scholarly journals DEVELOPMENT OF NEW SNOWDRIFT MODEL BASED ON TWO TRANSPORT EQUATIONS OF DRIFTING SNOW DENSITY

2013 ◽  
Vol 78 (684) ◽  
pp. 149-156 ◽  
Author(s):  
Tsubasa OKAZE ◽  
Yoshihide TOMINAGA ◽  
Akashi MOCHIDA
Keyword(s):  
Transport Equations ◽  
Snow Density ◽  
Model Based ◽  
Drifting Snow

scholarly journals A transient backward erosion piping model based on laminar flow transport equations

2021 ◽  
Vol 132 ◽  
pp. 103992
Author(s):  
Manuel Wewer ◽  
Juan Pablo Aguilar-López ◽  
Matthijs Kok ◽  
Thom Bogaard
Keyword(s):  
Laminar Flow ◽  
Transport Equations ◽  
Model Based ◽  
Flow Transport ◽  
Backward Erosion Piping

SWE modelling: comparison between different approaches applied to Germany

2021 ◽  
Author(s):  
Fabiana Castino ◽  
Bodo Wichura ◽  
Harald Schellander ◽  
Michael Winkler
Keyword(s):  
Time Series ◽  
Snow Depth ◽  
Cross Validation ◽  
Snow Water Equivalent ◽  
Snow Density ◽  
Design Standards ◽  
Model Based ◽  
Meteorological Observations ◽  
Snow Model ◽  
Leave One Out

<p>The characterization of the snow cover by snow water equivalent (SWE) is fundamental in several environmental applications, e.g., monitoring mountain water resources or defining structural design standards. However, SWE observations are usually rare compared to other snow measurements as snow depth (HS). Therefore, model-based methods have been proposed in past studies for estimating SWE, in particular for short timescales (e.g., daily). In this study, we compare two different approaches for SWE-data modelling. The first approach, based on empirical regression models (ERMs), provides the regional parametrization of the bulk snow density, which can be used to estimate SWE values from HS. In particular, we investigate the performances of four different schemes based on previously developed ERMs of bulk snow density depending on HS, date, elevation, and location. Secondly, we apply the semi-empirical multi-layer Δsnow model, which estimates SWE solely based on snow depth observations. The open source Δsnow model has been recently used for deriving a snow load map for Austria, resulting in an improved Austrian standard. A large dataset of HS and SWE observations collected by the National Weather Service in Germany (DWD) is used for calibrating and validating the models. This dataset consists of daily HS and three-times-a-week SWE observations from in total ~1000 stations operated by DWD over the period from 1950 to 2020. A leave-one-out cross validation is applied to evaluate the performance of the different model approaches. It is based on 185 time series of HS and SWE observations that are representative of the diversity of the regional snow climatology of Germany. Cross validation reveals for all ERMs: 90% of the modelled SWE time series have a root mean square error (RMSE) and a bias lower than 45 kg/m² and 2 kg/m², respectively. The Δsnow model shows the best performance with 90% of the modelled SWE time series having an RMSE lower than 30 kg/m² and bias similar to the ERMs. This comparative study provides new insights on the reliability of model-based methods for estimating SWE values. The results show that the Δsnow model and, to a lower degree, the developed ERMs can provide satisfactory performances even on short timescales. This suggest that these models can be used as reliable alternative to more complex thermodynamic snow models, even more if long-term meteorological observations aside HS are scarce.</p>

Observation Test on the Physical Properties of Accumulated Snow in Constructing Embankment

2010 ◽  
Vol 108-111 ◽  
pp. 380-385
Author(s):  
Shou Yun Liang ◽  
Xiang Xian Ma ◽  
Xiang Yang Wang
Keyword(s):  
Physical Properties ◽  
Air Temperature ◽  
Great Influence ◽  
Weather Conditions ◽  
Snow Density ◽  
Engineering Construction ◽  
Topographic Features ◽  
Drifting Snow ◽  
In Situ Experiments

