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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 10, issue 2
Atmos. Meas. Tech., 10, 445–458, 2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Ten years of Ozone Monitoring Instrument (OMI) observations...

Atmos. Meas. Tech., 10, 445–458, 2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 06 Feb 2017

Research article | 06 Feb 2017

New-generation NASA Aura Ozone Monitoring Instrument (OMI) volcanic SO2 dataset: algorithm description, initial results, and continuation with the Suomi-NPP Ozone Mapping and Profiler Suite (OMPS)

Can Li1,2, Nickolay A. Krotkov2, Simon Carn3, Yan Zhang1,2, Robert J. D. Spurr4, and Joanna Joiner2 Can Li et al.
  • 1Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20742, USA
  • 2NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
  • 3Department of Geological and Mining Engineering and Sciences, Michigan Technological University, Houghton, MI 49931, USA
  • 4RT Solutions, Inc., Cambridge, MA 02138, USA

Abstract. Since the fall of 2004, the Ozone Monitoring Instrument (OMI) has been providing global monitoring of volcanic SO2 emissions, helping to understand their climate impacts and to mitigate aviation hazards. Here we introduce a new-generation OMI volcanic SO2 dataset based on a principal component analysis (PCA) retrieval technique. To reduce retrieval noise and artifacts as seen in the current operational linear fit (LF) algorithm, the new algorithm, OMSO2VOLCANO, uses characteristic features extracted directly from OMI radiances in the spectral fitting, thereby helping to minimize interferences from various geophysical processes (e.g., O3 absorption) and measurement details (e.g., wavelength shift). To solve the problem of low bias for large SO2 total columns in the LF product, the OMSO2VOLCANO algorithm employs a table lookup approach to estimate SO2 Jacobians (i.e., the instrument sensitivity to a perturbation in the SO2 column amount) and iteratively adjusts the spectral fitting window to exclude shorter wavelengths where the SO2 absorption signals are saturated. To first order, the effects of clouds and aerosols are accounted for using a simple Lambertian equivalent reflectivity approach. As with the LF algorithm, OMSO2VOLCANO provides total column retrievals based on a set of predefined SO2 profiles from the lower troposphere to the lower stratosphere, including a new profile peaked at 13  km for plumes in the upper troposphere. Examples given in this study indicate that the new dataset shows significant improvement over the LF product, with at least 50 % reduction in retrieval noise over the remote Pacific. For large eruptions such as Kasatochi in 2008 (∼ 1700 kt total SO2) and Sierra Negra in 2005 (> 1100 DU maximum SO2), OMSO2VOLCANO generally agrees well with other algorithms that also utilize the full spectral content of satellite measurements, while the LF algorithm tends to underestimate SO2. We also demonstrate that, despite the coarser spatial and spectral resolution of the Suomi National Polar-orbiting Partnership (Suomi-NPP) Ozone Mapping and Profiler Suite (OMPS) instrument, application of the new PCA algorithm to OMPS data produces highly consistent retrievals between OMI and OMPS. The new PCA algorithm is therefore capable of continuing the volcanic SO2 data record well into the future using current and future hyperspectral UV satellite instruments.

Publications Copernicus
Short summary
In this paper, we describe the new-generation OMI volcanic SO2 algorithm based on our principal component analysis (PCA) retrieval technique. We demonstrate significant improvement in the our new OMI volcanic SO2 data, with the retrieval noise reduced by a factor of 2 as compared with the previous dataset. The algorithm also improves the accuracy for large volcanic eruptions. It is also capable of producing consistent retrievals between different instruments.
In this paper, we describe the new-generation OMI volcanic SO2 algorithm based on our principal...