Changes in the emission rate of volcanic sulphur dioxide (SO2) are crucial parameters for identifying volcanic unrest and forecasting the eruptive activity. Ground-based ultraviolet (UV) remote sensing provides a near continuous record of the SO2 emission rate, with Differential Optical Absorption Spectroscopy (DOAS) being the preferred method for quantifying SO2 absorption from recorded spectra. However, retrieving accurate column amounts of SO2 using DOAS requires a complex fitting procedure that relies on user expertise for selecting suitable fit parameters and visually inspecting the fit results. We explore an alternative approach that exploits the well-defined spatial frequencies present in sky-scattered UV spectra. We use wavelet coherence to compare UV spectra recorded with calibration cells of known SO2 concentration in the wavelength–spatial frequency plane. Our findings reveal that the Magnitude-Squared Wavelet Coherence (MSWC) is inversely proportional to the SO2 concentration, suggesting that this relationship could be used to quantify volcanic SO2 in natural spectra. To validate this approach, we analyze UV spectra recorded by scanning-DOAS instruments from the Network of Volcanic and Atmospheric Change (NOVAC) at Masaya volcano, Nicaragua, and Soufrière Hills volcano, Montserrat. We observe a favourable comparison between the MSWC values we calculate and the slant column densities (SCDs) of SO2 obtained using the DOAS and iFit algorithms, respectively. We demonstrate the MSWC to be a robust indicator of SO2 which may potentially serve as a proxy for differential SCDs of volcanic SO2. The straightforward computation of the wavelet coherence between spectra offers an efficient means to identify spectra which contain the signature of the volcanic plume and an objective approach to validate results obtained using traditional fitting routines.
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