{"id":445,"date":"2024-12-06T20:56:04","date_gmt":"2024-12-07T01:56:04","guid":{"rendered":"https:\/\/sites.usnh.edu\/ness\/?p=445"},"modified":"2025-08-18T16:41:32","modified_gmt":"2025-08-18T20:41:32","slug":"measuring-snow","status":"publish","type":"post","link":"https:\/\/sites.usnh.edu\/ness\/measuring-snow","title":{"rendered":"Measuring Snow\u00a0"},"content":{"rendered":"\n
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by Catherine Schmitt<\/p>\n<\/div>\n\n\n\n

<\/div>\n\n\n\n
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November 18, 2024<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/p>\n\n\n\n

A diverse array of people want to know how much snow has fallen and how much is on the ground: state and federal agency staff who forecast river flooding, skiers and other winter recreationists, foresters who cut wood in the winter, ecologists tracking the overwinter survival of pests and diseases, and scientists studying the warming climate.  <\/p>\n\n\n\n

Yet measuring snow is not a simple task, because snow is not simple stuff. Snow forms when temperatures in the atmosphere are below freezing, and the air holds excess moisture. Water vapor freezes, if temperatures are cold enough, or collects around specks of dust and other particles and then freezes into six-sided crystals. More water collects around the crystals and they grow into flakes, needles, columns, and infinite other unique shapes, eventually falling from the clouds. Snow transforms even as it descends, sometimes melting into rain, sometimes freezing harder into pellets. <\/p>\n\n\n\n

Snow begins to change as soon as it lands, sliding, drifting, sublimating and returning to the air, liquifying and seeping into soil. As it accumulates on the ground, snow continues to change, melting, refreezing, thawing, compacting.  <\/p>\n\n\n\n

Snow\u2019s ephemeral nature makes it challenging to measure. There are both manual methods, people directly observing and recording, and automated methods that use electronic instruments equipped with sensors and computers. Several networks across the region collect snow measurements, but no single entity coordinates measurement protocols and standards.  <\/p>\n\n\n\n

Snow is not a single number but a collection of different measurements including: <\/p>\n\n\n\n

Snowfall: How much snow has fallen in a given precipitation event? <\/p>\n\n\n\n

Depth: How much snow has accumulated? <\/p>\n\n\n\n

Snow Water Equivalent: How much water is stored in the snowpack? <\/p>\n\n\n\n

Distribution and Duration: Where is the most snow and when does it melt? Where is snow monitored?  <\/p>\n\n\n\n

Each of these is described in more detail below.  <\/p>\n\n\n\n

Snowfall: How much snow has fallen in a given precipitation event? <\/p>\n\n\n\n

Staff at official National Weather Service stations, as well as volunteer observers in the Cooperative Observer<\/a> and Community Collaborative Rain, Hail and Snow (CoCoRaHS<\/a>) networks, record snowfall as it happens with relatively simple equipment: <\/p>\n\n\n\n

Snow gauge<\/em> <\/p>\n\n\n\n

An 8- or 4-inch precipitation gauge with a funnel, inner measuring tube (for rainfall), and outer cylinder (for snowfall), mounted in a permanent location away from tall trees and buildings, collects snow as it falls. As soon as a snow event is over, observers melt the snow (by either adding a measured amount of warm water that is later subtracted or bringing the gauge inside and waiting for the snow to melt), pour it back through the funnel to the smaller inner measuring tube. This melted amount is reported in inches. Snow to water ratios can vary from 8:1 or less to 20:1 or more. Methods and equipment are described in detail by CoCoRaHS<\/a> and National Weather Service<\/a>.  <\/p>\n\n\n\n

\"\"
PC: CoCoRaHS, 2022 Winter Weather Measurements Webinar<\/em><\/figcaption><\/figure>\n\n\n\n

