Explosive volcanic eruptions can generate ash and gas clouds rising to the stratosphere and dispersing on a global scale. Such volcanic features are at the origin of many hazards including aircraft engine damages, ash fallouts and health threats. It is thus crucial, to mitigate such hazards, to monitor volcanic clouds dispersion and altitude. In this study, we use the Global Navigation Satellite System (GNSS) Radio Occultation (RO) technique to assess the volcanic cloud altitude resulting from the 2015 Calbuco's eruption. We find volcanic cloud altitude estimations based on RO data in good agreement with the collocated Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) and the Infrared Atmospheric Sounding Interferometer (IASI). The preliminary results of this study confirm that automatized RO profiles processing has great potential in the field of volcanic clouds monitoring.

One of the most prominent topics in climate research is the investigation, detection, and allocation of climate change. In this paper, we aim at identifying regions in the atmosphere (e.g., certain height layers) which can act as sensitive and robust indicators for climate change. We demonstrate how interactive visual data exploration of large amounts of multi-variate and time-dependent climate data enables the steered generation of promising hypotheses for subsequent statistical evaluation. The use of new visualization and interaction technology--in the context of a coordinated multiple views framework--allows not only to identify these promising hypotheses, but also to efficiently narrow down parameters that are required in the process of computational data analysis. Two datasets, namely an ECHAM5 climate model run and the ERA-40 reanalysis incorporating observational data, are investigated. Higher-order information such as linear trends or signal-to-noise ratio is derived and interactively explored in order to detect and explore those regions which react most sensitively to climate change. As one conclusion from this study, we identify an excellent potential for usefully generalizing our approach to other, similar application cases, as well.