GTA Snow by CG v2

GTA Snow by CG v2

GTA Snow by CG v2

 

 

 

 

 

 

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Remote sensing of snowpacks with satellites and other platforms typically includes multi-spectral collection of imagery. Multi-faceted interpretation of the data obtained allows inferences about what is observed. The science behind these remote observations has been verified with ground-truth studies of the actual conditions.[1]

Satellite observations record a decrease in snow-covered areas since the 1960s, when satellites when satellite observations began. In some areas, including China, snow cover has increased. In some regions such as China, a trend of increasing snow cover has been observed from 1978 to 2006. These changes are attributed to global climate change, which may lead to earlier melting and less aea coverage. However, in some areas there may be an increase in snow depth because of higher temperatures for latitudes north of 40°. For the Northern Hemisphere as a whole the mean monthly snow-cover extent has been decreasing by 1.3% per decade.[59]

The most frequently used methods to map and measure snow extent, snow depth and snow water equivalent employ multiple inputs on the visible–infrared spectrum to deduce the presence and properties of snow. The National Snow and Ice Data Center (NSIDC) uses the reflectance of visible and infrared radiation to calculate a normalized difference snow index, which is a ratio of radiation parameters that can distinguish between clouds and snow. Other researchers have developed decision trees, employing the available data to make more accurate assessments. One challenge to this assessment is where snow cover is patchy, for example during periods of accumulation or ablation and also in forested areas. Cloud cover inhibits optical sensing of surface reflectance, which has led to other methods for estimating ground conditions underneath clouds. For hydrological models, it is important to have continuous information about the snow cover. Passive microwave sensors are especially valuable for temporal and spatial continuity because they can map the surface beneath clouds and in darkness. When combined with reflective measurements, passive microwave sensing greatly extends the inferences possible about the сновпацк
Snow science often leads to predictive models that include snow deposition, snow melt, and snow hydrology—elements of the Earth’s water cycle—which help describe global climate change.[1]

Global climate change models (GCMs) incorporate snow as a factor in their calculations. Some important aspects of snow cover include its albedo (reflectivity of incident radiation, including light) and insulating qualities, which slow the rate of seasonal melting of sea ice. As of 2011, the melt phase of GCM snow models were thought to perform poorly in regions with complex factors that regulate snow melt, such as vegetation cover and terrain. These models typically derive snow water equivalent (SWE) in some manner from satellite observations of snow cover.[1] The International Classification for Seasonal Snow on the Ground defines SWE as “the depth of water that would result if the mass of snow melted completely”.[4]

Given the importance of snowmelt to agriculture, hydrological runoff models that include snow in their predictions address the phases of accumulating snowpack, melting processes, and distribution of the meltwater through stream networks and into the groundwater. Key to describing the melting processes are solar heat flux, ambient temperature, wind, and precipitation. Initial snowmelt models used a degree-day approach that emphasized the temperature difference between the air and the snowpack to compute snow water equivalent, SWE. More recent models use an energy balance approach that take into account the following factors to compute Qm, the energy available for melt. This requires measurement of an array of snowpack and environmental factors to compute six heat flow mechanisms that contribute to Qm.[1]

Effects on human activity
Snow affects human activity in four major areas, transportation, agriculture, structures, and sports. Most transportation modes are impeded by snow on the travel surface. Agriculture often relies on snow as a source of seasonal moisture. Structures may fail under snow loads. Humans find a wide variety of recreational activities in snowy landscapes.

Transportation
See also: Snowplow
Snow affects the rights of way of highways, airfields and railroads. They share a common tool for clearing snow, the snowplow. However, the application is different in each case—whereas roadways employ anti-icing chemicals to prevent bonding of ice, airfields may not; railroads rely on abrasives to enhance traction on tracks.

In the late 20th Century, an estimated $2 billion was spent annually in North America on roadway winter maintenance, owing to snow and other winter weather events, according to a 1994 report by Kuemmel. The study surveyed the practices of jurisdictions within 44 US states and nine Canadian provinces. It assessed the policies, pra

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