Snow board + ruler<\/em> <\/p>\n\n\n\n

Using a snow stick or ruler, an observer measures the depth of snow that has fallen on a 24 inch x 24 inch piece of plywood, painted white (or commercially available snowboard) and placed on level ground. <\/p>\n\n\n\n

\"\"
PC: CoCoRaHS, 2022 Winter Weather Measurements Webinar<\/em><\/figcaption><\/figure>\n\n\n\n

Automated\/electronic gauge<\/em> <\/p>\n\n\n\n

Automated precipitation gauges use various combinations of tipping buckets, probes, heated funnels, and thermostats to collect, measure, and record snowfall. <\/p>\n\n\n\n

\"\"
PC: NiuBoL.com, 2024<\/em><\/figcaption><\/figure>\n\n\n\n

<\/p>\n\n\n\n

Depth: How much snow has accumulated on the ground and on other surfaces?<\/h2>\n\n\n\n

After snow descends, it immediately begins to transform. Dendritic ice crystals decompose into fragments, with larger fragments growing as smaller fragments shrink. Over time, the fragments become rounded. If there is a temperature gradient in the snowpack, larger snow grains may form near the bottom of the snowpack, especially if the snow remains on the ground for very long.\u00a0\u00a0<\/p>\n\n\n\n

Manual Methods<\/h2>\n\n\n\n

Ruler or Measuring Stick<\/em> <\/p>\n\n\n\n

NWS observers measure snow depth (in inches) once daily (24-hour snowfall) with a wooden or metal ruler pushed vertically into the snow on the snowboard or other flat surface. Often depth is determined by averaging several readings from different locations within 300 feet of the official observing location, to account for variation in sun and wind exposure. You can contribute your own snow depth readings to the community or citizen science networks such as Community Collaborative Rain, Hail, and Snow (CoCoRaHS.org) Network. If you are a backcountry enthusiast, consider using your avalanche probe to submit snow depth measurements to the Community Snow Observations (CommunitySnowObs.org) network. The Appalachian Mountain Club<\/a> has detailed instructions on how to submit data through CSO.  <\/p>\n\n\n\n

Snow stake<\/em> <\/p>\n\n\n\n

Observers record snow depth at a permanently mounted measuring stake. On Mount Mansfield<\/a> (VT), the National Weather Service uses a stake to measure snow depth. Snow stake measurements can be automated with cameras. <\/p>\n\n\n\n

Snow tube (core sampler)<\/em> <\/p>\n\n\n\n

An aluminum tube with cutting edge and measured increments is pushed down through the snowpack to the ground and depth of snow recorded.  <\/p>\n\n\n\n

\"\"
PC: (Leah Hogsten | The Salt Lake Tribune) Snow sampling tubes measure snow density and water content at SNOTELs on March 24, 2022 <\/em><\/figcaption><\/figure>\n\n\n\n

Aerial snow marker<\/em><\/p>\n\n\n\n

Located in areas of the Western U.S. that are difficult to access on the ground, snow markers<\/a> are large pipes with cross pieces attached at set heights, observed from aircraft flying over the area. <\/p>\n\n\n\n

Automated Methods<\/h2>\n\n\n\n

Ultrasonic sensor<\/em>\u00a0<\/p>\n\n\n\n

An instrument that measures the time it takes an ultrasonic pulse directed at the snow to reflect back to the sensor. The elapsed time between signal send and receive tells how much snow is present. Air temperature must be measured at the same time, as the speed of sound in air varies with temperature. The New York State Mesonet<\/a> snowpack monitoring stations use ultrasonic sensors to measure snow depth every five minutes. <\/p>\n\n\n\n

 <\/p>\n\n\n\n

\"\"
PC: MaxBotix<\/em>.com, 2024<\/figcaption><\/figure>\n\n\n\n

Radio transmitter<\/em>\u00a0<\/p>\n\n\n\n

A receiver detects signals from a Radio Frequency Identification (RFID) tag placed under the snow. The elapsed time is an indicator of how much snow is present. The greater the delay in transmission, the more snow is present. This is an experimental method<\/a>.  <\/p>\n\n\n\n

