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Abstract. After the eruption of volcanoes around the world, 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, tailored for Eyjafjallajökull ash but applicable to other eruptions as well, 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 the 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 of up to 0.7 for ash clouds with high mass column concentrations. A comparison with spaceborne lidar observations by CALIPSO/CALIOP confirms these results: For six overpasses over the ash cloud from the Puyehue-Cordón Caulle volcano in June 2011, VADUGS shows similar features as the corresponding lidar data, with a correlation coefficient of 0.49 and an overestimation of ash column concentration by 55 %, although still in the range of uncertainty of CALIOP. A comparison with another ash algorithm shows that both retrievals provide plausible detection results, with VADUGS being able to detect ash further away from the Eyjafjallajökull volcano, but sometimes missing the thick ash clouds close to the vent. VADUGS is run operationally at the German Weather Service and this application is also presented.

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Surveys in Geophysics 38 (2017): 1529–1568, doi:10.1007/s10712-017-9428-0. ; Trade-wind cumuli constitute the cloud type with the highest frequency of occurrence on Earth, and it has been shown that their sensitivity to changing environmental conditions will critically influence the magnitude and pace of future global warming. Research over the last decade has pointed out the importance of the interplay between clouds, convection and circulation in controling this sensitivity. Numerical models represent this interplay in diverse ways, which translates into different responses of trade-cumuli to climate perturbations. Climate models predict that the area covered by shallow cumuli at cloud base is very sensitive to changes in environmental conditions, while process models suggest the opposite. To understand and resolve this contradiction, we propose to organize a field campaign aimed at quantifying the physical properties of trade-cumuli (e.g., cloud fraction and water content) as a function of the large-scale environment. Beyond a better understanding of clouds-circulation coupling processes, the campaign will provide a reference data set that may be used as a benchmark for advancing the modelling and the satellite remote sensing of clouds and circulation. It will also be an opportunity for complementary investigations such as evaluating model convective parameterizations or studying the role of ocean mesoscale eddies in air–sea interactions and convective organization. ; The EUREC4A project is supported by the European Research Council (ERC), under the European Union's Horizon 2020 research and innovation programme (Grant Agreement No. 694768), by the Max Planck Society and by DFG (Deutsche Forschungsgemeinschaft, German Research Foundation) Priority Program SPP 1294.