The New Volcanic Ash Satellite Retrieval Vacos Using Msg/Seviri And Artificial Neural Networks: 2. Validation

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 Eyjafjallajokull (2010) and Puyehue-Cordon 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 similar to 40% or less for ash layers with an optical thickness at 10.8 mu m 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 mu m. 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 mu m increases by only up to similar to 30%. Viewing zenith angles >60 degrees increase the mean percentage error by up to similar 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.