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Snowdrift

A snowdrift is an accumulation of wind-blown snow deposited in the lee of obstructions or heaped by wind eddies.^[1] These formations occur when strong winds transport loose snow particles through processes such as saltation, where particles bounce along the surface, and suspension, where finer particles are carried aloft.^[2] Snowdrifts play a significant role in shaping snow distribution in snowy regions, often forming around natural features like hills or human-made structures such as buildings and roads.^[3] Their development is influenced by factors including wind speed, direction, terrain roughness, and snowpack conditions, leading to uneven snow depths that can vary dramatically over short distances.^[4] In mountainous or polar areas, snowdrifts contribute to avalanche formation by loading slopes with excess snow in vulnerable spots.^[5] Beyond natural impacts, snowdrifts pose challenges for infrastructure and safety, accumulating on roadways to cause closures or on rooftops to impose structural loads.^[6] Mitigation strategies, such as installing porous snow fences, redirect drifting snow to controlled areas, reducing hazards in transportation corridors and urban environments.^[7] In Arctic and subarctic settings, understanding snowdrift patterns is crucial for engineering resilient structures against extreme winter conditions.^[8]

Definition and Formation

Basic Definition

A snowdrift is a mound or bank of snow sculpted and accumulated primarily through wind redistribution of existing snow particles, rather than direct fallout from precipitation, often forming against natural or artificial obstructions such as fences, buildings, or terrain features and resulting in depths significantly greater than the adjacent snow cover.^[9] This process contrasts with uniform snow accumulation, emphasizing wind as the dominant force in shaping localized deposits that can reach heights of several meters.^[10] Unlike a snowpack, which develops gradually from the settling and compaction of successive layers of fallen precipitation under gravity, a snowdrift arises from dynamic aeolian transport and deposition, creating irregular, wind-oriented formations rather than a broad, stratified layer.^[11] Snowdrifts also differ from snow dunes, which are ephemeral, ripple-like features sculpted by wind in expansive, unobstructed areas, akin to desert dunes but composed of snow and typically smaller in scale and duration.^[12] The term "snowdrift" originates from the compound of "snow" and "drift," with "drift" deriving from Old English drīfan, meaning "to drive" or "to push," reflecting the wind-driven movement of snow; its earliest recorded use dates to the Middle English period, before 1400.^[13] Fundamentally, snowdrifts require snow, composed of intricate ice crystals formed by the direct freezing of atmospheric water vapor into solid structures, and sustained winds to erode, transport, and redeposit these crystals into concentrated piles.^[14]

Mechanisms of Formation

Snowdrifts form primarily through the interaction between wind and snow particles on the surface, involving distinct transport processes that redistribute loose snow into accumulations. The key mechanisms include saltation, where larger snow grains (typically 0.1–1 mm in diameter) are lifted by wind shear or particle impacts and follow short ballistic trajectories near the surface, reaching heights of 10–15 cm before rebounding or depositing, and suspension, where finer particles (<0.1 mm) are carried aloft by turbulent air currents over longer distances without frequent surface contact. Saltation dominates the initial entrainment and near-surface transport, while suspension contributes to higher-altitude flux and broader redistribution, with the transition occurring as wind speeds increase and turbulence lifts particles beyond the saltation layer.^[15] Initiation of these processes requires wind speeds exceeding a threshold, typically 7–10 m s⁻¹ at 10 m height, corresponding to friction velocities of 0.15–0.25 m s⁻¹, beyond which aerodynamic forces overcome snow particle cohesion and gravity. This threshold varies by snow type: fresh, uncompacted snow has a lower threshold (around 7.5 m s⁻¹ at 10 m) due to weaker inter-particle bonds, while compacted or aged snow requires higher speeds (up to 9.9 m s⁻¹ or more) as sintering strengthens cohesion over time since snowfall. The snow transport rate, which quantifies the mass flux driving drift formation, can be approximated by the equation

