 2 algorithm theoretical basis unreported emission sources worldwide (Theys et al, 2015;Fioletov et al, 2016;McLinden et al, 2016) as well as investigating their long-term changes van der A et al, 2016;He et al, 2016).…”