Preprints
https://doi.org/10.5194/nhess-2021-270
https://doi.org/10.5194/nhess-2021-270

  27 Sep 2021

27 Sep 2021

Review status: this preprint is currently under review for the journal NHESS.

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

Luca Bugliaro1, Dennis Piontek1, Stephan Kox1,a, Marius Schmidl1,b, Bernhard Mayer2,1, Richard Müller3, Margarita Vázquez-Navarro1,c, Daniel M. Peters4,d, Roy G. Grainger5, Josef Gasteiger2,e, and Jayanta Kar6,7 Luca Bugliaro et al.
  • 1Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
  • 2Ludwig-Maximilians Universität, Meteorologisches Institut, München, Germany
  • 3Deutscher Wetterdienst, Offenbach, Germany
  • 4Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford, UK
  • 5COMET, Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford, UK
  • 6Science System and Applications, Inc., Hampton, VA, USA
  • 7Science Directorate, NASA Langley Research Center, Hampton, VA, USA
  • anow at: Telespazio Germany GmbH, Darmstadt, Germany
  • bnow at: MTU Aero Engines AG
  • cnow at: European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), Darmstadt, Germany
  • dnow at: RAL Space, STFC Rutherford Appleton Laboratory, Harwell, UK
  • enow at: University of Vienna, Faculty of Physics, Vienna, Austria

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.

Luca Bugliaro et al.

Status: open (until 18 Nov 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Luca Bugliaro et al.

Luca Bugliaro et al.

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Short summary
The dispersion of ash in the atmosphere is an important task for satellite remote sensing since ash represents a threat to air traffic. We present an AI-based method that enables to monitor the spatial extension and properties of volcanic ash clouds with high temporal resolution during day and night by means of geostationary satellite measurements. This spaceborne retrieval, based on realistic synthetic observations, runs operationally at the German Weather Service.
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