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Pinzer B.R.,WSL Institute for Snow and Avalanche Research SLF Davos | Medebach A.,Paul Scherrer Institute | Limbach H.J.,Nestle | Dubois C.,Nestle | And 3 more authors.
Soft Matter | Year: 2012

The microstructure of food is key to its sensorial perception, and methods to characterize the microstructure are of crucial importance in food engineering. Ice cream is a special example whose microstructure changes dramatically in response to temperature variations. Since ice cream is a multiphase material, the complex interactions among the phases and the physical mechanisms that drive the evolution of microstructure are not yet well understood. This is mostly due to the fact that observing the microstructure with traditional microscopic methods is destructive and does not allow the study of undisturbed samples. With X-ray micro-tomography, it is possible to overcome these limitations and carry out time lapse studies of the evolution of the microstructure of ice cream. Using iodine as a contrast agent, we measured the three-dimensional distribution of the three main phases (air, unfrozen sugar solution, and ice crystals) with a voxel size of 6 μm. An automated routine was developed that allows for the segmentation of the three phases. Based on the three-dimensional data we calculated the temporal evolution of air bubble sizes and ice crystal sizes during cyclic variations of temperature. Under the given temperature variations we find strong hints that for ice crystal coarsening a melt refreeze mechanism and for air microstructure coarsening coalescence are the dominating underlying mechanisms. This method - which can be applied to a plethora of soft multiphase materials - provides new insights into the coarsening mechanisms of multiphase materials and could contribute to a better understanding of complex materials. © 2012 The Royal Society of Chemistry.

Ignatius K.,Leibniz Institute for Tropospheric Research | Kristensen T.B.,Leibniz Institute for Tropospheric Research | Jarvinen E.,Karlsruhe Institute of Technology | Nichman L.,University of Manchester | And 28 more authors.
Atmospheric Chemistry and Physics | Year: 2016

There are strong indications that particles containing secondary organic aerosol (SOA) exhibit amorphous solid or semi-solid phase states in the atmosphere. This may facilitate heterogeneous ice nucleation and thus influence cloud properties. However, experimental ice nucleation studies of biogenic SOA are scarce. Here, we investigated the ice nucleation ability of viscous SOA particles. The SOA particles were produced from the ozone initiated oxidation of α-pinene in an aerosol chamber at temperatures in the range from-38 to-10°C at 5-15% relative humidity with respect to water to ensure their formation in a highly viscous phase state, i.e. semi-solid or glassy. The ice nucleation ability of SOA particles with different sizes was investigated with a new continuous flow diffusion chamber. For the first time, we observed heterogeneous ice nucleation of viscous α-pinene SOA for ice saturation ratios between 1.3 and 1.4 significantly below the homogeneous freezing limit. The maximum frozen fractions found at temperatures between-39.0 and-37.2°C ranged from 6 to 20% and did not depend on the particle surface area. Global modelling of monoterpene SOA particles suggests that viscous biogenic SOA particles are indeed present in regions where cirrus cloud formation takes place. Hence, they could make up an important contribution to the global ice nucleating particle budget. © 2016 Author(s).

Marty C.,WSL Institute for Snow and Avalanche Research SLF Davos | Blanchet J.,WSL Institute for Snow and Avalanche Research SLF Davos | Blanchet J.,Ecole Polytechnique Federale de Lausanne
Climatic Change | Year: 2012

