scholarly journals Enhancing the operational value of snowpack models with visualization design principles

2020 ◽  
Vol 20 (6) ◽  
pp. 1557-1572
Author(s):  
Simon Horton ◽  
Stan Nowak ◽  
Pascal Haegeli
Keyword(s):  
Spatial Scales ◽  
Critical Role ◽  
Design Principles ◽  
Physical Models ◽  
Model Data ◽  
User Testing ◽  
Avalanche Forecasting ◽  
Cognitive Principles ◽  
Visualization Tools ◽  
Visualization Design

Abstract. Forecasting snow avalanches requires a reliable stream of field observations, which are often difficult and expensive to collect. Despite the increasing capability of simulating snowpack conditions with physical models, models have seen limited adoption by avalanche forecasters. Feedback from forecasters suggests that model data are presented in ways that are difficult to interpret and irrelevant to operational needs. We apply a visualization design framework to enhance the value of snowpack models to avalanche forecasters. An established risk-based avalanche forecasting workflow is used to define the ways forecasters solve problems with snowpack data. We suggest that model data be visualized in ways that directly support common forecasting tasks such as identifying snowpack features related to avalanche problems and locating avalanche problems in terrain at relevant spatial scales. Examples of visualizations that support these tasks and follow established perceptual and cognitive principles from the field of information visualization are presented. Interactive designs play a critical role in understanding these complex datasets and are well suited for forecasting workflows. Although extensive user testing is still needed to evaluate the effectiveness of these designs, visualization design principles open the door to more relevant and interpretable applications of snowpack model for avalanche forecasters. This work sets the stage for implementing snowpack models into visualization tools where forecasters can test their operational value and learn their capabilities and deficiencies.

scholarly journals Enhancing the operational value of snowpack models with visualization design principles

2019 ◽  
Author(s):  
Simon Horton ◽  
Stan Nowak ◽  
Pascal Haegeli
Keyword(s):  
Information Visualization ◽  
Critical Role ◽  
Design Principles ◽  
Physical Models ◽  
Snow Avalanches ◽  
Model Data ◽  
Avalanche Forecasting ◽  
Operational Testing ◽  
Cognitive Principles ◽  
Visualization Design

Abstract. Forecasting snow avalanches requires a reliable stream of field observations, which are often difficult and expensive to collect. Despite the increasing capability of simulating snowpack conditions with physical models, models have seen limited adoption by avalanche forecasters. Feedback from forecasters suggest model data is presented in ways that are difficult to interpret and irrelevant to operational needs. We apply a visualization design framework to enhance the value of snowpack models to avalanche forecasters. An established risk-based workflow for avalanche forecasting is used to define the ways forecasters solve problems with snowpack data. We address common forecasting tasks such as identifying snowpack features related to avalanche problems, summarizing snowpack features within a forecast area, and locating problems in terrain. Examples of visualizations that support these tasks are presented and follow established perceptual and cognitive principles from the field of information visualization. Interactive designs play a critical role in understanding these complex datasets and are well suited for forecasting workflows. Preliminary feedback suggests these design principles produce visualizations that are more relevant and interpretable for avalanche forecasters, but additional operational testing is needed to evaluate their effectiveness. By addressing issues with interpretability and relevance, this work sets the stage for implementing snowpack models into workstations where forecasters can test their operational value and learn their capabilities and deficiencies.

