Snow Today

What is Snow Today?

Snow Today is a NASA-funded research project that examines where snow is present, where it has snowed recently, and how much water is in the snow. It also makes comparisons between snow today, snow yesterday, snow last year, and snow over the last few decades. 

The Snow Today project publishes the following open access resources on this website:

• Daily data visualizations of snow conditions in near-real time across the western United States
• Scientific data analysis articles published during the winter and spring months of the snow season

Snow Today relies on satellite data collected hundreds of miles above Earth and data from snow monitoring stations in remote mountain areas. To map snow surface properties, our team takes daily satellite data and applies physically-based techniques, which have been refined to produce accurate snow surface properties and to map snow hidden from satellites, such as beneath clouds and forest canopies. While the satellite data show snow location, it does not provide direct information on snow depth or snow water equivalent (SWE), which is a measure of how much water is stored within snow. To complement the remotely sensed data, our team also analyzes in situ SWE measured at hundreds of snow monitoring stations, many of which have records dating back to the 1980s. Snow Today uses SWE data to determine changes in snow quantity from new snowstorms that add water and from snowmelt events that drain melted snow from the snowpack. For stations with multiple decades of SWE data, Snow Today also compares recent SWE and snowstorm events to average conditions for that time of year.

Remotely sensed snow surface properties

Notes:

1. Cloud-free and canopy-adjusted snow surface properties are updated daily, with a typical one to two-day lag.
2. For full bibliographic information on the studies cited in these descriptions, see the References section of this page.

Snow cover percent

Spatially and temporally complete snow cover percent is interpolated (Rittger et al, 2020) from Moderate Resolution Imaging Spectroradiometer (MODIS) snow covered area and grain size (MODSCAG) model (Painter et al, 2009). To fill gaps identified as clouds or missing data, this analysis interpolates using spatial and temporal filters. Satellite observations that look straight down are weighted more heavily in the interpolation than satellite views from an angle. To account for distortions caused by satellite angle and forest cover, this analysis uses forest height maps and vegetation cover percent.

Snow albedo

Spatially and temporally complete snow albedo is estimated by combining data from the Moderate Resolution Imaging Spectroradiometer (MODIS) snow covered area and grain size (MODSCAG) model (Painter et al., 2009) and the MODIS Dust Radiative Forcing model (Painter et al., 2012). Satellite views that look straight down are weighted more heavily in the interpolation than satellite views from a side-looking angle. Our remotely sensed albedos are interpolated over the snow surface (Rittger et al., 2020) and show 4 to 6 percent root mean square error and negligible bias, a 3 to 11 percent improvement over empirical decay models, which require extensive in situ measurements (Bair et al., 2019).

Snow radiative forcing

Spatially and temporally snow radiative forcing is estimated by combining data from the Moderate Resolution Imaging Spectroradiometer (MODIS) snow covered area and grain size (MODSCAG) model (Painter et al, 2009) and the MODIS Dust Radiative Forcing (MODDRFS) model (Painter et al, 2012). Satellite observations that look straight down are weighted more heavily in the interpolation than satellite views from a side looking angle. These remotely sensed products are interpolated over the snow surface (Rittger et al, 2020) and show a root mean square error of 3.6 percent and bias of -1.5 percent (Bair et al, 2019).

Snow cover days

Spatially and temporally complete snow cover days are counted as the number of days with snow cover since October 1, the beginning of the water year in the Northern Hemisphere. We use a threshold of 10 percent snow cover percent for each pixel to count the day as snow covered.

In situ snow water equivalent overlays

Note: Snow water equivalent data are updated daily. 

Change in Snow Water Equivalent

This overlay shows changes in daily snow water equivalent (SWE) at the date indicated on the image, based on snow station data

Percentage of Median Snow Water Equivalent

This overlay gives an estimate of snow water equivalent (SWE) calculated by dividing SWE for the current day by the average SWE for the historical record on the same calendar day. The resulting value is multiplied by 100 to convert from a fractional value to a percentage. For Snow Today, we require stations to have measured 25 years of data to make this calculation.