Q = k u_*^3,

where Q is the horizontal mass transport rate (kg m⁻¹ s⁻¹), u_* is the friction velocity (m s⁻¹) above the threshold u_{*t}, and k is an empirical constant depending on snow properties (typically 0.001–0.01 for dry snow). This formulation accounts for the cubic dependence on friction velocity, reflecting increased particle entrainment and flux as shear stress rises.^[16]^[17]^[18]^[15] Turbulence plays a crucial role in drift development by generating eddies around surface obstacles, such as terrain features or structures, that reduce local wind speeds and create zones of convergence where suspended particles settle out of the flow. These turbulent structures, including recirculation zones downwind of obstacles, dissipate kinetic energy and promote preferential deposition, leading to snow accumulation in low-velocity areas while erosion occurs in high-shear regions upwind. The scale and intensity of these eddies depend on obstacle geometry and ambient wind, enhancing the spatial variability of drifts.^[19]^[20] Only certain snow types are prone to drifting, specifically loose, dry snow with low cohesion and density, such as fresh dendritic crystals formed shortly after snowfall in cold, low-humidity conditions. These feathery, branched structures (density ~50–100 kg m⁻³) exhibit minimal bonding, facilitating easy dislodgement by wind, whereas wet, rounded, or heavily compacted snow resists transport due to higher cohesion from liquid bridges or sintering. This selectivity ensures that drifts primarily form from recent, powdery accumulations rather than older, metamorphosed layers.^[18]^[16]

Physical Characteristics

Morphology and Structure

Snowdrifts display diverse morphologies influenced by wind patterns and local topography, often resembling aeolian dunes in their formation. In open, unobstructed areas, they commonly adopt barchan-like crescent shapes, termed snow barchans, where the curved form has horns pointing downwind due to preferential snow deposition on the leeward side. Against linear obstacles such as fences, walls, or rows of trees, snowdrifts typically form elongated linear banks, accumulating in a parallel alignment to the barrier.^[21] The spatial extent of snowdrifts varies significantly with storm intensity and duration. Vertically, they can reach heights of 5 to 10 meters in extreme conditions, while horizontally, they may extend for hundreds of meters along prevailing wind directions, particularly in flat terrains where wind transport processes allow sustained accumulation.^[12] Internally, snowdrifts exhibit stratified compositions with alternating layers of dense wind-packed snow and looser, faceted crystals resulting from successive wind events that compact surface layers while preserving underlying textures.^[12] At the base, scour features often develop from sublimation, eroding the snow-ground interface and creating irregular voids or thinned layers beneath the drift.^[12] Obstacles like buildings, trees, and terrain features play a critical role in dictating deposition sites by disrupting airflow and promoting snow capture. For instance, structures create low-pressure zones on leeward slopes or sides, leading to rapid buildup of drifts immediately downwind, while topographic depressions such as valleys or lake margins enhance trapping efficiency through reduced wind speeds.^[22] A notable case of extreme snowdrift formation occurred during the 1888 Children's Blizzard in the American Midwest, where intense winds accompanying heavy snowfall produced drifts up to about 5 meters high across prairies and near settlements, severely impeding travel and contributing to the storm's high toll.^[23]

Thermal and Density Properties

Snowdrifts exhibit density variations typically ranging from 100 to 400 kg/m³, with freshly formed drifts possessing lower densities due to abundant air pockets trapped during wind redistribution of snow particles.^[24] As time progresses, these densities increase through compaction processes driven by overlying snow weight and sustained wind action, which expels air and restructures the snowpack.^[25] This evolution can be modeled using an exponential function that describes the approach toward a maximum density limit:

\rho(t) = \rho_0 + (\rho_{\max} - \rho_0)(1 - e^{-kt})

