Identification of blowing snow particles in images from a Multi-Angle Snowflake Camera

Abstract. A new method to automatically discriminate between hydrometeors and blowing snow particles on Multi-Angle Snowflake Camera (MASC) images is introduced. The method uses four selected descriptors related to the image frequency, the number of particles detected per image, and their size and geometry to classify each individual image. The classification task is achieved with a two-component Gaussian mixture model fitted on a subset of representative images of each class from field campaigns in Antarctica and Davos, Switzerland. The performance is evaluated by labeling the subset of images on which the model was fitted. An overall accuracy and a Cohen kappa score of 99.4 % and 98.8 %, respectively, are achieved. In a second step, the probabilistic information is used to flag images composed of a mix of blowing snow particles and hydrometeors, which turns out to occur frequently. The percentage of images belonging to each class from an entire austral summer in Antarctica and during a winter in Davos, respectively, is presented. The capability to distinguish precipitation, blowing snow and a mix of those in MASC images is highly relevant to disentangle the complex interactions between wind, snowflakes and snowpack close to the surface.

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Identification of blowing snow particles in images from a multi-angle snowflake camera

10.5194/tc-2018-248 ◽

2018 ◽

Cited By ~ 1

Author(s):

Mathieu Schaer ◽

Christophe Praz ◽

Alexis Berne

Keyword(s):

Austral Summer ◽

Gaussian Mixture ◽

Second Step ◽

Blowing Snow ◽

Kappa Score ◽

Probabilistic Information ◽

Complex Interactions ◽

Field Campaigns ◽

Number Of Particles ◽

Image Frequency

Abstract. A new method to automatically discriminate between hydrometeors and blowing snow particles on Multi-Angle Snowflake Camera (MASC) images is introduced. The method uses four selected descriptors related to the image frequency, the number of particles detected per image as well as their size and geometry to classify each individual image. The classification task is achieved with a two components Gaussian Mixture Model fitted on a subset of representative images of each class from field campaigns in Antarctica and Davos, Switzerland. The performance is evaluated by labelling the subset of images on which the model was fitted. An overall accuracy and Cohen's Kappa score of 99.4 and 98.8 %, respectively, is achieved. In a second step, the probabilistic information is used to flag images composed of a mix of blowing snow particles and hydrometeors, which turns out to occur frequently. The percentage of images belonging to each class from an entire austral summer in Antartica and during a winter in Davos, respectively, are presented. The capability to distinguish precipitation, blowing snow and a mix of those in MASC images is highly relevant to disentangle the complex interactions between wind, snowflakes and snowpack close to the surface.

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Biosurfactants as demulsifying agents for oil recovery from oily sludge – performance evaluation

Water Science & Technology ◽

10.2166/wst.2013.207 ◽

2013 ◽

Vol 67 (12) ◽

pp. 2875-2881 ◽

Cited By ~ 10

Author(s):

Evans M. N. Chirwa ◽

Tshepo Mampholo ◽

Oluwademilade Fayemiwo

Keyword(s):

Oil Recovery ◽

Sodium Dodecyl ◽

Cost Effective ◽

Solid Particles ◽

Second Step ◽

Oily Sludge ◽

Waste Sludge ◽

Complex Interactions ◽

Oil Water ◽

Living Organisms

The oil producing and petroleum refining industries dispose of a significant amount of oily sludge annually. The sludge typically contains a mixture of oil, water and solid particles in the form of complex slurry. The oil in the waste sludge is inextractible due to the complex composition and complex interactions in the sludge matrix. The sludge is disposed of on land or into surface water bodies thereby creating toxic conditions or depleting oxygen required by aquatic animals. In this study, a fumed silica mixture with hydrocarbons was used to facilitate stable emulsion (‘Pickering’ emulsion) of the oily sludge. The second step of controlled demulsification and separation of oil and sludge into layers was achieved using either a commercial surfactant (sodium dodecyl sulphate (SDS)) or a cost-effective biosurfactant from living organisms. The demulsification and separation of the oil layer using the commercial surfactant SDS was achieved within 4 hours after stopping mixing, which was much faster than the 10 days required to destabilise the emulsion using crude biosurfactants produced by a consortium of petrochemical tolerant bacteria. The recovery rate with bacteria could be improved by using a more purified biosurfactant without the cells.