Snow Water Equivalent

This overlay shows daily snow water equivalent (SWE) on the date indicated on the image, based on snow station data. The data are expressed as a percent of average over each station’s record, which includes a minimum of 25 years.

Glossary of terms

Hydrologic Unit Code (HUC)

A hierarchical designation consisting of two additional digits for each level of specificity; established by the United States Geological Survey (USGS) to delineate portions of the United States based on surface features related to the distribution and movement of water. The HUC system divides the United States into 21 two-digit regions, 222 four-digit subregions, 370 six-digit basins, and smaller regions designated by additional digits. Snow Today currently includes HUC2 and HUC4 subregions.

MOD09GA surface reflectance

Surface spectral reflectance of Terra MODIS Bands 1 through 7, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering.

MODDRFS

The MODIS Dust Radiative Forcing in Snow algorithm determines the spectral reflectance differences between measured and modeled clean snow of the same grain size, along with direct solar irradiance estimates of snow radiative forcing. MODDRFS has been processed by the Jet Propulsion Laboratory. 

MODSCAG

The MODIS snow cover and grain size algorithm, a physically based model for estimating snow cover percent and snow grain size for clear sky surface reflectance using MOD09GA surface reflectance. MODSCAG has been processed by the Jet Propulsion Laboratory.

SNOTEL

A network of snow telemetry sites measuring snow water equivalent (SWE) with snow pillows, as well as other variables of interest related to snow depth, weather, and soil moisture. These sites are maintained by the Natural Resources Conservation Service (NRCS), an agency within the United States Department of Agriculture (USDA).

Snow albedo (snow brightness)

A non-dimensional, unitless quantity that measures how well a surface reflects solar energy, ranging from 0 to 1. A value of 0 means the surface is a perfect absorber, where all incoming energy is absorbed, while a value of 1 means the surface is a perfect reflector, where all incoming energy is reflected and none is absorbed. Fresh, clean snow with a high albedo appears bright, while old or dirty snow tends to have a lower albedo and appears darker. This quantity can also be expressed as a percent with a range from 0 to 100, with zero percent absorbing all incoming energy and 100 percent reflecting all energy.

Snow cover days

The number of days a region has been covered with snow, identified with snow cover percent greater than a specific threshold since a specific starting time. Our data use 15 percent as the snow percent threshold and October 1 as the starting date for Daily Snow Viewer maps.

Snow cover percent

The areal extent of snow-covered ground, expressed as the mathematical percent of a region covered with snow. In the context of Snow Today, the region refers to an Earth-observing satellite’s smallest measurement area. We use data from the Moderate Resolution Imaging Spectroradiometer at roughly 463 meter spatial resolution. Note that the Earth’s surface is sometimes covered by clouds.

Snow cover percent threshold

Snow cover percent, below which Snow Today has less confidence, generally due to bright soils or shallow water that cannot be easily separated using surface reflectance data.

Snow pillow

A large, flat instrument that measures and reports the water weight of snowpack on the ground. The weight of water is the snow water equivalent (SWE).

Snow radiative forcing

When snow impurities such as dust or soot fall on snow, its surface darkens and absorbs more solar energy. Snow radiative forcing is a measure of the additional absorption of solar radiation from light absorbing particles (LAP) such as dust or soot. Units of measure are Watts per square meter (W/m²) and values can range from 0 to 500 W/m². This maximum value depends on incoming solar radiation (elevation, direct sun versus shaded) and the amount of dust or soot. A value of 0 means no additional radiation is being absorbed. A value of 500 means nearly all of the solar energy is absorbed (depending on latitude, elevation, clouds). Radiative forcing is calculated by the difference between the net (downward minus upward) radiative fluxes (irradiance) with and without LAP.

Snow water equivalent (SWE)

The water content obtained if all snowpack at a location melted instantly. Because snow contains a mix of water (ice and liquid) and air, the snow water equivalent (SWE) is less than the depth of the snow on the ground.

Snowfall

New snow that has fallen out of the atmosphere and accumulated on the ground since the previous day or since the previous observation.

Data sources

Moderate Resolution Imaging Spectroradiometer (MODIS) data

The MODIS Snow Covered Area and Grain-size (MODSCAG) data are provided by Snow Data System (SnowDS) at the Jet Propulsion Laboratory (JPL). The reflectance data are from NASA MODIS.