where \rho(t) is the density at time t, \rho_0 is the initial density, \rho_{\max} is the asymptotic maximum density (often around 300-450 kg/m³ for compacted snow), and k is a rate constant influenced by factors such as wind speed and temperature.^[25] The thermal properties of snowdrifts contribute significantly to their role as insulators, with effective thermal conductivity values generally between 0.1 and 0.5 W/m·K, depending on density and structure.^[24] Lower-density drifts, rich in air pockets, exhibit conductivities closer to 0.1 W/m·K, enhancing insulation by trapping heat and minimizing conductive heat loss to the atmosphere. This insulation effect protects underlying soil from deep freezing, maintaining soil temperatures near or above 0°C even in subzero air conditions, which is crucial for preserving microbial activity and root systems in winter ecosystems.^[26] Density plays a critical role in the stability of snowdrifts on slopes, where higher densities from wind compaction can increase the risk of slab avalanches by creating denser, cohesive layers that overlay weaker, less dense basal snow.^[27] Such configurations promote shear failure at interfaces, as the added mass from dense drift accumulation amplifies gravitational stress on potential weak layers below.^[28] Measurement of snowdrift density and thermal properties relies on techniques such as manual snow probes, which insert into the drift to extract core samples for direct weighing and volume assessment, providing vertical profiles of density variation.^[29] Complementary remote sensing methods, including ground-penetrating radar (GPR) and airborne lidar, enable non-invasive mapping of density by combining snow depth data with estimated water equivalence or spectral reflectance correlations.^[30] These approaches allow for efficient profiling across larger areas, essential for assessing drift evolution and stability in varied terrains.^[31]

Environmental and Human Impacts

Effects on Wildlife and Ecosystems

Snowdrifts significantly alter habitats in tundra and alpine environments by providing protective cover for small mammals during winter, while simultaneously burying vegetation and limiting access to forage. In Arctic regions, species such as voles and lemmings utilize the subnivean tunnels beneath snowdrifts as insulated refuges, enabling survival in subzero air temperatures by maintaining stable, near-freezing conditions at the snow-soil interface.^[32]^[33] However, the accumulation of deep drifts can submerge grasses and shrubs, rendering winter forage inaccessible to herbivores unless they tunnel extensively, which increases energy expenditure and predation risk.^[34] The formation of snowdrifts creates distinct microclimates in subnival spaces, where temperatures remain warmer than ambient air due to the insulating properties of snow, fostering biological activity during harsh winters. These basal layers under drifts support enhanced microbial decomposition and nutrient cycling, as the stable thermal environment—often 0 to -5°C—prevents deep freezing and allows soil bacteria to remain metabolically active.^[35] Similarly, invertebrates such as springtails and mites thrive in these protected zones, contributing to detrital food webs that sustain higher trophic levels upon snowmelt.^[36] This insulation effect, derived from snow's low thermal conductivity, briefly underscores how drifts buffer ecosystems against extreme cold, promoting year-round subterranean biodiversity.^[37] Deep snowdrifts pose barriers to animal migration in tundra landscapes, particularly affecting larger herbivores and ground-nesting birds by obstructing traditional pathways. For caribou herds crossing Arctic tundra during spring and fall, accumulated drifts in low-lying areas force detours or increased physical effort to traverse, potentially delaying calving or exposing calves to predators.^[38] Ptarmigan, reliant on tundra routes for short-distance altitudinal migrations, face heightened energy costs when navigating or avoiding dense drifts, which can limit access to foraging grounds and nesting sites.^[34] In the long term, snowdrifts contribute to ecosystem resilience by enhancing soil moisture retention during spring melt, which supports vegetation regrowth in alpine areas. As drifts melt gradually, they release stored water into the soil profile, extending the period of adequate hydration for emergent plants like sedges and forbs, thereby boosting primary productivity in nutrient-poor environments.^[39] This hydrological input mitigates drought stress in early growing seasons, facilitating community recovery and maintaining biodiversity in high-elevation tundra.^[40] A notable example of snowdrift influence on predator-prey dynamics involves the Arctic fox, which exploits drifts for ambush hunting of lemmings active beneath the snowpack. By listening for subnival movements and pouncing through the surface, foxes use the drift's cover to surprise prey, achieving higher success rates in deep accumulations where lemmings seek refuge.^[41] This strategy highlights how drifts structure trophic interactions, linking small mammal populations to top predators in Arctic food webs.^[42]

