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A comprehensive understanding of the cloud thermodynamic phase is crucial for assessing the cloud radiative effect and is a prerequisite for remote sensing retrievals of microphysical cloud properties. While previous algorithms mainly detected ice and liquid phases, there is now a growing awareness for the need to further distinguish between warm liquid, supercooled and mixed-phase clouds. To address this need, we introduce a novel method named ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI), which enables cloud detection and the determination of cloud-top phase using SEVIRI (Spinning Enhanced Visible and Infrared Imager), the geostationary passive imager aboard Meteosat Second Generation. ProPS discriminates between clear sky, optically thin ice (TI) cloud, optically thick ice (IC) cloud, mixed-phase (MP) cloud, supercooled liquid (SC) cloud and warm liquid (LQ) cloud. Our method uses a Bayesian approach based on the cloud mask and cloud phase from the lidar–radar cloud product DARDAR (liDAR/raDAR). The validation of ProPS using 6 months of independent DARDAR data shows promising results: the daytime algorithm successfully detects 93 % of clouds and 86 % of clear-sky pixels. In addition, for phase determination, ProPS accurately classifies 91 % of IC, 78 % of TI, 52 % of MP, 58 % of SC and 86 % of LQ clouds, providing a significant improvement in accurate cloud-top phase discrimination compared to traditional retrieval methods. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

High quality volcanic ash forecasts are crucial to minimize the economic impact of volcanic hazards on air traffic. Decision-making is usually based on numerical dispersion modeling with only one model realization. Given the inherent uncertainty of such approach, a multi-model multi-source term ensemble has been designed and evaluated for the Eyjafjallajökull eruption in May 2010. Its use for air traffic management is discussed. Two multi-model ensembles were built: the first is based on the output of four dispersion models and their own implementation of ash ejection. All a priori model source terms were constrained by observational evidence of the volcanic ash cloud top as a function of time. The second ensemble is based on the same four dispersion models, which were run with three additional source terms: (i) a source term obtained with background modeling constrained with satellite data (a posteriori source term), (ii) its lower bound estimate, and (iii) its upper bound estimate. The a priori ensemble gives valuable information about the probability of ash dispersion during the early phase of the eruption, when observational evidence is limited. However, its evaluation with observational data reveals lower quality compared to the second ensemble. While the second ensemble ash column load and ash horizontal location compare well to satellite observations, 3D ash concentrations are negatively biased. This might be caused by the vertical distribution of ash, which is too much diluted in all model runs, probably due to defaults in the a posteriori source term and vertical transport and/or diffusion processes in all models. Relevant products for the air traffic management are horizontal maps of ash concentration quantiles (median, 75 %, 99 %) at a fine-resolved flight level grid. These maps can be used for route optimization in the areas where ash does not pose a direct and urgent threat to aviation. Cost-optimized consideration of such hazards will result in much less impact on flight cancellations, reroutings, and traffic flow congestions. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANNs) have been proven to be a powerful tool for classification and parameter estimation and have been successfully applied to various remote sensing problems (e.g. Piscini et al. 2014, Strandgren et al. 2017). Here we use them to detect and analyze volcanic ash clouds pixelwise in MSG-SEVIRI images. The quality of the training dataset is of crucial importance for the accuracy of such an algorithm. Our approach relies on detailed radiative transfer calculations based on realistic ash properties to train the ANN. Therefore we present thoroughly investigations of the different aspects that must be considered to perform these radiative transfer calculations.First, we address the question of the refractive index (RI) of volcanic ash. In the past, this was a major uncertainty as it can vary strongly between different eruptions (Reed et al. 2017), but for a long time there existed only a few RI measurements reported by Pollack, Toon and Khare (1973) and Volz (1973). Here we show that it is possible to mimic the RI and the dense rock equivalent density of different ash samples using typical volcanic ash compositions and measurements of individual minerals and volcanic glasses. Based on this we compile a collection of different ash compositions and calculate their RIs. Second, we tackle the big uncertainty represented by the shape and size of the ash particles. For instance many retrievals assumed spherical particles in the past (e.g. Prata and Grant 2001, Gangale et al. 2009, Lee et al. 2014), although ash particles are usually highly aspherical and irregular (Cashman and Rust in "Volcanic Ash: Hazard Observation", 2016). The effective radius varies between different measurements and eruptions by more than one order of magnitude, e.g. between 0.5μm and 9μm (Grainger et al. 2013). Third, we estimate the influence of the vertical profile and multiple ash layers on the measured brightness temperature as well as typical column concentrations and cloud top heights that are retrievable. Finally, we present the training dataset based on the previous findings and calculated using the radiative transfer model libRadtran (Emde et al. 2016), as well as a first prototype algorithm for the retrieval. [ABSTRACT FROM AUTHOR]