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The BFS Method Combined with Chemical Cluster Interactions for the Study of Order-Disorder Transitions

Defect and Diffusion Forum ◽

10.4028/www.scientific.net/ddf.263.129 ◽

2007 ◽

Vol 263 ◽

pp. 129-134

Author(s):

Maarten Schurmans ◽

Jan Luyten ◽

Claude Creemers

Keyword(s):

First Principles ◽

Strain Energy ◽

Local Environment ◽

Second Step ◽

Atom Pair ◽

Atomic Displacements ◽

Modeling Techniques ◽

Structural Contribution ◽

Alloying Effects ◽

Number Of Particles

First Principles (FP) methods are invoked to improve the accuracy of Bozzolo-Ferrante- Smith (BFS) model, one of the quantum-approximate modeling techniques for the computation of thermodynamic properties that involve a large number of particles. The BFS method calculates the energy of an atom in an alloy in two steps [1]. A first term pertains to the structural contribution. A recent improvement [2] allows to calculate the strain energy depending on the local environment [1,2] and this involves only pure element properties of the different atomic species. In the second step, binary chemical interactions are taken into account. This was originally done by only two interaction parameters for each atom pair in an alloy. In contrast, the adaptable parameterization of Cluster Expansion Methods (CEM) routinely incorporates any number of FP data to describe ordering in alloy systems. But in standard CEM calculations, no explicit information on local atomic displacements is used. In this work, the BFS chemical energy term is successfully replaced by a CEM chemical term to combine the ability of BFS to account for local displacements and the ability of CEM to include as many FP results as needed for the correct evaluation of alloying effects.

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Solid hydrometeor classification and riming degree estimation from pictures collected with a Multi-Angle Snowflake Camera

Atmospheric Measurement Techniques ◽

10.5194/amt-10-1335-2017 ◽

2017 ◽

Vol 10 (4) ◽

pp. 1335-1357 ◽

Cited By ~ 23

Author(s):

Christophe Praz ◽

Yves-Alain Roulet ◽

Alexis Berne

Keyword(s):

High Performance ◽

Multinomial Logistic Regression ◽

Skill Score ◽

Swiss Alps ◽

Second Step ◽

Probabilistic Information ◽

Heidke Skill Score ◽

Classification Tasks ◽

Type Classification ◽

Mlr Model

Abstract. A new method to automatically classify solid hydrometeors based on Multi-Angle Snowflake Camera (MASC) images is presented. For each individual image, the method relies on the calculation of a set of geometric and texture-based descriptors to simultaneously identify the hydrometeor type (among six predefined classes), estimate the degree of riming and detect melting snow. The classification tasks are achieved by means of a regularized multinomial logistic regression (MLR) model trained over more than 3000 MASC images manually labeled by visual inspection. In a second step, the probabilistic information provided by the MLR is weighed on the three stereoscopic views of the MASC in order to assign a unique label to each hydrometeor. The accuracy and robustness of the proposed algorithm is evaluated on data collected in the Swiss Alps and in Antarctica. The algorithm achieves high performance, with a hydrometeor-type classification accuracy and Heidke skill score of 95 % and 0.93, respectively. The degree of riming is evaluated by introducing a riming index ranging between zero (no riming) and one (graupel) and characterized by a probable error of 5.5 %. A validation study is conducted through a comparison with an existing classification method based on two-dimensional video disdrometer (2DVD) data and shows that the two methods are consistent.

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Measurements of precipitation in Dumont d'Urville, Adélie Land, East Antarctica

The Cryosphere ◽

10.5194/tc-11-1797-2017 ◽

2017 ◽

Vol 11 (4) ◽

pp. 1797-1811 ◽

Cited By ~ 28

Author(s):

Jacopo Grazioli ◽

Christophe Genthon ◽

Brice Boudevillain ◽

Claudio Duran-Alarcon ◽

Massimo Del Guasta ◽

...

Keyword(s):

Austral Summer ◽

Ground Level ◽

Blowing Snow ◽

Climatological Data ◽

Weather Forecasts ◽

First Results ◽

Terre Adélie ◽

Intensive Observation ◽

Rain Radar

Abstract. The first results of a campaign of intensive observation of precipitation in Dumont d'Urville, Antarctica, are presented. Several instruments collected data from November 2015 to February 2016 or longer, including a polarimetric radar (MXPol), a Micro Rain Radar (MRR), a weighing gauge (Pluvio2), and a Multi-Angle Snowflake Camera (MASC). These instruments collected the first ground-based measurements of precipitation in the region of Adélie Land (Terre Adélie), including precipitation microphysics. Microphysical observations during the austral summer 2015/2016 showed that, close to the ground level, aggregates are the dominant hydrometeor type, together with small ice particles (mostly originating from blowing snow), and that riming is a recurring process. Eleven percent of the measured particles were fully developed graupel, and aggregates had a mean riming degree of about 30 %. Spurious precipitation in the Pluvio2 measurements in windy conditions, leading to phantom accumulations, is observed and partly removed through synergistic use of MRR data. The yearly accumulated precipitation of snow (300 m above ground), obtained by means of a local conversion relation of MRR data, trained on the Pluvio2 measurement of the summer period, is estimated to be 815 mm of water equivalent, with a confidence interval ranging between 739.5 and 989 mm. Data obtained in previous research from satellite-borne radars, and the ERA-Interim reanalysis of the European Centre for Medium-Range Weather Forecasts (ECMWF) provide lower yearly totals: 655 mm for ERA-Interim and 679 mm for the climatological data over DDU. ERA-Interim overestimates the occurrence of low-intensity precipitation events especially in summer, but it compensates for them by underestimating the snowfall amounts carried by the most intense events. Overall, this paper provides insightful examples of the added values of precipitation monitoring in Antarctica with a synergistic use of in situ and remote sensing measurements.