Risks to Infrastructure and Safety

Snowdrifts pose significant risks to transportation infrastructure by accumulating rapidly in roadways, railways, and airports, leading to blockages that disrupt travel and commerce. During the Northeast Blizzard of 1978, fierce winds created drifts up to 15 feet high across Massachusetts and surrounding states, paralyzing highways and stranding thousands of motorists on interstates for days.^[43] Similarly, Interstate 75 in Ohio remained closed for three days due to deep snow accumulations, highlighting the potential for multi-day shutdowns.^[44] Rail lines are equally vulnerable; in the 1910 Wellington disaster in Washington's Cascade Mountains, blizzard-induced drifts blocked tracks, contributing to an avalanche that buried two trains and killed 96 people.^[45] The 1890 Great Sierra Snow Blockade in California saw repeated storms pile drifts over 10 feet deep, halting Central Pacific Railroad operations for weeks and requiring extensive plowing efforts.^[46] Structures, particularly buildings with flat or low-slope roofs, face collapse risks from uneven snowdrift loads that concentrate weight in specific areas. In Russia, including Siberian regions like Novosibirsk and Kemerovo, analysis of 266 roof collapse cases from 2001 to 2021 revealed that drifts often triggered failures at loads exceeding 1 kN/m² during intense snowfalls, with 13 cases in Novosibirsk alone.^[47] For instance, the 2021 collapse of a 37,000 m² pig farm roof in Primorsky Territory (near Siberia) occurred under short-term snow loads greater than 1 kN/m², resulting in structural failure without casualties.^[47] In the U.S., three-dimensional drifts on multilevel roofs have caused partial collapses in multiple incidents, as documented by FEMA, where drifts exceeded design capacities, emphasizing the need for drift-specific load considerations in building codes.^[48] On sloped terrain, snowdrifts formed by wind can create unstable wind slabs that trigger larger slab avalanches, posing threats to mountainous infrastructure and recreation areas. Wind-drifted snow accumulates on leeward slopes, forming cohesive slabs over weaker layers that, when overloaded, propagate cracks and release.^[49] Risk assessment often employs the North American Public Avalanche Danger Scale, a five-level system where considerable to high danger levels (3-4) indicate heightened instability from recent wind-drifted accumulations, advising avoidance of wind-loaded features.^[50] This scale integrates observations of slab formation to guide safe travel decisions in backcountry areas. Human safety is compromised by snowdrifts through disorientation in whiteout conditions and potential burial in avalanches or drifts. Whiteouts, caused by blowing snow from drifts, severely limit visibility, leading to spatial disorientation and increased hypothermia risk as exposed individuals lose body heat rapidly in subzero winds.^[51] In mountainous regions, burial under avalanche slabs initiated by drifts accounts for most fatalities, with asphyxia as the primary cause; the U.S. averages about 22 avalanche deaths annually, many involving full burial.^[52] Economically, snowdrifts contribute to substantial blizzard-related damages in the U.S., with annual insured losses from winter storms averaging about $5 billion from 2015 to 2024 (in 2024 dollars).^[53] From 1994 to 2013, winter events caused approximately $27 billion in total insured losses, partly attributable to drift-induced disruptions and collapses.^[54] More recently, the January 2025 U.S. blizzard produced significant snowdrifts across the High Plains and Midwest, leading to transportation disruptions and heightened safety risks.

