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Tag: snow
  • Microbial Activity in Dust-Contaminated Antarctic Snow

    Abstract: During weather events, particles can accumulate on the snow near the Pegasus ice and Phoenix compacted-snow Runways at the US McMurdo Station in Antarctica. The deposited particles melt into the surface, initially forming steep-sided holes, which can widen into patches of weak and rotten snow and ice. These changes negatively impact the ice and snow runways and snow roads trafficked by vehicles. To understand the importance of microbes on this process, we examined deposited dust particles and their microbial communities in snow samples collected near the runways. Snow samples were analyzed at the Cold Regions Research and Engineering Laboratory where we performed a respiration study to measure the microbial activity during a simulated melt, isolated microorganisms, examined particle-size distribution, and performed 16S rRNA gene sequencing. We measured higher levels of carbon dioxide production from a sample containing more dust than from a sample containing less dust, a finding consistent with viable dust-associated microbial communities. Additionally, eleven microorganisms were isolated and cultured from snow samples containing dust particles. While wind patterns and satellite images suggest that the deposited particles originate from nearby Black Island, comparisons of the particle size and chemical composition were inconclusive.
  • Incorporating Advanced Snow Microphysics and Lateral Transport into the Noah-Multiparameterization (Noah-MP) Land Surface Model

    Abstract: The dynamic state of the land surface presents challenges and opportunities for military and civil operations in extreme cold environments. In particular, the effects of snow and frozen ground on Soldier and vehicle mobility are hard to overstate. Current authoritative weather and land models are run at global scales (i.e., dx > 10 km) and are of limited use at the Soldier scale (dx < 100 m). Here, we describe several snow physics upgrades made to the Noah-Multiparameterization (Noah-MP) community land surface model (LSM). These upgrades include a blowing snow overlay to simulate the lateral redistribution of snow by the wind and the addition of new prognostic snow microstructure variables, namely grain size and bond radius. These additions represent major upgrades to the snow component of the Noah-MP LSM because they incorporate processes and methods used in more specialized snow modeling frameworks. These upgrades are demonstrated in idealized and real-world applications. The test simulations were promising and show that the newly added snow physics replicate observed behavior with reasonable accuracy. We hope these upgrades facilitate ongoing and future research on characterizing the effects of the integrated snow and soil land surface in extreme cold environments at the tactical scale.
  • Extracting Sintered Snow Properties from MicroCT Imagery to Initialize a Discrete Element Method Model

    Abstract: Modeling snow’s mechanical behavior is important for many cold regions engineering problems. Because snow’s microstructure plays a significant role in its mechanical response, it is imperative to initialize models with accurate bond characteristics and realistic snow-grain geometries to precisely capture the microstructure interactions. Previous studies have processed microcomputed tomography scans of snow samples with a watershed method to extract grain geometries. This approach relies on identification of seed points to segment each grain. Our new methodology, called the “moving window method,” does not require prior knowledge of the snow-grain-size distribution to identify seed points. We use the interconnectivity of the segmented grains to identify bond characteristics. We compare the resultant grain-size and bond-size distributions to the known grain sizes of the laboratory-made snow samples. The grain-size distributions from the moving window method closely match the known grain sizes, while both results from the traditional method produce grains that are too large. We propose that the bond net-work identified using the traditional method underestimates the number of bonds and overestimates bond radii. Our method allows us to segment realistic snow grains and their associated bonds, without prior knowledge of the samples, from which we can initialize numerical models of the snow.
  • SAGE-PEDD Theory Manual: Modeling Windblown Snow Deposition around Buildings

    Abstract: Numerical modeling of snowdrifting is a useful tool for assessing the im-pact of building design on operations and facility maintenance. Here we outline the theory for the SAGE-PEDD snowdrift model that has applica-tion for determining snowdrift accumulation around buildings. This model uses the SAGE computational fluid dynamics code to determine the flow field in the computational domain. A particle entrainment, dis-persion, and deposition (PEDD) model is coupled to SAGE to simulate the movement and deposition of the snow within the computational do-main. The report also outlines areas of future development that upgrades to the SAGE-PEDD model should address.
  • Summary of Ground-Based Snow Measurements for the Northeastern United States

    ABSTRACT: Snow is an important resource for both communities and ecosystems of the Northeastern United States. Both flood risk management and water supply forecasts for major municipalities, including New York City, depend on the collection of snowpack information. Therefore, the purpose of this study is to summarize all of the snowpack data from ground-based networks currently available in the Northeast. The collection of snow-depth and snow water equivalent information extends back several decades, and there are over 2,200 active sites across the region. Sites are distributed across the entire range of elevations in the region. The number of locations collecting snow information has increased substantially in the last 20 years, primarily from the expansion of the CoCoRaHS (Community Collaborative Rain, Hail, and Snow) network. Our summary of regional snow measurement locations provides a foundation for future studies and analysis, including a template for other regions of the United States.
  • The Blowing Snow Hazard Assessment and Risk Prediction Model: A Python Based Downscaling and Risk Prediction for Snow Surface Erodibility and Probability

