VADUGS: A neural network for the remote sensing of volcanic ash with MSG/SEVIRI trained with synthetic thermal satellite observations simulated with a radiative transfer model

Bugliaro, L; Piontek, D; Kox, S; Grainger, RG

Journal article

VADUGS: A neural network for the remote sensing of volcanic ash with MSG/SEVIRI trained with synthetic thermal satellite observations simulated with a radiative transfer model

Abstract:: After the eruption of volcanoes around the world, monitoring of the dispersion of ash in the atmosphere is an important task for satellite remote sensing since ash represents a threat to air traffic. In this work we present a novel method, tailored for Eyjafjallajökull ash but applicable to other eruptions as well, that uses thermal observations of the SEVIRI imager aboard the geostationary Meteosat Second Generation satellite to detect ash clouds and determine their mass column concentration and top height during the day and night. This approach requires the compilation of an extensive data set of synthetic SEVIRI observations to train an artificial neural network. This is done by means of the RTSIM tool that combines atmospheric, surface and ash properties and runs automatically a large number of radiative transfer calculations for the entire SEVIRI disk. The resulting algorithm is called “VADUGS” (Volcanic Ash Detection Using Geostationary Satellites) and has been evaluated against independent radiative transfer simulations. VADUGS detects ash-contaminated pixels with a probability of detection of 0.84 and a false-alarm rate of 0.05. Ash column concentrations are provided by VADUGS with correlations up to 0.5, a scatter up to 0.6 g m−2 for concentrations smaller than 2.0 g m−2 and small overestimations in the range 5 %–50 % for moderate viewing angles 35–65∘, but up to 300 % for satellite viewing zenith angles close to 90 or 0∘. Ash top heights are mainly underestimated, with the smallest underestimation of −9 % for viewing zenith angles between 40 and 50∘. Absolute errors are smaller than 70 % and with high correlation coefficients of up to 0.7 for ash clouds with high mass column concentrations. A comparison with spaceborne lidar observations by CALIPSO/CALIOP confirms these results: For six overpasses over the ash cloud from the Puyehue-Cordón Caulle volcano in June 2011, VADUGS shows similar features as the corresponding lidar data, with a correlation coefficient of 0.49 and an overestimation of ash column concentration by 55 %, although still in the range of uncertainty of CALIOP. A comparison with another ash algorithm shows that both retrievals provide plausible detection results, with VADUGS being able to detect ash further away from the Eyjafjallajökull volcano, but sometimes missing the thick ash clouds close to the vent. VADUGS is run operationally at the German Weather Service and this application is also presented.

Publication status:: Published

Peer review status:: Peer reviewed

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APA Style

Bugliaro, L., Piontek, D., Kox, S., & Grainger, R. G. (2022). VADUGS: A neural network for the remote sensing of volcanic ash with MSG/SEVIRI trained with synthetic thermal satellite observations simulated with a radiative transfer model. Natural Hazards and Earth System Sciences, 22(3), 1029–1054.

MLA Style

Bugliaro, L., et al. “VADUGS: A Neural Network for the Remote Sensing of Volcanic Ash with MSG/SEVIRI Trained with Synthetic Thermal Satellite Observations Simulated with a Radiative Transfer Model.” Natural Hazards and Earth System Sciences, vol. 22, no. 3, Copernicus Publications, 2022, pp. 1029–54.

Chicago Style

Bugliaro, L, D Piontek, S Kox, and RG Grainger. 2022. “VADUGS: A Neural Network for the Remote Sensing of Volcanic Ash with MSG/SEVIRI Trained with Synthetic Thermal Satellite Observations Simulated with a Radiative Transfer Model.” Natural Hazards and Earth System Sciences 22 (3): 1029–54.
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