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)

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.