    Abstract: Blowing snow is an extreme terrain hazard causing intermittent severe reductions in ground visibility and snow drifting. These hazards pose significant risk to operations in snow-covered regions. While many ingredients-based forecasting methods can be employed to predict where blowing snow is likely to occur, there are currently no physically based tools to predict blowing snow from a weather forecast. However, there are several different process models that simulate the transport of snow over short distances that can be adapted into a terrain forecasting tool. This report documents a downscaling and blowing-snow prediction tool that leverages existing frameworks for snow erodibility, lateral snow transport, and visibility, and applies these frameworks for terrain prediction. This tool is designed to work with standard numerical weather model output and user-specified geographic models to generate spatially variable forecasts of snow erodibility, blowing snow probability, and deterministic blowing-snow visibility near the ground. Critically, this tool aims to account for the history of the snow surface as it relates to erodibility, which further refines the blowing-snow risk output. Qualitative evaluations of this tool suggest that it can provide more precise forecasts of blowing snow. Critically, this tool can aid in mission planning by downscaling high-resolution gridded weather forecast data using even higher resolution terrain dataset, to make physically based predictions of blowing snow.
  • Use of a Portable Friction Tester on Snow and Ice Pavement

    Abstract: The objective of this project was to determine if portable friction testers could be used for friction measurements on compacted snow and ice surfaces. First, the effect of cold temperatures on the operation, consistency, and accuracy of commercially available portable pavement friction measuring tools was evaluated. Tests entailed a series of experiments in a controlled cold room environment. Two portable fixed slip continuous measurement devices and one deceleration spot measurement device were evaluated. The controlled temperature testing determined how ambient temperature and duration of exposure can affect results, but that with care, the devices could be operated in conditions as cold as ˗25°C. This was followed by using one of the devices on outdoor testing on snow, ice, and asphalt surfaces and compared the portable tester to the well-known SAAB vehicle runway friction tester. Results showed good agreement between the portable tester and the SAAB Friction tester, providing validation for the operational use of a portable tester on frozen surfaces.
  • Assessing the Mechanisms Thought to Govern Ice and Snow Friction and Their Interplay with Substrate Brittle Behavior

    Abstract: Sliding friction on ice and snow is characteristically low at temperatures common on Earth’s surface. This slipperiness underlies efficient sleds, winter sports, and the need for specialized tires. Friction can also play micro-mechanical role affecting ice compressive and crushing strengths. Researchers have proposed several mechanisms thought to govern ice and snow friction, but directly validating the underlying mechanics has been difficult. This may be changing, as instruments capable of micro-scale measurements and imaging are now being brought to bear on friction studies. Nevertheless, given the broad regimes of practical interest (interaction length, temperature, speed, pressure, slider properties, etc.), it may be unrealistic to expect that a single mechanism accounts for why ice and snow are slippery. Because bulk ice, and the ice grains that constitute snow, are solids near their melting point at terrestrial temperatures, most research has focused on whether a lubricating water film forms at the interface with a slider. However, ice is extremely brittle, and dry-contact abrasion and wear at the front of sliders could prevent or delay a transition to lubricated contact. Also, water is a poor lubricant, and lubricating films thick enough to separate surface asperities may not form for many systems of interest. This article aims to assess our knowledge of the mechanics underlying ice and snow friction.
  • Imagery Classification for Autonomous Ground Vehicle Mobility in Cold Weather Environments

    Abstract: Autonomous ground vehicle (AGV) research for military applications is important for developing ways to remove soldiers from harm’s way. Current AGV research tends toward operations in warm climates and this leaves the vehicle at risk of failing in cold climates. To ensure AGVs can fulfill a military vehicle’s role of being able to operate on- or off-road in all conditions, consideration needs to be given to terrain of all types to inform the on-board machine learning algorithms. This research aims to correlate real-time vehicle performance data with snow and ice surfaces derived from multispectral imagery with the goal of aiding in the development of a truly all-terrain AGV. Using the image data that correlated most closely to vehicle performance the images were classified into terrain units of most interest to mobility. The best image classification results were obtained when using Short Wave InfraRed (SWIR) band values and a supervised classification scheme, resulting in over 95% accuracy.
  • Methodology for the Analysis of Geospatial and Vehicle Datasets in the R Language

    Abstract: The challenge of autonomous off-road operations necessitates a robust understanding of the relationships between remotely sensed terrain data and vehicle performance. The implementation of statistical analyses on large geospatial datasets often requires the transition between multiple software packages that may not be open-source. The lack of a single, modular, and open-source analysis environment can reduce the speed and reliability of an analysis due to an increased number of processing steps. Here we present the capabilities of a workflow, developed in R, to perform a series of spatial and statistical analyses on vehicle and terrain datasets to quantify the relationship between sensor data and vehicle performance in winter conditions. We implemented the R-based workflow on datasets from a large, coordinated field campaign aimed at quantifying the response of military vehicles on snow-covered terrains. This script greatly reduces processing times of these datasets by combining the GIS, data-assimilation and statistical analyses steps into one efficient and modular interface.