Management and Mitigation

Engineering Techniques

Snow fences represent one of the most widely adopted engineering techniques for mitigating snowdrift formation around infrastructure such as roadways and buildings. These structures consist of porous barriers, typically constructed from wood, plastic, or metal slats supported by posts, with an optimal solidity of 50-60% (corresponding to 40-50% porosity) to maximize snow trapping efficiency while allowing sufficient airflow to prevent excessive upwind accumulation. Placed upwind of vulnerable areas, snow fences intercept blowing snow by reducing wind speed in their wake, causing particles to settle and form controlled drifts rather than uncontrolled ones near structures. According to guidelines from the National Cooperative Highway Research Program (NCHRP), a standard fence height of 3-4 meters, oriented perpendicular to prevailing winds, can trap 90-95% of incoming snow initially, with overall reductions in snow deposition on protected areas ranging from 70-90% over a season, depending on fence length and local snow transport rates.^[55] Berms and deflectors provide complementary methods to divert wind flow and minimize drift buildup, particularly in terrain where fences alone are impractical. Berms are earthen mounds or compacted snow/soil barriers, often 2-5 meters high with smoothed crests, designed to accelerate wind over their tops and deposit snow upwind while creating a snow-scoured zone downwind. Deflectors, such as V-shaped lateral barriers or aerodynamic wedges (e.g., 1-1.5 meters high and up to 15 times their height in width), redirect airflow to prevent deposition on roadsides or building bases. The Strategic Highway Research Program (SHRP) recommends placing berms 10 times their height upwind of infrastructure and deflectors immediately adjacent to cuts or fills, achieving drift reductions of up to 50-70% in targeted zones by inducing turbulence that lifts and disperses lighter snow particles. These techniques are especially effective in open prairies or mountain passes, where they integrate with road grading to maintain clear sightlines and reduce maintenance needs.^[56] Chemical treatments offer a targeted approach for high-traffic areas like airports, where rapid intervention is essential to prevent snowdrifts from disrupting operations. For runways, acetate-based deicers like potassium acetate or sodium formate are applied to prevent snow bonding and facilitate removal, reducing accumulation and drift risks. These are sprayed during snow events to lower the freezing point and create non-adherent surfaces, with effectiveness in minimizing closures reported by airport operators, though limited by environmental regulations on runoff.^[57] Effective implementation of these techniques relies on site-specific design principles, including assessment of fetch distance—the upwind expanse of snow-covered terrain that supplies drifting particles, often extending 1-6 kilometers in open areas. Placement decisions incorporate fetch calculations to determine required barrier heights and spacings, ensuring drifts are confined away from infrastructure; for instance, fences should be set back 18-35 times their height from roads to allow for equilibrium drift development without spillover. The American Society of Civil Engineers (ASCE) provides related guidelines in its standards for wind and snow load considerations, emphasizing integration with fetch-based modeling to optimize barrier configurations and avoid exacerbating drifts through improper alignment.^[56] A notable case study of these methods' effectiveness is along the Trans-Canada Highway in Rogers Pass, British Columbia, where severe winter conditions historically caused frequent drifts up to 5 meters deep, leading to closures. Implementation of snow fences, combined with berms and deflectors, along with snow nets in avalanche-prone sections, has improved traffic flow and safety by reducing closures and maintenance needs; for example, porous fences installed upwind of cuts trap excess snow, while earthen berms divert flows around the corridor, as part of integrated snow and avalanche management strategies documented by Parks Canada. This approach has minimized plowing needs and enhanced reliability, demonstrating the scalability of these techniques in mountainous environments.^[58]

Monitoring and Prediction Methods

Remote sensing technologies, such as LiDAR and drone-based surveys, enable precise three-dimensional mapping of snowdrift volumes by capturing high-resolution topographic data of snow-covered surfaces. LiDAR systems, often deployed via unmanned aerial vehicles (UAVs), measure snow depth by differencing pre- and post-snowfall digital elevation models, achieving vertical accuracies of approximately 10 cm in various terrains.^[59] Drone surveys complement this by providing flexible, on-demand coverage over targeted areas prone to drifting, such as open fields or roadways, facilitating the quantification of drift accumulation patterns with sub-meter horizontal resolution.^[60] Meteorological models integrate wind, precipitation, and terrain data to forecast snowdrift locations and intensities. The Weather Research and Forecasting (WRF) model, enhanced with drifting and blowing snow schemes, simulates snow transport by incorporating particle trajectory calculations and sublimation effects, allowing predictions of drift-prone zones during storm events.^[61] These simulations rely on high-resolution grids to resolve microscale wind flows around obstacles, improving forecast reliability for operational planning in regions like the Arctic or mountainous areas.^[62] Ground-based sensor networks provide essential real-time data for validating models and monitoring drift evolution. Anemometers measure wind speed and direction, critical for assessing erosion and deposition thresholds, while snow gauges quantify precipitation and accumulation rates at fixed sites.^[63] The NOAA Cooperative Observer Program operates over 11,000 stations across the U.S., as of 2025, where volunteers record daily snow depth and snowfall using standardized instruments, contributing to national datasets that inform drift risk assessments in windy, snowy locales.^[64] Empirical indices offer simplified tools for estimating snowdrift potential based on observable parameters. The Snow Drift Index (SDI), derived from SNOWPACK model calculations of mass fluxes including saltation and suspension components influenced by wind speed, evaluates the likelihood of significant drifting.^[65] This index, derived from field observations and modeling, helps prioritize monitoring in areas with long fetch lengths and moderate snow cover, though it assumes uniform terrain conditions.^[65] Recent advances in artificial intelligence have enhanced prediction capabilities, particularly through machine learning applied to satellite imagery. Post-2020 developments include models that process multispectral satellite data to detect blowing snow events and forecast drift formation in near real-time, achieving root mean square errors of 2-5 cm for depth predictions by training on historical meteorological and remote sensing archives.^[66] These AI-driven systems, such as XGBoost-based predictors, integrate variables like wind velocity and snow density—referencing typical drift densities of 200-400 kg/m³—to issue alerts for infrastructure protection, outperforming traditional physics-based models in complex terrains.^[67]