Machine learning analyses of remote sensing measurements establish strong relationships between vegetation and snow depth in the boreal forest of Interior Alaska

2021 ◽  
Author(s):  
Thomas Douglas ◽  
Caiyun Zhang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Snow Depth ◽  
Spatial Scales ◽  
Critical Role ◽  
Winter Season ◽  
Thermal Conditions ◽  
Near Surface ◽  
Lidar Measurements ◽  
Remote Sensing Methods

The seasonal snowpack plays a critical role in Arctic and boreal hydrologic and ecologic processes. Though snow depth can be different from one season to another there are repeated relationships between ecotype and snowpack depth. Alterations to the seasonal snowpack, which plays a critical role in regulating wintertime soil thermal conditions, have major ramifications for near-surface permafrost. Therefore, relationships between vegetation and snowpack depth are critical for identifying how present and projected future changes in winter season processes or land cover will affect permafrost. Vegetation and snow cover areal extent can be assessed rapidly over large spatial scales with remote sensing methods, however, measuring snow depth remotely has proven difficult. This makes snow depth–vegetation relationships a potential means of assessing snowpack characteristics. In this study, we combined airborne hyperspectral and LiDAR data with machine learning methods to characterize relationships between ecotype and the end of winter snowpack depth. Our results show hyperspectral measurements account for two thirds or more of the variance in the relationship between ecotype and snow depth. An ensemble analysis of model outputs using hyperspectral and LiDAR measurements yields the strongest relationships between ecotype and snow depth. Our results can be applied across the boreal biome to model the coupling effects between vegetation and snowpack depth.

scholarly journals The electrical self-potential method is a non-intrusive snow-hydrological sensor

2015 ◽  
Vol 9 (4) ◽  
pp. 4437-4457 ◽  
Author(s):  
S. S. Thompson ◽  
B. Kulessa ◽  
R. L. H. Essery ◽  
M. P. Lüthi
Keyword(s):  
Spatial Scales ◽  
Chemical Properties ◽  
Potential Method ◽  
Measurement Procedure ◽  
Theoretical Modelling ◽  
Avalanche Forecasting ◽  
Physical And Chemical ◽  
Self Potential ◽  
Water Contents

Abstract. Our ability to measure, quantify and assimilate hydrological properties and processes of snow in operational models is disproportionally poor compared to the significance of seasonal snowmelt as a global water resource and major risk factor in flood and avalanche forecasting. Encouraged by recent theoretical, modelling and laboratory work, we show here that the diurnal evolution of aerially-distributed self-potential magnitudes closely track those of bulk meltwater fluxes in melting in-situ snowpacks at Rhone and Jungfraujoch glaciers, Switzerland. Numerical modelling infers temporally-evolving liquid water contents in the snowpacks on successive days in close agreement with snow-pit measurements. Muting previous concerns, the governing physical and chemical properties of snow and meltwater became temporally invariant for modelling purposes. Because measurement procedure is straightforward and readily automated for continuous monitoring over significant spatial scales, we conclude that the self-potential geophysical method is a highly-promising non-intrusive snow-hydrological sensor for measurement practice, modelling and operational snow forecasting.

scholarly journals Designing Visualizations For Biological Data

Leonardo ◽  
2013 ◽  
Vol 46 (3) ◽  
pp. 270-271 ◽  
Author(s):  
Miriah Meyer
Keyword(s):  
Biological Data ◽  
Discovery Process ◽  
Design Studies ◽  
Biological Discovery ◽  
Vital Component ◽  
Visualization Tools ◽  
Visualization Design ◽  
Effective Visualization

Visualization is now a vital component of the biological discovery process. This article presents visualization design studies as a promising approach for creating effective, visualization tools for biological data.

scholarly journals Three-dimensional numerical assessment of axial and torsional interface shear behaviour

2019 ◽  
Vol 92 ◽  
pp. 13016
Author(s):  
Jiaxing Su ◽  
David Frost ◽  
Alejandro Martínez
Keyword(s):  
Wide Spectrum ◽  
Spatial Scales ◽  
Critical Role ◽  
Three Dimensional ◽  
Shear Behaviour ◽  
Discrete Element Modelling ◽  
Interface Shear ◽  
Model Studies ◽  
Soil Site ◽  
The Difference