Mountain snow cover is an important source of water and essential for winter tourism in Alpine countries. However, large amounts of snow can lead to destructive avalanches, floods, traffic interruptions or even the collapse of buildings. We use annual maximum snow depth and snowfall data from 25 stations (between 200 and 2,500 m) collected during the last 80 winters (1930/31 to 2009/2010) to highlight temporal trends of annual maximum snow depth and 3-day snowfall sum. The generalized extreme value (GEV) distribution with time as a covariate is used to assess such trends. It allows us in particular to infer how return levels and return periods have been modified during the last 80 years. All the stations, even the highest one, show a decrease in extreme snow depth, which is mainly significant at low altitudes (below 800 m). A negative trend is also observed for extreme snowfalls at low and high altitudes but the pattern at mid-altitudes (between 800 and 1,500 m) is less clear. The decreasing trend of extreme snow depth and snowfall at low altitudes seems to be mainly caused by a reduction in the magnitude of the extremes rather than the scale (variability) of the extremes. This may be caused by the observed decrease in the snow/rain ratio due to increasing air temperatures. In contrast, the decreasing trend in extreme snow depth above 1,500 m is caused by a reduction in the scale (variability) of the extremes and not by a reduction in the magnitude of the extremes. However, the decreasing trends are significant for only about half of the stations and can only be seen as an indication that climate change may be already impacting extreme snow depth and extreme snowfall. © 2011 Springer Science+Business Media B.V.

Nichman L.,University of Manchester | Fuchs C.,Paul Scherrer Institute | Jarvinen E.,Karlsruhe Institute of Technology | Ignatius K.,Leibniz Institute for Tropospheric Research | And 34 more authors.
Atmospheric Chemistry and Physics | Year: 2016

Cloud microphysical processes involving the ice phase in tropospheric clouds are among the major uncertainties in cloud formation, weather, and general circulation models. The detection of aerosol particles, liquid droplets, and ice crystals, especially in the small cloud particle-size range below 50 μm, remains challenging in mixed phase, often unstable environments. The Cloud Aerosol Spectrometer with Polarization (CASPOL) is an airborne instrument that has the ability to detect such small cloud particles and measure the variability in polarization state of their backscattered light. Here we operate the versatile Cosmics Leaving OUtdoor Droplets (CLOUD) chamber facility at the European Organization for Nuclear Research (CERN) to produce controlled mixed phase and other clouds by adiabatic expansions in an ultraclean environment, and use the CASPOL to discriminate between different aerosols, water, and ice particles. In this paper, optical property measurements of mixed-phase clouds and viscous secondary organic aerosol (SOA) are presented. We report observations of significant liquid–viscous SOA particle polarization transitions under dry conditions using CASPOL. Cluster analysis techniques were subsequently used to classify different types of particles according to their polarization ratios during phase transition. A classification map is presented for water droplets, organic aerosol (e.g., SOA and oxalic acid), crystalline substances such as ammonium sulfate, and volcanic ash. Finally, we discuss the benefits and limitations of this classification approach for atmospherically relevant concentrations and mixtures with respect to the CLOUD 8-9 campaigns and its potential contribution to tropical troposphere layer analysis. © 2016 Author(s).

Rubin M.J.,Colorado School of Mines | Camp T.,Colorado School of Mines | Herwijnen A.V.,WSL Institute for Snow and Avalanche Research SLF Davos | Schweizer J.,WSL Institute for Snow and Avalanche Research SLF Davos
Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012 | Year: 2012

During the 2010-2011 winter season, we deployed seven geophones on a mountain outside of Davos, Switzerland and collected over 100 days of seismic data containing 385 possible avalanche events (33 confirmed slab avalanches). In this article, we describe our efforts to develop a pattern recognition workflow to automatically detect snow avalanche events from passive seismic data. Our initial workflow consisted of frequency domain feature extraction, cluster-based stratified subsampling, and 100 runs of training and testing of 12 different classification algorithms. When tested on the entire season of data from a single sensor, all twelve machine learning algorithms resulted in mean classification accuracies above 84%, with seven classifiers reaching over 90%. We then experimented with a voting based paradigm that combined information from all seven sensors. This method increased overall accuracy and precision, but performed quite poorly in terms of classifier recall. We, therefore, decided to pursue other signal preprocessing methodologies. We focused our efforts on improving the overall performance of single sensor avalanche detection, and employed spectral flux based event selection to identify events with significant instantaneous increases in spectral energy. With a threshold of 90% relative spectral flux increase, we correctly selected 32 of 33 slab avalanches and reduced our problem space by nearly 98%. When trained and tested on this reduced data set of only significant events, a decision stump classifier achieved 93% overall accuracy, 89.5% recall, and improved the precision of our initial workflow from 2.8% to 13.2%. © 2012 IEEE.

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