<\/p>\n\n\n\n

Snow Water Equivalent (SWE): How much water is in the snow?<\/h2>\n\n\n\n

Snow Water Equivalent is a measure of the amount of water within the snowpack. The snow-water equivalent is the depth of water that would cover the ground if the snow cover was melted. Snow-water equivalent can be calculated from the density and depth of the snowpack SWE = depth * density (in decimal %, for example 10% = 0.10). Light, powdery snow typically has a density of 5 % to 10 %, while heavy, wet snow might be closer to 25 % to 30 %. Light, powdery snow holds less water than a heavy, wet snowpack of the same depth.\u00a0\u00a0<\/p>\n\n\n\n

Manual<\/h2>\n\n\n\n

Snow tube (core sampler)<\/em> <\/p>\n\n\n\n

An aluminum tube with cutting edge and measured increments is pushed down through the snowpack to the ground. After surveyors record the depth of snow, the core is extracted and weighed with a handheld spring scale to determine snow water equivalent (after subtracting the weight of the empty tube), which can be converted to density using standard tables or by dividing SWE by the observed depth.\u00a0\u00a0<\/p>\n\n\n\n

Automated<\/h2>\n\n\n\n

Snow pillow<\/em><\/p>\n\n\n\n

A snow pillow sits on flat ground and is typically a round or hexagonal, ten-foot diameter \u201cpillow\u201d of butyl or Hypalon filled with a dilute, low-toxicity antifreeze liquid and covered with a protective layer. As snow accumulates on the pillow, it pushes down, displacing an equal weight of fluid, pushing it into a pipe. The height of this displacement is measured by a pressure transducer to calculate the weight and then convert to snow water equivalent. Snow pillows are commonly used at over 900 sites the Western US and Alaska (SNOTEL<\/a>), and western Canada. <\/p>\n\n\n\n

\"\"
PC: NRCS, 11\/5\/2024<\/em><\/figcaption><\/figure>\n\n\n\n

Snow scale<\/em> <\/p>\n\n\n\n

Alternatives to the pillow are scales that measure weight directly, using springs and levers like a typical household scale, or load cells that have an electrical signal that changes with weight. <\/p>\n\n\n\n

Gamma ray sensor<\/em> <\/p>\n\n\n\n

A sensor detects tiny amounts of naturally occurring gamma rays emitted from potassium and thallium in soil. The snowpack attenuates the signal emitted by each element, and the level of attenuation allows the sensor to estimate the amount of water present in the column. When combined with a near-surface soil moisture reading, an approximate liquid water content in the snow can be extracted. A collimator is used when there are sources of potassium and thallium at the site that will not be covered by snow, such as trees. The collimator acts to reduce this radiation from affecting the measurement. The New York State Mesonet<\/a> stations use gamma ray sensors to estimate SWE over a six-hour period with observations provided up to four times daily.  <\/p>\n\n\n\n

Gamma ray sensors are also used by the National Weather Service National Operational Hydrologic Remote Sensing Center<\/a> (\u201cNOAA\u2019s source for snow information\u201d) to measure snow water equivalent and soil moisture via low-flying airborne surveys<\/a>. <\/p>\n\n\n\n

Other sensors<\/em> <\/p>\n\n\n\n

A sensor detects the change in naturally occurring electromagnetic energy from the ground after it passes through snow cover. Other sensors use ultra wide band radar<\/a>. <\/p>\n\n\n\n

Distribution: Where is the snow?<\/h2>\n\n\n\n

Data on snow cover spatial extent and duration are needed to understand change over time, forecast hydrological conditions, model suitable wildlife habitat, and address other questions. Snow is monitored via national, regional, and state-based stations and networks. <\/p>\n\n\n\n