Interfaces between geo-materials and soils play a critical role in a wide spectrum of geotechnical structures and soil/site characterization techniques in geotechnical engineering. Consequently, understanding the mechanics of interface shear behaviour at different scales can benefit both soil characterization and the design of geotechnical systems. This paper presents a series of numerical simulations that utilize the 3D discrete element modelling (DEM) technique and compares the results with those obtained from laboratory counterpart tests under axial and torsional axisymmetric interface shear. The difference observed in macro- and meso-scale responses under these loading conditions, such as shear strength, volumetric change, and shear zone characteristics are evaluated. In addition, responses at microscale including particle displacement trajectory, particles rotation, and local void ratio evolution are assessed allowing for links to the results obtained at larger spatial scales. These 3D numerical model studies expand the micromechanical processes under different shearing conditions previously presented by the authors from 2D to 3D space.

Imaging Morphogenesis: Technological Advances and Biological Insights

Science ◽  
2013 ◽  
Vol 340 (6137) ◽  
pp. 1234168 ◽  
Author(s):  
Philipp J. Keller
Keyword(s):  
Structural Changes ◽  
Imaging Techniques ◽  
Spatial Scales ◽  
Image Data ◽  
Physical Models ◽  
Data Sets ◽  
Full Potential ◽  
Technological Advances ◽  
Complex Image ◽  
Underlying Mechanisms

Morphogenesis, the development of the shape of an organism, is a dynamic process on a multitude of scales, from fast subcellular rearrangements and cell movements to slow structural changes at the whole-organism level. Live-imaging approaches based on light microscopy reveal the intricate dynamics of this process and are thus indispensable for investigating the underlying mechanisms. This Review discusses emerging imaging techniques that can record morphogenesis at temporal scales from seconds to days and at spatial scales from hundreds of nanometers to several millimeters. To unlock their full potential, these methods need to be matched with new computational approaches and physical models that help convert highly complex image data sets into biological insights.

scholarly journals Regional Climate Models Add Value to Global Model Data: A Review and Selected Examples

2011 ◽  
Vol 92 (9) ◽  
pp. 1181-1192 ◽  
Author(s):  
Frauke Feser ◽  
Burkhardt Rockel ◽  
Hans von Storch ◽  
Jörg Winterfeldt ◽  
Matthias Zahn
Keyword(s):  
Regional Climate Model ◽  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Spatial Scales ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Small Scale ◽  
Model Data ◽  
Polar Lows

An important challenge in current climate modeling is to realistically describe small-scale weather statistics, such as topographic precipitation and coastal wind patterns, or regional phenomena like polar lows. Global climate models simulate atmospheric processes with increasingly higher resolutions, but still regional climate models have a lot of advantages. They consume less computation time because of their limited simulation area and thereby allow for higher resolution both in time and space as well as for longer integration times. Regional climate models can be used for dynamical down-scaling purposes because their output data can be processed to produce higher resolved atmospheric fields, allowing the representation of small-scale processes and a more detailed description of physiographic details (such as mountain ranges, coastal zones, and details of soil properties). However, does higher resolution add value when compared to global model results? Most studies implicitly assume that dynamical downscaling leads to output fields that are superior to the driving global data, but little work has been carried out to substantiate these expectations. Here a series of articles is reviewed that evaluate the benefit of dynamical downscaling by explicitly comparing results of global and regional climate model data to the observations. These studies show that the regional climate model generally performs better for the medium spatial scales, but not always for the larger spatial scales. Regional models can add value, but only for certain variables and locations—particularly those influenced by regional specifics, such as coasts, or mesoscale dynamics, such as polar lows. Therefore, the decision of whether a regional climate model simulation is required depends crucially on the scientific question being addressed.

scholarly journals Forecasting global atmospheric CO<sub>2</sub>

2014 ◽  
Vol 14 (9) ◽  
pp. 13909-13962 ◽  
Author(s):  
A. Agustí-Panareda ◽  
S. Massart ◽  
F. Chevallier ◽  
S. Boussetta ◽  
G. Balsamo ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Real Time ◽  
Diurnal Cycle ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Physical Models ◽  
Atmospheric Co2 ◽  
Co2 Fluxes ◽  
Near Surface ◽  
Forecasting System

