Abstract
Volcanic ash clouds can damage aircrafts during flight and, thus, have the potential to disrupt air traffic on a large scale, making their detection and monitoring necessary. The new retrieval algorithm VACOS (Volcanic Ash Cloud properties Obtained from SEVIRI) using the geostationary instrument MSG/SEVIRI and artificial neural networks is introduced in a companion paper. It performs pixelwise classifications and retrieves (indirectly) the mass column concentration, the cloud top height and the effective particle radius. VACOS is comprehensively validated using simulated test data, CALIOP retrievals, lidar and in situ data from aircraft campaigns of the DLR and the FAAM, as well as volcanic ash transport and dispersion multi model multi source term ensemble predictions. Specifically, emissions of the eruptions of Eyjafjallajökull (2010) and Puyehue-Cordón Caulle (2011) are considered. For ash loads larger than 0.2 g m−2 and a mass column concentration-based detection procedure, the different evaluations give probabilities of detection between 70% and more than 90% at false alarm rates of the order of 0.3–3%. For the simulated test data, the retrieval of the mass load has a mean absolute percentage error of ~40% or less for ash layers with an optical thickness at 10.8 μm of 0.1 (i.e., a mass load of about 0.3–0.7 g m−2, depending on the ash type) or more, the ash cloud top height has an error of up to 10% for ash layers above 5 km, and the effective radius has an error of up to 35% for radii of 0.6–6 μm. The retrieval error increases with decreasing ash cloud thickness and top height. VACOS is applicable even for overlaying meteorological clouds, for example, the mean absolute percentage error of the optical depth at 10.8 μm increases by only up to ~30%. Viewing zenith angles >60° increase the mean percentage error by up to ~20%. Desert surfaces are another source of error. Varying geometrical ash layer thicknesses and the occurrence of multiple layers can introduce an additional error of about 30% for the mass load and 5% for the cloud top height. For the CALIOP data, comparisons with its predecessor VADUGS (operationally used by the DWD) show that VACOS is more robust, with retrieval errors of mass load and ash cloud top height reduced by >10% and >50%, respectively. Using the model data indicates an increase in detection rate in the order of 30% and more. The reliability under a wide spectrum of atmospheric conditions and volcanic ash types make VACOS a suitable tool for scientific studies and air traffic applications related to volcanic ash clouds.
Highlights
A new volcanic ash retrieval using artificial neural networks (ANNs) and the SpinningEnhanced Visible and Infrared Imager (SEVIRI) aboard the Meteosat Second Generation (MSG)satellites is developed and presented; this algorithm is called VACOS (Volcanic Ash CloudProperties Obtained from SpinningEnhanced Visible and Infrared Imager (SEVIRI)) and builds upon its predecessor VADUGS (Volcanic AshDetection Using Geostationry Satellites [1])
We introduced a new algorithm to retrieve volcanic ash properties, i.e., a pixel classification, the cloud top height, the effective particle radius
(indirectly from the optical depth at 10.8 μm, τ10.8 ) the mass column concentration from MSG/SEVIRI data using artificial neural networks; it is called VACOS (Volcanic Ash Cloud properties Obtained from SEVIRI [2])
Summary
Dennis Piontek 1, *, Luca Bugliaro 1 , Jayanta Kar 2,3 , Ulrich Schumann 1 , Franco Marenco 4,5 , Matthieu Plu 6 and Christiane Voigt 1,7. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Climate and Atmosphere Research Centre (CARE-C), Cyprus Institute, Aglantzia, Nicosia 2121, Cyprus. Institut für Physik der Atmosphäre, Johannes Gutenberg-Universität Mainz, 55099 Mainz, Germany
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