Estimation of anthropogenic and volcanic SO₂ emissions from satellite data in the presence of snow/ice on the ground

Abstract. Early versions of satellite nadir-viewing UV SO₂ data products did not explicitly account for the effects of snow/ice on retrievals. Snow covered terrain, with its high reflectance in the UV, typically enhances satellite sensitivity to boundary layer pollution. However, a significant fraction of high-quality cloud-free measurements over snow is currently excluded from analyses. This leads to increased uncertainties of satellite emissions estimates and potential seasonal biases due to the lack of data in winter months for some high-latitudinal sources. In this study, we investigated how OMI and TROPOMI satellite SO₂ measurements over snow-covered surfaces can be used to improve the annual emissions reported in our SO₂ emissions catalogue (version 2, Fioletov et al., 2023). Only 100 out of 759 sources listed in the catalogue have 10 % or more of the observations over snow. However, for 40 high-latitude sources, more than 30 % of measurements suitable for emission calculations were made over snow-covered surfaces. For example, in the case of Norilsk, the world’s largest SO₂ point source, annual emissions estimates in the SO₂ catalogue were based only on 3–4 summer months, while addition of data for snow conditions extends that period to 7 months. Emissions in the SO₂ catalogue were based on satellite measurements of SO₂ slant column densities (SCDs) that were converted to vertical column densities (VCDs) using site-specific clear-sky air mass factors (AMFs), calculated for snow-free conditions. The same approach was applied to measurements with snow on the ground whereby a new set of constant, site-specific, clear sky with snow AMFs was created, and these were applied to the measured SCDs. Annual emissions were then estimated for each source considering (i) only clear-sky snow-free days, (ii) only clear-sky with snow days and (iii) a merged dataset (snow and snow-free conditions). For individual sources, the difference between emissions estimated for snow and snow-free conditions is within ±20 % for three quarters of smelters and oil and gas sources, and with practically no systematic bias. This is excellent consistency given that there is typically a factor of 3–5 difference between AMFs for snow and snow-free conditions. For coal-fired power plants, however, emissions estimated for snow conditions are on average 25 % higher than for snow-free conditions; this difference is likely real and due to larger production (consumption of coal) and emissions in wintertime.