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‘Georods’: the development of a four-element geophone for improved seismic imaging of glaciers and ice sheets

Annals of Glaciology ◽

10.3189/2013aog64a432 ◽

2013 ◽

Vol 54 (64) ◽

pp. 142-148 ◽

Cited By ~ 6

Author(s):

Donald E. Voigt ◽

Leo E. Peters ◽

Sridhar Anandakrishnan

Keyword(s):

Seismic Data ◽

Climate Models ◽

Seismic Imaging ◽

Ice Sheets ◽

Weather Conditions ◽

Single Element ◽

Blowing Snow ◽

High Quality ◽

Field Campaigns ◽

Excellent Source

Abstract Active seismic imaging of glaciers and ice sheets is important for constraining inputs to climate models, such as englacial ice fabric and the nature of the basal interface. However, acquiring high-quality seismic data is time-consuming and resource-intensive. Using traditional single-element geophones requires ideal weather conditions (e.g. light winds) and excellent source coupling. In addition, deploying and retrieving these geophones is slow and cumbersome. We have developed a four-element ‘georod’ that enhances signal levels by 20–30dB in a variety of conditions, including blowing snow and poorly coupled source detonations. The long, slender design of these georods makes them easy to deploy and retrieve, allowing researchers to acquire greater line-kilometers of seismic data during field campaigns that are commonly time-constrained.

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In situ measurements of cloud microphysics and aerosol over coastal Antarctica during the MAC campaign

10.5194/acp-2017-212 ◽

2017 ◽

Cited By ~ 1

Author(s):

Sebastian J. O'Shea ◽

Thomas W. Choularton ◽

Michael Flynn ◽

Keith N. Bower ◽

Martin Gallagher ◽

...

Keyword(s):

Austral Summer ◽

Droplet Size Distribution ◽

Supercooled Liquid ◽

Cloud Microphysics ◽

In Situ Measurements ◽

Weddell Sea ◽

Blowing Snow ◽

Ice Production ◽

Surface Ice

Abstract. During austral summer 2015 the Microphysics of Antarctic Clouds (MAC) field campaign collected detailed airborne and ground based in situ measurements of cloud and aerosol properties over coastal Antarctica and the Weddell Sea. This paper presents the first results from the experiment and discusses the key processes important in this region. The sampling was predominantly of stratus cloud, at temperatures between −20 and 0 °C. These clouds were dominated by supercooled liquid water droplets, which had a median concentration of 113 cm−3 and an inter-quartile range of 86 cm−3. The concentration of large aerosols (0.5 to 1.6 μm) decreased with altitude and were depleted in airmasses that originated over the Antarctic Continent compared to those more heavily influenced by the Southern Ocean and sea ice regions. The dominant aerosol in the region was hygroscopic in nature, with the hygroscopicity parameter, κ having a median value for the campaign of 0.64 (interquartile range = 0.34). This is consistent with other remote marine locations that are dominated by sea salt/sulphate. Cloud ice particle concentrations were highly variable with the ice tending to occur in small isolated patches. Below ca 2000 m glaciated cloud regions were more common at higher temperatures; however the clouds were still predominantly liquid throughout. When ice was present at temperatures higher than −10 °C, secondary ice production most likely through the Hallet-Mossop mechanism lead to ice concentrations 1 to 3 orders of magnitude higher than the number predicted by commonly used primary ice nucleation parameterisations. The drivers of the ice crystal variability are investigated. No clear dependence on the droplet size distribution was found. However, higher ice concentrations were found in updrafts and downdrafts compared to quiescent regions. The source of first ice in the clouds remains uncertain, but may include contributions from biogenic particles, blowing snow or other surface ice production mechanisms.

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Measurements of precipitation in Dumont d'Urville, Terre Adélie, East Antarctica

10.5194/tc-2017-18 ◽

2017 ◽

Author(s):

Jacopo Grazioli ◽

Christophe Genthon ◽

Brice Boudevillain ◽

Claudio Duran-Alarcon ◽

Massimo Del Guasta ◽

...

