Volcanic SO<sub>2</sub> and SiF<sub>4</sub> visualization using 2-D thermal emission spectroscopy – Part 2: Wind propagation and emission rates

Abstract

Abstract. A technique for measuring two-dimensional (2-D)plumes of volcanic gases with thermal emission spectroscopy was described in Part 1 by Stremme et al. (2012a). In that paper the instrumental aspects as well as retrieval strategies for obtaining the slant column images of SO 2 and SiF 4 , as well as animations of particular events observed at the Popocatépetl volcano, were presented. This work focuses on the procedures for determining the propagation speed of the gases and estimating an emission rate from the given image sequences. A 2-D column density distribution of a volcanic gas, available as time-consecutive frames, provides information of a projected wind field and the average velocity at which the volcanic plume is propagating. This information is valuable since the largest uncertainties when calculating emission rates of the gases using remote sensing techniques arise from propagation velocities which are often inadequately assumed. The presented reconstruction method solves the equation of continuity as an ill-posed problem using mainly a Tikhonov-like regularisation. It is observed from the available data sets that if the main direction of propagation is perpendicular to the line-of-sight, the algorithm works well for SO 2 , which has the strongest signals, and also for SiF 4 in some favourable cases. Due to the similarity of the algorithm used here with the reconstruction methods used for profile retrievals based on optimal estimation theory, diagnostic tools like the averaging kernels can be calculated in an analogous manner and the information can be quantified as degrees of freedom. Thus, it is shown that the combination of wind field and column distribution of the gas plume can provide the emission rate of the volcano both during day and night.

Top-down estimation of carbon monoxide emissions from the Mexico Megacity based on FTIR measurements from ground and space

“…UNAM instrument: The ground-based spectra at the UNAM campus were taken with a Bruker OPAG22 interferometer at 0.5 cm −1 spectral resolution. The interferometer is part of a Scanning Infrared Gas Imaging System (SIGIS) built to measure infrared emission spectra at 1360 W. Stremme et al: Carbon monoxide emissions from the Mexico Megacity different pointing directions used in various applications (Grutter et al, 2008a;Grutter et al, 2008b;Stremme et al, 2012;Krueger et al, 2013) but adapted to measure solar absorption spectra as described by Stremme et al (2009Stremme et al ( , 2011. It consists of a scanning mirror which directs the solar radiation to a 1/4-telescope prior to entering the spectrometer, which is equipped with a KBr beamsplitter and a MCT detector.…”

Real-time remote detection and measurement for airborne imaging spectroscopy: a case study with methane

“…Another real-time airborne investigation used non-imaging spectroscopy for detecting dangerous volcanic plumes (Vogel et al, 2011). On the ground, real-time analysis has imaged these plumes' SO 2 and SiF 4 absorption (Lübcke et al, 2013;Stremme et al, 2012), enabling subsequent analysis to infer emission rates Krueger et al (2013).…”

Improved optical flow velocity analysis in SO<sub>2</sub> camera images of volcanic plumes – implications for emission-rate retrievals investigated at Mt Etna, Italy and Guallatiri, Chile

“…The velocities, if retrieved from the images, represent averages along the line of sight (LoS) of each pixel (see e.g. Krueger et al, 2013 for a derivation). Since the velocity components in the LoS direction cannot be measured from the images, the measured velocities are approximately underestimated by a factor of cos(α) (α being the angle between plume direction and image plane).…”

“…Luckily, the high resolution of the imaging systems allows us to account for these spatial and temporal fluctuations by directly measuring the projected 2-D velocity fields using optical flow (OF) algorithms (e.g. Krueger et al, 2013, Bjornsson et al, 2013, Peters et al, 2015, Lopez et al, 2015, Stebel et al, 2015, Kern et al, 2015.…”