Abstract Volcanic gas emissions, particularly sulfur dioxide (SO2), are crucial for volcano monitoring. SO2 has a significant impact on air quality, the climate, and human health, making it a critical component of volcano monitoring programs. Additionally, SO2 can be used to assess the state of a volcano and the progression of an individual eruption and can serve as a proxy for volcanic ash. The Tajogaite La Palma (Spain) eruption in 2021 emitted large amounts of SO2 over 85 days, with the plume reaching Central Europe. In this study, we present the results achieved by monitoring Tajogaite SO2 emissions from 19 September to 31 October 2021 at different acquisition times (i.e., 10:00 UTC, 12:00 UTC, 14:00 UTC, and 16:00 UTC). An optimized configuration of the Robust Satellite Technique (RST) approach, tailored to volcanic SO2 detection and exploiting the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) channel at an 8.7 µm wavelength, was used. The results, assessed by means of a performance evaluation compared with masks drawn from the EUMETSAT Volcanic Ash RGB, show that the RST product identified volcanic SO2 plumes on approximately 81% of eruption days, with a very low false-positive rate (2% and 0.3% for the mid/low and high-confidence-level RST products, respectively), a weighted precision of ~79%, and an F1-score of ~54%. In addition, the comparison with the Tropospheric Monitoring Instrument (TROPOMI) S5P Product Algorithm Laboratory (S5P-PAL) L3 grid Daily SO2 CBR product shows that RST-SEVIRI detections were mostly associated with SO2 plumes having a column density greater than 0.4 Dobson Units (DU). This study gives rise to some interesting scenarios regarding the near-real-time monitoring of volcanic SO2 by means of the Flexible Combined Imager (FCI) aboard the Meteosat Third-Generation (MTG) satellites, offering improved instrumental features compared with the SEVIRI.
Robust Satellite Techniques (RSTs) for SO2 Detection with MSG-SEVIRI Data: A Case Study of the 2021 Tajogaite Eruption
Falconieri, AlfredoWriting – Review & Editing
;Pergola, NicolaConceptualization
;Tramutoli, ValerioMethodology
;
2025-01-01
Abstract
Abstract Volcanic gas emissions, particularly sulfur dioxide (SO2), are crucial for volcano monitoring. SO2 has a significant impact on air quality, the climate, and human health, making it a critical component of volcano monitoring programs. Additionally, SO2 can be used to assess the state of a volcano and the progression of an individual eruption and can serve as a proxy for volcanic ash. The Tajogaite La Palma (Spain) eruption in 2021 emitted large amounts of SO2 over 85 days, with the plume reaching Central Europe. In this study, we present the results achieved by monitoring Tajogaite SO2 emissions from 19 September to 31 October 2021 at different acquisition times (i.e., 10:00 UTC, 12:00 UTC, 14:00 UTC, and 16:00 UTC). An optimized configuration of the Robust Satellite Technique (RST) approach, tailored to volcanic SO2 detection and exploiting the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) channel at an 8.7 µm wavelength, was used. The results, assessed by means of a performance evaluation compared with masks drawn from the EUMETSAT Volcanic Ash RGB, show that the RST product identified volcanic SO2 plumes on approximately 81% of eruption days, with a very low false-positive rate (2% and 0.3% for the mid/low and high-confidence-level RST products, respectively), a weighted precision of ~79%, and an F1-score of ~54%. In addition, the comparison with the Tropospheric Monitoring Instrument (TROPOMI) S5P Product Algorithm Laboratory (S5P-PAL) L3 grid Daily SO2 CBR product shows that RST-SEVIRI detections were mostly associated with SO2 plumes having a column density greater than 0.4 Dobson Units (DU). This study gives rise to some interesting scenarios regarding the near-real-time monitoring of volcanic SO2 by means of the Flexible Combined Imager (FCI) aboard the Meteosat Third-Generation (MTG) satellites, offering improved instrumental features compared with the SEVIRI.| File | Dimensione | Formato | |
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