Weather observations<\/em> <\/p>\n\n\n\n

Extent of snow cover and duration of snow cover can be estimated by compiling data from weather stations and observers. Global Historical Climatology Network<\/a> daily (GHCNd) is an integrated database of daily climate summaries from land surface stations across the globe. Snow and weather (temperature precipitation) observations can then be interpolated into gridded snow products<\/a> to assess the spatial extent of snow over time.  <\/p>\n\n\n\n

Remote sensing <\/p>\n\n\n\n

Satellites equipped with various sensors provide data that can be analyzed to estimate snow depth, SWE, and snow distribution. For example, some sensors detect multifrequency microwave radiation<\/a> emitted from the Earth\u2019s surface, which can be scattered or blocked by snow. Snow depth can be estimated from optical remote sensing data using photogrammetric techniques or lidar (Light Detection and Ranging) scans of surface elevation to measure snow depth relative to snow-free areas. <\/p>\n\n\n\n

Currently there is no single way to measure SWE from space<\/a>, but this is an active area of research. Data from satellite-based microwave, radar, and optical sensors can be combined with other snow measurements to estimate snow water equivalent. The NASA Committee on Earth Observation Satellites<\/a> maintains a list of operational snow data products accompanied with their spatial and temporal coverage, spatial and temporal resolution, and a link to validation information. Most relate to snow cover (see below).  <\/p>\n\n\n\n

For snow cover distribution, NOAA\u2019s National Operational Hydrologic Remote Sensing Center<\/a> provides comprehensive snow observations, analyses, data sets and maps. The U.S. National Ice Center<\/a> analyzes imagery from various satellites, radar, models, and ground station data to map snow and ice cover over the Northern Hemisphere at one-kilometer resolution. Snow cover geography is easier to observe from space than other aspects because of how much solar radiation snow reflects (albedo). The albedo of snow can range from 80 percent or more for freshly-fallen snow to less than 40 percent after snow has decayed. The high albedo of snow presents a good contrast with most other natural surfaces (except clouds) and can therefore be detected by satellites, equipped with low resolution optical and microwave sensors (GOES, MSG, MetOp) and high resolution imagery of infrared and other wavelengths (NASA Landsat 8\/9 and\/or Sentinel-2) NASA currently provides data on snow cover derived from remote sensing observations, including snow cover (absence or presence), or fractional (the fraction of a pixel which is covered by snow), such as MODIS<\/a>, with a resolution of 500 meters, which incorporates all available spectral information to retrieve fractional snow cover and other properties such as snow albedo, grain size or the presence of light absorbing particles. Researchers are studying<\/a> how to translate these data to more complicated mountain environments. <\/p>\n\n\n\n

Regional & State Data Sources  <\/p>\n\n\n\n

The collection of snow-depth and snow water equivalent information in the region extends back several decades, with more than 2,200 active ground-based observation sites across the region, according to a 2022 US Army Corps of Engineers summary report<\/a>.  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. The Network for Environment and Weather Applications<\/a> (NEWA) based at Cornell University collects weather data from weather stations primarily located on farms and at airports, however, these stations do not report snow information. <\/p>\n\n\n\n

\"\"
PC: Braedon Lineman – Sources and Locations for Regional Automated and Manual Snow Measurements<\/em><\/figcaption><\/figure>\n\n\n\n

<\/p>\n\n\n\n

New York<\/h2>\n\n\n\n

National Weather Service observation stations<\/em><\/p>\n\n\n\n

10 WBAN (Binghamton, Islip, LaGuardia, Buffalo, Albany, Canton, Rochester, Syracuse, Manhattan\/Central Park, Queens)  
118 COOP 
206 CoCoRaHS <\/p>\n\n\n\n

Additional snow monitoring<\/em> <\/p>\n\n\n\n

The New York State Mesonet<\/a> operates a sub-network of 20 snowpack monitoring stations across select areas of the Adirondacks, Tug Hill, and Catskills. Ultrasonic sensors measure snow depth and gamma ray sensors estimate SWE.\u00a0\u00a0<\/p>\n\n\n\n