Drifting snow is one of the main disasters in blocking railways and road traffics in cold areas. It has been becoming a key issue to reduce hazards from drifting snow in the engineering constructions in cold areas. The physical properties of snowfall and drifting snow, together with the transformation over terrain by engineering construction, give significant influence on the formation and growth of drifting snow. The accumulated snow distributing on high embankment transects built in the Jinghe-Yining Railway (JYR) has been chosen as the main object of this research, and a field observation of weather conditions over this area with in-situ experiments of the physical properties of snow and has been reported. All results indicated: the snow density and hardness were generally changed with the similar trending of air temperature variation, and both snow density and hardness increased with the duration of snow accumulated. However, the change of snow density and hardness appeared much more complicated variations than that of the air temperature. The snow depth and pressure, as well as the density and hardness were complicatedly distributed on both sides of the embankment transect, either roughly symmetrical or significantly different. We concluded that a great influence on the physical properties of snow had been exerted by many factors such as the topographic features, the redistribution of snowfall brought by engineering constructions, the weather conditions during snow accumulating etc.

scholarly journals Modelling Small-Scale Drifting Snow with a Lagrangian Stochastic Model Based on Large-Eddy Simulations

2014 ◽  
Vol 153 (1) ◽  
pp. 117-139 ◽  
Author(s):  
C. D. Groot Zwaaftink ◽  
M. Diebold ◽  
S. Horender ◽  
J. Overney ◽  
G. Lieberherr ◽  
...  
Keyword(s):  
Stochastic Model ◽  
Large Eddy Simulations ◽  
Small Scale ◽  
Lagrangian Stochastic Model ◽  
Model Based ◽  
Drifting Snow ◽  
Large Eddy

New generalized model based on Onsager's transport equations for describing PDLC's morphology

2004 ◽  
Author(s):  
Vincent Rachet ◽  
Patrick Feneyrou ◽  
Pierre L. Le Barny ◽  
Brigitte Loiseaux ◽  
Jean-Pierre Huignard
Keyword(s):  
Transport Equations ◽  
Generalized Model ◽  
Model Based

scholarly journals A seamless hybrid RANS-LES model based on transport equations for the subgrid stresses and elliptic blending

2010 ◽  
Vol 22 (5) ◽  
pp. 055104 ◽  
Author(s):  
Atabak Fadai-Ghotbi ◽  
Christophe Friess ◽  
Rémi Manceau ◽  
Jacques Borée
Keyword(s):  
Transport Equations ◽  
Model Based ◽  
Subgrid Stresses ◽  
Les Model

Representation, abstraction, and simple-minded sophisticates

2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan
Keyword(s):  
Decision Theory ◽  
Predictive Coding ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Model Based ◽  
Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

Quantitative Model-Based Image Analysis of NuMa Distribution Links Nuclear Organization with Cell Phenotype

2001 ◽  
Vol 7 (S2) ◽  
pp. 578-579
Author(s):  
David W. Knowles ◽  
Sophie A. Lelièvre ◽  
Carlos Ortiz de Solόrzano ◽  
Stephen J. Lockett ◽  
Mina J. Bissell ◽  
...  
Keyword(s):  
Image Analysis ◽  
Mammary Epithelial Cells ◽  
Nuclear Organization ◽  
Critical Role ◽  
Quantitative Model ◽  
Mammary Epithelial ◽  
Cell Phenotype ◽  
Proliferating Cells ◽  
Mitotic Apparatus ◽  
Model Based

The extracellular matrix (ECM) plays a critical role in directing cell behaviour and morphogenesis by regulating gene expression and nuclear organization. Using non-malignant (S1) human mammary epithelial cells (HMECs), it was previously shown that ECM-induced morphogenesis is accompanied by the redistribution of nuclear mitotic apparatus (NuMA) protein from a diffuse pattern in proliferating cells, to a multi-focal pattern as HMECs growth arrested and completed morphogenesis . A process taking 10 to 14 days.To further investigate the link between NuMA distribution and the growth stage of HMECs, we have investigated the distribution of NuMA in non-malignant S1 cells and their malignant, T4, counter-part using a novel model-based image analysis technique. This technique, based on a multi-scale Gaussian blur analysis (Figure 1), quantifies the size of punctate features in an image. Cells were cultured in the presence and absence of a reconstituted basement membrane (rBM) and imaged in 3D using confocal microscopy, for fluorescently labeled monoclonal antibodies to NuMA (fαNuMA) and fluorescently labeled total DNA.

Model-Based Clustering and Classification for Data Science

2019 ◽  
Author(s):  
Charles Bouveyron ◽  
Gilles Celeux ◽  
T. Brendan Murphy ◽  
Adrian E. Raftery
Keyword(s):  
Data Science ◽  
Model Based Clustering ◽  
Model Based ◽  
Clustering And Classification
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