A Novel Approach to Estimating Time-Averaged Volcanic SO2 Fluxes from Infrared Satellite Measurements

Abstract

Long-term continuous time series of SO2 emissions are considered critical elements of both volcano monitoring and basic research into processes within magmatic systems. One highly successful framework for computing these fluxes involves reconstructing a representative time-averaged SO2 plume from which to estimate the SO2 source flux. Previous methods within this framework have used ancillary wind datasets from reanalysis or numerical weather prediction (NWP) to construct the mean plume and then again as a constrained parameter in the fitting. Additionally, traditional SO2 datasets from ultraviolet (UV) sensors lack altitude information, which must be assumed, to correctly calibrate the SO2 data and to capture the appropriate NWP wind level which can be a significant source of error. We have made novel modifications to this framework which do not rely on prior knowledge of the winds and therefore do not inherit errors associated with NWP winds. To perform the plume rotation, we modify a rudimentary computer vision algorithm designed for object detection in medical imaging to detect plume-like objects in gridded SO2 data. We then fit a solution to the general time-averaged dispersion of SO2 from a point source. We demonstrate these techniques using SO2 data generated by a newly developed probabilistic layer height and column loading algorithm designed for the Cross-track Infrared Sounder (CrIS), a hyperspectral infrared sensor aboard the Joint Polar Satellite System’s Suomi-NPP and NOAA-20 satellites. This SO2 data source is best suited to flux estimates at high-latitude volcanoes and at low-latitude, but high-altitude volcanoes. Of particular importance, IR SO2 data can fill an important data gap in the UV-based record: estimating SO2 emissions from high-latitude volcanoes through the polar winters when there is insufficient solar backscatter for UV sensors to be used.

Automatic estimation of daily volcanic sulfur dioxide gas flux from TROPOMI satellite observations: application to Etna and Piton de la Fournaise

“…Taking the "e-folding distance" u/k as the characteristic length L (e.g. as in Hyman et al, 2021) translates the slender plume approximation into: P e = u 2 /D x k ≫ 1. The extent of the P e ≫ 1 domain, as a function of u, k and D x is represented in Figure S1.…”

“…This plume model depends on three atmospheric parameters (the wind speed u, the cross-wind diffusivity D y and the gas loss rate k) which are not retrieved by the inversion (unlike, e.g. Hyman et al, 2021). Indeed, the outputs are independent of the actual values of k, D y and u, so long as they remain within certain ranges of validity (see Section 4).…”

“…3. "Wind-rotation" methods apply a correction to compensate changing day-to-day plume directions and speeds, which makes it possible to fit a simplified model of gas transport, loss rate and dispersion, either on daily observations, or on stacked measurements providing monthly-or annually-averaged emission budgets released by "hotspots" (Beirle et al, 2014;Fioletov et al, 2016;Carn et al, 2017;Hyman et al, 2021;Fioletov et al, 2023). 4.…”

Automatic Estimation of Daily Volcanic Sulfur Dioxide Gas Flux From TROPOMI Satellite Observations: Application to Etna and Piton de la Fournaise

“…3. "Wind-rotation" methods apply a correction to compensate changing day-to-day plume directions and speeds, which makes it possible to fit a simplified model of gas transport, loss rate and dispersion, either on daily observations, or on stacked measurements providing monthly-or annually-averaged emission budgets released by "hotspots" (Beirle et al, 2014;Carn et al, 2017;Fioletov et al, 2016Fioletov et al, , 2023Hyman et al, 2021). 4.…”

“…“Plume traverses” consist of computing plume cross‐sections (defined as the integral of column amounts on a transect perpendicular to the plume), followed by multiplication by plume speed (Carn et al., 2003; Merucci et al., 2011). “Wind‐rotation” methods apply a correction to compensate changing day‐to‐day plume directions and speeds, which makes it possible to fit a simplified model of gas transport, loss rate and dispersion, either on daily observations, or on stacked measurements providing monthly‐ or annually‐averaged emission budgets released by “hotspots” (Beirle et al., 2014; Carn et al., 2017; Fioletov et al., 2016, 2023; Hyman et al., 2021). “Inverse modeling” attempts to match the observed spatial distribution of vertical column densities against simulations from a numerical (chemistry‐)transport model, initialized with a weather model, thereby incorporating potentially complex atmospheric processes such as diffusion, deposition and/or chemical conversion (Behera et al., 2023; Boichu et al., 2013, 2014, 2015; Cai et al., 2022; Eckhardt et al., 2008; Flemming & Inness, 2013; Heng et al., 2016; Kristiansen et al., 2010; Moxnes et al., 2014; Theys et al., 2013; Vira et al., 2017).…”