Vermont<\/h2>\n\n\n\n

National Weather Service observation stations<\/em><\/p>\n\n\n\n

1 WBAN (Burlington) 
24 COOP 
72 CoCoRaHS<\/a> <\/p>\n\n\n\n

Additional snow monitoring<\/em><\/p>\n\n\n\n

The National Weather Service Cooperative Weather Station at the summit of Mount Mansfield<\/a> includes daily snow depth monitoring at a snow stake since 1954 (view data here<\/a>).\u00a0
Snowfall is also monitored and reported by ski resorts.\u00a0<\/p>\n\n\n\n

New Hampshire<\/h2>\n\n\n\n

National Weather Service observation stations<\/em> <\/p>\n\n\n\n

3 WBAN (Manchester, Concord, Mount Washington) 
31 COOP 
51 CoCoRaHS<\/a> <\/p>\n\n\n\n

Additional snow monitoring<\/em><\/p>\n\n\n\n

The Mount Washington Observatory Regional Mesonet<\/a> is a network of automated stations in and around the White Mountains, including ski resorts and Appalachian Mountain Club huts, that measure a variety of near-surface variables<\/a> at different elevations. \u00a0
\u00a0<\/p>\n\n\n\n

Maine<\/h2>\n\n\n\n

National Weather Service observation stations<\/em><\/p>\n\n\n\n

3 WBAN stations (Portland, Bangor, Caribou) 
36 COOP
60 CoCoRaHS <\/p>\n\n\n\n

Additional snow monitoring<\/em> <\/p>\n\n\n\n

The Maine Cooperative Snow Survey<\/a> collects, interprets, and distributes information on the depth and water content of Maine’s snowpack in the late winter and early spring, when the danger of flooding in Maine’s rivers and streams is greatest. The data are obtained from a number of cooperating sources and managed by the Maine Geological Survey<\/a><\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

by Catherine Schmitt November 18, 2024 A diverse array of people want to know how much snow has fallen and how much is on the ground: state and federal agency staff who forecast river flooding, skiers and other winter recreationists, foresters who cut wood in the winter, ecologists tracking the overwinter survival of pests and […]<\/p>\n","protected":false},"author":304,"featured_media":478,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[43,15,23],"tags":[54,50,48,44,45,52,55,53,41,49,35,47,51,46],"class_list":["post-445","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data","category-info","category-outreach","tag-automated","tag-citizen","tag-citizen-science","tag-data","tag-data-collection","tag-depth","tag-manual","tag-methods","tag-monitoring","tag-science","tag-sensors","tag-stations","tag-swe","tag-tools"],"_links":{"self":[{"href":"https:\/\/sites.usnh.edu\/ness\/wp-json\/wp\/v2\/posts\/445","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.usnh.edu\/ness\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sites.usnh.edu\/ness\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sites.usnh.edu\/ness\/wp-json\/wp\/v2\/users\/304"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.usnh.edu\/ness\/wp-json\/wp\/v2\/comments?post=445"}],"version-history":[{"count":45,"href":"https:\/\/sites.usnh.edu\/ness\/wp-json\/wp\/v2\/posts\/445\/revisions"}],"predecessor-version":[{"id":680,"href":"https:\/\/sites.usnh.edu\/ness\/wp-json\/wp\/v2\/posts\/445\/revisions\/680"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sites.usnh.edu\/ness\/wp-json\/wp\/v2\/media\/478"}],"wp:attachment":[{"href":"https:\/\/sites.usnh.edu\/ness\/wp-json\/wp\/v2\/media?parent=445"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.usnh.edu\/ness\/wp-json\/wp\/v2\/categories?post=445"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.usnh.edu\/ness\/wp-json\/wp\/v2\/tags?post=445"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}