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Rating 5.0 (55) Oct 25, 2024 · Over the last 10 years, there have been 225 deaths from avalanches in the United States. That's an average of 22.5 deaths per year. Winter ...
[55]
The Cost Of Blizzards: $1.2 Billion A Year Since 1995
Jan 26, 2015 · In the past 20 years, winter storms have caused an average of $1.2 billion in insurable losses every year.
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The 10 costliest U.S. winter storms - CBS News
Feb 17, 2015 · And from 1994 to 2013, winter events were responsible for about $27 billion in insured losses, or over $1 billion in losses on average annually.
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[PDF] Controlling Blowing and Drifting Snow with Snow Fences and Road ...
In the United States, research on snow fences and drift control methods also began in the 1930s with F.A. Finney's wind tunnel experiments at Michigan State ...
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[PDF] Design Guidelines for the Control of Blowing and Drifting Snow
... wind speeds at anemometer height Z to 10-m. (33-ft) height. The threshold wind speed for blowing snow varies with snow conditions, elevation, and temperature ...<|separator|>
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Snow and Ice Control Chemicals for Airports Operations
Liquid deicers are generally used as "anti-icers." This means they are applied before frost, ice or snow accumulate and have a chance to bond to the pavement.
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[PDF] Ground Deicing Program - Federal Aviation Administration
Aug 2, 2023 · (3) Most FPD fluids are Ethylene Glycol (EG)-based or Propylene Glycol (PG)-based. Under precipitation conditions, chemical additives improve ...
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[PDF] Using Fixed Snow Nets to Mitigate Avalanche Risk to the Trans ...
Snow nets stabilize snow in avalanche start zones, are used for permanent defense, and are sized by snowpack thickness. They are also less obtrusive than rigid ...
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Snow Depth Retrieval with UAS Using Photogrammetric Techniques
Current research into the use of UAS as a 3D data-capture platform includes archaeological surveys, landslide deformation, glacier dynamics, and vegetation ...
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Snow depth mapping with unpiloted aerial system lidar observations
Mar 24, 2021 · Although the mean lidar snow depths were only 10.3 cm in the field and 6.0 cm in the forest, a pairwise Steel–Dwass test showed that snow depths ...
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A Drifting and Blowing Snow Scheme in the Weather Research and ...
Jun 11, 2024 · In this paper we introduced a novel module to calculate drifting snow within the framework of the Weather Research and Forecasting (WRF) model.
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Simulating snow drift in WRF - First results and future plans of a ...
We present a new framework to simulate snow drift in the Weather Research and Forecasting (WRF) model. Here, we show the basic structure of the module.
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Cooperative Weather Observer Program - Equipment Information
Using a snow stick they measure snowfall to the nearest tenth of an inch. They also will report any snow remaining on the ground (snow depth) to the nearest ...Missing: snowdrift | Show results with:snowdrift
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Cooperative Observer Network (COOP)
The National Weather Service (NWS) Cooperative Observer Program (COOP) is a network of daily weather observations taken by more than 8500 volunteers.Missing: anemometers snowdrift
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(PDF) A snowdrift index based on SNOWPACK model calculations
Aug 5, 2025 · The new snowdrift routine uses the modelled snow to determine a threshold friction velocity. The drift model describes the local mass flux of ...
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Leveraging advanced deep learning and machine learning ...
The Results highlight the efficacy of AI-based approaches for snow depth prediction, with SVR achieving the best performance (Root Mean Square Error of 2–5 cm ...
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Explainable machine learning for predictive modeling of blowing ...
In this study, XGBoost was employed to model the prediction of wind-blown snow events. XGBoost is an efficient machine learning algorithm based on Gradient ...