Keyword(s):

Austral Summer ◽

Ground Level ◽

Blowing Snow ◽

Climatological Data ◽

Weather Forecasts ◽

Model Free ◽

First Results ◽

Terre Adélie ◽

Intensive Observation ◽

Polarimetric Weather Radar

Abstract. The first results of a campaign of intensive observation of precipitation in Dumont d'Urville, Antarctica, are presented. Several instruments collected data from October 2015, including a polarimetric weather radar (MXPol), a Micro Rain Radar (MRR), a weighing gauge (Pluvio2), and a Multi-Angle Snowflake Camera (MASC). These instruments collected the first model-free measurements of precipitation in the region in the region of Terre Adélie (Adélie Land), including of precipitation microphysics. Microphysical observations during the austral summer 2015/2016 showed that, close to ground level, aggregates are the dominant hydrometeor type, together with small ice particles (mostly originating from blowing snow), and that riming often occurs. Contamination of the Pluvio2 measurements in windy conditions is observed and partly removed through synergistic use of MRR data. The yearly accumulated precipitation of snow (300 m above ground), obtained by means of a local conversion relation of MRR data, trained on the Pluvio2 measurement of the summer period, is estimated to be 815 mm of water equivalent, with a confidence interval ranging between 739.5 to 989 mm. Climatological data obtained from satellite-borne radars, and the ERA-Interim reanalysis of the European Center for Medium-Range Weather Forecasts (ECMWF) both provide lower yearly totals: 655 mm for ERA-Interim, while 679 mm for the climatological data over DDU. ERA-Interim seems to overestimate the occurrence of low-intensity precipitation events especially in summer, while visual observations conducted at the research stations all year long seem to underestimate it. Overall, this manuscript provides insightful examples of the added values of precipitation monitoring in Antarctica with a synergistic use of in-situ and remote sensing measurements.

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Reaching for known unknowns: Rapid reach decisions accurately reflect the future state of dynamic probabilistic information

10.1101/2020.07.31.231563 ◽

2020 ◽

Author(s):

Nathan J. Wispinski ◽

Scott A. Stone ◽

Jennifer K. Bertrand ◽

Alexandra A. Ouellette Zuk ◽

Ewen B. Lavoie ◽

...

Keyword(s):

Reaction Times ◽

Movement Trajectories ◽

Probabilistic Information ◽

Complex Interactions ◽

System A ◽

Future State ◽

The Future ◽

The Central Nervous System ◽

Human Participants ◽

Known Unknowns

Everyday tasks such as catching a ball appear effortless, but in fact require complex interactions and tight temporal coordination between the brain’s visual and motor systems. What makes such interceptive actions particularly impressive is the capacity of the brain to account for temporal delays in the central nervous system—a limitation that can be mitigated by making predictions about the environment as well as one’s own actions. Here, we wanted to assess how well human participants can plan an upcoming movement based on a dynamic, predictable stimulus that is not the target of action. A central stationary or rotating stimulus determined the probability that each of two potential targets would be the eventual target of a rapid reach-to-touch movement. We examined the extent to which reach movement trajectories convey internal predictions about the future state of dynamic probabilistic information conveyed by the rotating stimulus. We show that movement trajectories reflect the target probabilities determined at movement onset, suggesting that humans rapidly and accurately integrate visuospatial predictions and estimates of their own reaction times to effectively guide action.

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Solid hydrometeor classification and riming degree estimation from pictures collected with a Multi-Angle Snowflake Camera

10.5194/amt-2016-417 ◽

2017 ◽

Author(s):

Christophe Praz ◽

Yves-Alain Roulet ◽

Alexis Berne

Keyword(s):

High Performance ◽

Multinomial Logistic Regression ◽

Skill Score ◽

Swiss Alps ◽

Second Step ◽

Probabilistic Information ◽

Heidke Skill Score ◽

Classification Tasks ◽

Type Classification ◽

Mlr Model

Abstract. A new method to automatically classify solid hydrometeors based on a Multi-Angle Snowflake Camera (MASC) images is presented. For each individual image, the method relies on the calculation of a set of geometric and texture-based descriptors to simultaneously identify the hydrometeor type (among six predefined classes), estimate the degree of riming and detect melting snow. The classification tasks are achieved by means of a regularized multinomial logistic regression (MLR) model trained over more than 3000 MASC images manually labeled by visual inspection. In a second step, the probabilistic information provided by the MLR is weighed on the three stereoscopic views of the MASC in order to assign a unique label to each hydrometeor. The accuracy and robustness of the proposed algorithm is evaluated on data collected in the Swiss Alps and in Antarctica. The algorithm achieves high performance, with a hydrometeor type classification accuracy and Heidke skill score of 95 % and 0.93, respectively. The degree of riming is evaluated by introducing a riming index ranging between zero (no riming) and one (graupel), and characterized by a probable error of 5.3 %. A validation study is conducted through a comparison with an existing classification method based on two-dimensional video disdrometer (2DVD) data and shows that the two methods are consistent.

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