Combining radiative transfer calculations and a neural network for the remote sensing of volcanic ash using MSG/SEVIRI

Abstract

Abstract. After the eruption of volcanoes all over the world the 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 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 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 up to 0.7 for ash clouds with high mass column concentrations. A comparison against spaceborne lidar observations by CALIPSO/CALIOPconfirms these results. VADUGS is run operationally at the German Weather Service and this application is presented as well.

Remote sensing of solar surface radiation – a reflection of concepts, applications and input data based on experience with the effective cloud albedo

“…Similar approaches are also used for other atmospheric variables, e.g. for volcanic ash (Bugliaro et al, 2021). However, with respect to SSI, the burden of cumbersome LUTs, which require hundreds of thousands of RTM calculations, is eliminated by using the hybrid eigenvector LUT concept (Mueller et al, 2009).…”

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“…For example, frequent and high‐resolution measurements of carbon dioxide acquired by the Orbiting Carbon Observatory (Sun et al, 2017) helped to increase the understanding of CO$$$ {}_2 $$$ sinks and sources. Massive remotely sensed data were demonstrated to be of help in determining the concentration of volcanic ash in the atmosphere (Bugliaro et al, 2021), which is crucial for air traffic control and weather forecasting. Not all big datasets are collected using satellites however.…”

Scalable Spatio-Temporal Smoothing via Hierarchical Sparse Cholesky Decomposition

“…For example, frequent and highresolution measurements of carbon dioxide acquired by the Orbiting Carbon Observatory (Sun et al, 2017) helped to increase the understanding of CO 2 sinks and sources. Massive remotely-sensed data was demonstrated to be of help in determining the concentration of volcanic ash in the atmosphere (Bugliaro et al, 2021), which is crucial for air traffic control and weather forecasting. Not all big data sets are collected using satellites however.…”