Snow station data

Snow station data are sourced from the Snow Telemetry (SNOTEL) network by the Natural Resources Conservation Service (NRCS), United States Department of Agriculture (USDA) and the California Department of Water Resources. The data are publicly accessed at www.wcc.nrcs.usda.gov/snow.

Computing

This work utilizes resources from the University of Colorado Boulder Research Computing Group, which is supported by the National Science Foundation (awards ACI-1532235 and ACI-1532236), the University of Colorado Boulder, and Colorado State University.

Basemaps

NSIDC’s Daily Snow Viewer offers multiple basemaps, which are credited as follows:

USGS Topographic

US Geological Survey. "USGS Topo Base Map Service from The National Map" [basemap]. 2013. Reston, Virginia: US Department of the Interior. https://www.sciencebase.gov/catalog/item/544171f4e4b0b0a643c73c28. (August 20, 2022)

USGS Topographic + Imagery

US Geological Survey. "USGS Imagery Topo Base Map Service from The National Map" [basemap]. 2016. Reston, Virginia: US Department of the Interior.

USGS Imagery

US Geological Survey. "USGS Imagery Only Base Map Service from The National Map" [basemap]. 2016. Reston, Virginia: US Department of the Interior.

USGS Shaded Relief

US Geological Survey. "USGS Hill Shade Base Map Service from The National Map" [basemap]. 2016. Reston, Virginia: US Department of the Interior.

USGS Hydro Cached

US Geological Survey. "USGS Hydro-NHD Base Map Service from The National Map" [basemap]. 2016. Reston, Virginia: US Department of the Interior.

ArcGIS Light Gray

Esri. "ArcGIS Light Gray" [basemap]. Vector. "Light Gray Canvas". November 22, 2022. https://www.arcgis.com/home/item.html?id=979c6cc89af9449cbeb5342a439c6a76. (August 20, 2022).

ArcGIS Light Gray - Base Only

Esri. "ArcGIS Light Gray - Base Only" [basemap]. Vector. "Light Gray Canvas". November 22, 2022. https://www.arcgis.com/home/item.html?id=979c6cc89af9449cbeb5342a439c6a76. (August 20, 2022).

ArcGIS Dark Gray

Esri. "ArcGIS Dark Gray" [basemap]. Vector. "Dark Gray Canvas". November 22, 2022. https://www.arcgis.com/home/item.html?id=358ec1e175ea41c3bf5c68f0da11ae2b. (August 20, 2022).

ArcGIS Dark Gray - Base Only

Esri. "ArcGIS Dark Gray - Base Only" [basemap]. Vector. "Dark Gray Canvas". November 22, 2022. https://www.arcgis.com/home/item.html?id=358ec1e175ea41c3bf5c68f0da11ae2b. (August 20, 2022).

ArcGIS National Geographic

Esri. "ArcGIS National Geographic" [basemap]. Vector. "National Geographic Style". November 22, 2022. https://www.arcgis.com/home/item.html?id=3d1a30626bbc46c582f148b9252676ce. (August 20, 2022).

ArcGIS World Topographic

Esri. "ArcGIS World Topographic" [basemap]. Vector. "World Topographic Map". November 22, 2022. https://www.arcgis.com/home/item.html?id=7dc6cea0b1764a1f9af2e679f642f0f5. (August 20, 2022).

Acknowledgements

The Snow Today website is a collaborative effort of many team members at several institutions at the University of Colorado Boulder, including the Institute of Arctic and Alpine Research (INSTAAR), the National Snow and Ice Data Center (NSIDC), and the Cooperative Institute for Research in Environmental Sciences (CIRES). These analyses build upon the legacy of a similar website formerly maintained at NSIDC by the late Drew Slater. 

We would like to specifically acknowledge contributions from the following: Karl Rittger, Mark Raleigh, Mary Jo Brodzik, Mark Serreze, Agnieszka Gautier, Suzanne Craig, Daniel Crumley, Jessica Calme. Audrey Payne, Lisa Booker, Ted Scambos, Jeff Deems, Andrew Barrett, Donna Scott, Doug Young, Chris Torrence, Kate Heightley, Joni Reeves, Keith Musselman, Ann Windnagel, Matthew Fisher, Leslie Goldman, Timbo Stillinger, Aubrey Dugger, Michon Scott, and Merritt Turetsky.