Abstract. A new global atmospheric carbon dioxide (CO2) real-time forecast is now available as part of the pre-operational Monitoring of Atmospheric Composition and Climate – Interim Implementation (MACC-II) service using the infrastructure of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). One of the strengths of the CO2 forecasting system is that the land surface, including vegetation CO2 fluxes, is modelled online within the IFS. Other CO2 fluxes are prescribed from inventories and from off-line statistical and physical models. The CO2 forecast also benefits from the transport modelling from a state-of-the-art numerical weather prediction (NWP) system initialized daily with a wealth of meteorological observations. This paper describes the capability of the forecast in modelling the variability of CO2 on different temporal and spatial scales compared to observations. The modulation of the amplitude of the CO2 diurnal cycle by near-surface winds and boundary layer height is generally well represented in the forecast. The CO2 forecast also has high skill in simulating day-to-day synoptic variability. In the atmospheric boundary layer, this skill is significantly enhanced by modelling the day-to-day variability of the CO2 fluxes from vegetation compared to using equivalent monthly mean fluxes with a diurnal cycle. However, biases in the modelled CO2 fluxes also lead to accumulating errors in the CO2 forecast. These biases vary with season with an underestimation of the amplitude of the seasonal cycle both for the CO2 fluxes compared to total optimized fluxes and the atmospheric CO2 compared to observations. The largest biases in the atmospheric CO2 forecast are found in spring, corresponding to the onset of the growing season in the Northern Hemisphere. In the future, the forecast will be re-initialized regularly with atmospheric CO2 analyses based on the assimilation of CO2 satellite retrievals, as they become available in near-real time. In this way, the accumulation of errors in the atmospheric CO2 forecast will be reduced. Improvements in the CO2 forecast are also expected with the continuous developments in the operational IFS.

scholarly journals Will a perfect model agree with perfect observations? The impact of spatial sampling

2016 ◽  
Author(s):  
N. A. J. Schutgens ◽  
E. Gryspeerdt ◽  
N. Weigum ◽  
S. Tsyro ◽  
D. Goto ◽  
...  
Keyword(s):  
Black Carbon ◽  
Measurement Errors ◽  
Spatial Scales ◽  
Global Model ◽  
Spatial Sampling ◽  
Model Data ◽  
Temporal Sampling ◽  
Fold Reduction ◽  
Perfect Model ◽  
The Impact

Abstract. The spatial resolution of global climate models with interactive aerosol and the observations used to evaluate them is very different. Current models use grid-spacings of ∼ 200 km, while satellite observations of aerosol use so-called pixels of ∼ 10 km. Ground site or air-borne observations concern even smaller spatial scales. We study the errors incurred due to different resolutions by aggregating high-resolution simulations (10 km grid-spacing) over either the large areas of global model grid-boxes ("perfect" model data) or small areas corresponding to the pixels of satellite measurements or the field-of-view of ground-sites ("perfect" observations). Our analysis suggests that instantaneous RMS differences between these perfect observations and perfect global models can easily amount to 30–160%, for a range of observables like AOT (Aerosol Optical Thickness), extinction, black carbon mass concentrations, PM2.5, number densities and CCN (Cloud Condensation Nuclei). These differences, due entirely to different spatial sampling of models and observations, are often larger than measurement errors in real observations. Temporal averaging over a month of data reduces these differences more strongly for some observables (e.g. a three-fold reduction i.c. AOT), than for others (e.g. a two-fold reduction for surface black carbon concentrations), but significant RMS differences remain (10-75%). Note that this study ignores the issue of temporal sampling of real observations, which is likely to affect our present monthly error estimates. We examine several other strategies (e.g. spatial aggregation of observations, interpolation of model data) for reducing these differences and show their effectiveness. Finally, we examine consequences for the use of flight campaign data in global model evaluation and show that significant biases may be introduced depending on the flight strategy used.

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