Snow science
Snow science addresses how snow forms, its distribution, and processes affecting how snowpacks change over time. Scientists improve storm forecasting, study global snow cover and its effect on climate, glaciers, and water supplies around the world. The study includes physical properties of the material as it changes, bulk properties of in-place snow packs, and the aggregate properties of regions with snow cover. In doing so, they employ on-the-ground physical measurement techniques to establish ground truth and remote sensing techniques to develop understanding of snow-related processes over large areas. History. Snow was described in China , as early as 135 BCE in Han Ying's book "Disconnection", which contrasted the pentagonal symmetry of flowers with the hexagonal symmetry of snow. Albertus Magnus proved what may be the earliest detailed European description of snow in 1250. Johannes Kepler attempted to explain why snow crystals are hexagonal in his 1611 book, "Strena seu De Nive Sexangula". In 1675 Friedrich Martens, a German physician, catalogued 24 types of snow crystal. In 1865, Frances E. Chickering published "Cloud Crystals - a Snow-Flake Album". In 1894, A. A. Sigson photographed snowflakes under a microscope, preceding Wilson Bentley's series of photographs  of individual snowflakes in the "Monthly Weather Review". Ukichiro Nakaya began an extensive study on snowflakes in 1932. From 1936 to 1949, Nakaya created the first artificial snow crystals and charted the relationship between temperature and water vapor saturation, later called the "Nakaya Diagram" and other works of research in snow, which were published in 1954 by Harvard University Press publishes as "Snow Crystals: Natural and Artificial". Teisaku Kobayashi, verified and improves the "Nakaya Diagram" with the 1960 "Kobayashi Diagram", later refined in 1962. Further interest in artificial snowflake genesis continued in 1982 with Toshio Kuroda and Rolf Lacmann, of  the Braunschweig University of Technology, publishing "Growth Kinetics of Ice from the Vapour Phase and its Growth Forms". In August 1983, Astronauts synthesized snow crystals in orbit on the Space Shuttle Challenger during mission STS-8. By 1988 Norihiko Fukuta et al. confirmed the "Nakaya Diagram" with artificial snow crystals, made in an updraft and Yoshinori Furukawa demonstrated snow crystal growth in space. Measurement. Snow scientists typically excavate a snow pit within which to make basic measurements and observations. Observations can describe features caused by wind, water percolation, or snow unloading from trees. Water percolation into a snowpack can create flow  fingers and ponding or flow along capillary barriers, which can refreeze into horizontal and vertical solid ice formations within the snowpack. Among the measurements of the properties of snowpacks (together with their codes) that the "International Classification for Seasonal Snow on the Ground" presents are: Instruments. "Depth" – Depth of snow is measured with a snowboard (typically a piece of plywood painted white) observed during a six-hour period. At the end of the six-hour period, all snow is cleared from the measuring surface. For a daily total snowfall, four six-hour snowfall measurements are summed. Snowfall can be very  difficult to measure due to melting, compacting, blowing and drifting. "Liquid equivalent by snow gauge" – The liquid equivalent of snowfall may be evaluated using a snow gauge or with a standard rain gauge having a diameter of 100 mm (4 in; plastic) or 200 mm (8 in; metal). Rain gauges are adjusted to winter by removing the funnel and inner cylinder and allowing the snow/freezing rain to collect inside the outer cylinder. Antifreeze liquid may be added to melt the snow or ice that falls into the gauge. In both types of gauges once the snowfall/ice is finished  accumulating, or as its height in the gauge approaches , the snow is melted and the water amount recorded. Classification. The "International Classification for Seasonal Snow on the Ground" has a more extensive classification of deposited snow than those that pertain to airborne snow. A list of the main categories (quoted together with their codes) comprises: Precipitation particles. The classification of frozen particulates extends the prior classifications of Nakaya and his successors and are quoted in the following table: "All are formed in cloud, except for rime, which forms on objects exposed to supercooled moisture, and some plate, dendrites and stellars , which can form in a temperature inversion under clear sky." Physical properties. Each such layer of a snowpack differs from the adjacent layers by one or more characteristics that describe its microstructure or density, which together define the snow type, and other physical properties. Thus, at any one time, the type and state of the snow forming a layer have to be defined because its physical and mechanical properties depend on them. The "International Classification for Seasonal Snow on the Ground" lays out the following measurements of snow properties (together with their codes): Satellite data and analysis. Remote sensing of  snowpacks with satellites and other platforms typically includes multi-spectral collection of imagery. Sophisticated 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. Satellite observations record a decrease in snow-covered areas since the 1960s, when satellite observations began. 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. Satellite observation of snow relies on the usefulness of the physical and spectral properties of snow for analysing remotely sensed data. Dietz, et al. summarize this, as follows: 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. Applicable techniques involve interpolation, using the known to infer the unknown. Passive microwaves 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 snowpack. Models. 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. Global climate change. Global climate change models (GCMs) incorporate snow as a factor  in their calculations. Some important aspects of snow cover include its albedo (reflectivity of 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 snowmelt, such as vegetation cover and terrain. These models compute snow water equivalent (SWE) in some manner, such as: SWE = [ –ln( 1 – "f" )] / "D" where: Snowmelt. 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) as: SWE = "M" ("T" – "T") when "T" ≥ "T" where: More recent models use an energy balance approach that take into account the following factors to compute the energy available for melt ("Q") as: "Q" = "Q" +"Q" + "Q" + "Q" + "Q" – "Q  where: Calculation of the various heat flow quantities (Q" ) requires measurement of a much greater range of snow and environmental factors than just temperatures. Engineering. Knowledge gained from science translates into engineering. Four examples are the construction and maintenance of facilities on polar ice caps, the establishment of snow runways, the design of snow tires and ski sliding surfaces.