References

Stillinger, T., K. Rittger, M.S. Raleigh, A. Michell, R.E. Davis, and E.H. Bair. 2023. Landsat, MODIS, and VIIRS snow cover mapping algorithm performance as validated by airborne lidar datasets. The Cryosphere 17: 567-590, doi: 10.5194/tc-17-567-2023.

Rittger, K., K. J. Bormann, E. H. Bair, J. Dozier, and T. H. Painter. 2021. Evaluation of VIIRS and MODIS Snow Cover Fraction in High-Mountain Asia Using Landsat 8 OLI. Frontiers in Remote Sensing, 2 (8). doi: 10.3389/frsen.2021.647154.

Rittger, K., M. S. Raleigh, J. Dozier, A. F. Hill, J. A. Lutz, and T. H. Painter. 2020. Canopy Adjustment and Improved Cloud Detection for Remotely Sensed Snow Cover Mapping. Water Resources Research, 55. doi: 10.1029/2019WR024914.

Bair, E. H., K. Rittger, S. M. Skiles, and J. Dozier. 2019. An Examination of Snow Albedo Estimates From MODIS and Their Impact on Snow Water Equivalent Reconstruction. Water Resources Research, 55, 7826-7842, doi: 10.1029/2019wr024810.

Rittger, K., T. H. Painter, and J. Dozier. 2013. Assessment of methods for mapping snow cover from MODIS. Advances in Water Resources, 51, 367-380, doi: 10.1016/j.advwatres.2012.03.002.

Raleigh, M. S., K. Rittger, C. E. Moore, B. Henn, J. A. Lutz, and J. D. Lundquist. 2013. Ground-based testing of MODIS fractional snow cover in subalpine meadows and forests of the Sierra Nevada. Remote Sensing of Environment, 128: 44–57. doi: 10.1016/j.rse.2012.09.016.

Painter, T. H., A. C. Bryant, and S. M. Skiles. 2012. Radiative forcing by light absorbing impurities in snow from MODIS surface reflectance data. Geophysical Research Letters, 39, L17502, doi: 10.1029/2012gl052457.

Painter, T. H., K. Rittger, C. McKenzie, P. Slaughter, R. E. Davis, and J. Dozier. 2009. Retrieval of subpixel snow covered area, grain size, and albedo from MODIS. Remote Sensing of Environment, 113(4): 868–879. doi:10.1016/j.rse.2009.01.001.

Dozier, J., T. H. Painter, K. Rittger, and J. E. Frew. 2008. Time–space continuity of daily maps of fractional snow cover and albedo from MODIS. Advances in Water Resources, 31(11): 1515–1526. doi:10.1016/j.advwatres.2008.08.011.

Painter, T. H., J. Dozier, D. A. Roberts, R. E. Davis, and R. O. Green. 2003. Retrieval of subpixel snow-covered area and grain size from imaging spectrometer data. Remote Sensing of Environment, 85, 64-77, doi: 10.1016/s0034-4257(02)00187-6.

Related References

Published research using Snow Today data products for applications ranging from streamflow forecasts, Sierra Bighorn mortality to large scale climate modeling, and beyond.

Hao, D., Bisht, G., He, C., Bair, E., Huang, H., Dang, C., Rittger, K., Gu, Y., Wang, H., Qian, Y., & Leung, L.R. (2022). Improving snow albedo modeling in E3SM land model (version 2.0) and assessing its impacts on snow and surface fluxes over the Tibetan Plateau. Geosci. Model Dev. Discuss., 2022, 1-31, doi: 10.5194/gmd-2022-67.

Huang, H., Qian, Y., He, C., Bair, E.H., & Rittger, K. (2022). Snow Albedo Feedbacks Enhance Snow Impurity-Induced Radiative Forcing in the Sierra Nevada. Geophysical Research Letters, 49, e2022GL098102, doi: 10.1029/2022GL098102.

Berger, D.J., German, D.W., John, C., Hart, R., Stephenson, T.R., & Avgar, T. (2022). Seeing Is Be-Leaving: Perception Informs Migratory Decisions in Sierra Nevada Bighorn Sheep (Ovis canadensis sierrae). Frontiers in Ecology and Evolution, 10, doi: 10.3389/fevo.2022.742275.

Bair, E., Stillinger, T., Rittger, K., & Skiles, M. (2021). COVID-19 lockdowns show reduced pollution on snow and ice in the Indus River Basin. Proceedings of the National Academy of Sciences, 118, e2101174118, doi: 10.1073/pnas.2101174118.

Rittger, K., Krock, M., Kleiber, W., Bair, E.H., Brodzik, M.J., Stephenson, T.R., Rajagopalan, B., Bormann, K.J., & Painter, T.H. (2021). Multi-sensor fusion using random forests for daily fractional snow cover at 30 m. Remote Sensing of Environment, 264, 112608, doi: 10.1016/j.rse.2021.112608.

Yang, K., Musselman, K.N., Rittger, K., Margulis, S.A., Painter, T.H., & Molotch, N.P. (2021). Combining ground-based and remotely sensed snow data in a linear regression model for real-time estimation of snow water equivalent. Advances in Water Resources, 104075, doi: 10.1016/j.advwatres. 2021.104075.

Micheletty, P., Perrot, D., Day, G., & Rittger, K. (2021). Assimilation of Ground and Satellite Snow Observations in a Distributed Hydrologic Model for Water Supply Forecasting. JAWRA Journal of the American Water Resources Association, n/a, doi: 10.1111/1752-1688.12975.

Ackroyd, C., Skiles, S.M., Rittger, K., & Meyer, J. (2021). Trends in Snow Cover Duration Across River Basins in High Mountain Asia From Daily Gap-Filled MODIS Fractional Snow Covered Area. Frontiers in Earth Science, 9, doi: 10.3389/feart.2021.713145.

Zhao, H., Hao, X., Wang, J., Li, H., Huang, G., Donghang, S., Su, B., Huajin, L., & Hu, X. (2020). The Spatial–Spectral–Environmental Extraction Endmember Algorithm and Application in the MODIS Fractional Snow Cover Retrieval. Remote Sensing, 12, 3693, doi: 10.3390/rs12223693.

Khan, A., Rittger, K., Xian, P., Katich, J.J., Armstrong, R.L., Kayastha, R., Dana, J. McKnight, D.M., (2020). Biofuel Burning Influences Refractory Black Carbon Concentrations in Seasonal Snow at Lower Elevation of the Dudh Koshi River basin of Nepal. Frontiers, Earth Science. doi: 10.3389/feart.2020.00371.

Sarangi, C., Qian, Y., Rittger, K., Leung R., Chand, D., Bormann, K., Painter, T.H., (2020), Dust dominates high-altitude snow darkening and melt over high-mountain Asia, Nature-Climate Change. doi: 10.1038/s41558-020-00909-3.

Hill, A. F., Rittger, K., Dendup, T., Tshering, D., & Painter, T. H. (2020). How Important Is Meltwater to the Chamkhar Chhu Headwaters of the Brahmaputra River? Frontiers in Earth Science, 8(81). doi: 10.3389/feart.2020.0008.

Bair, E.H., Rittger, K., Ahmad, J.A., & Chabot, D. (2020): Comparison of modeled snow properties in Afghanistan, Pakistan, and Tajikistan, The Cryosphere, 14, 331-347, doi: 10.5194/tc-14-311-2020.

Painter, T.H., Skiles, S.M., Deems, J.S., Brandt, W.T., & Dozier, J. (2018). Variation in Rising Limb of Colorado River Snowmelt Runoff Hydrograph Controlled by Dust Radiative Forcing in Snow. Geophysical Research Letters, 45, 797-808, doi: 10.1002/2017gl075826.

Armstrong, R.L., Rittger, K., Brodzik, M.J., Racoviteanu, A., Barrett, A.P., Khalsa, S.J.S, Raup, B., Hill, A.F., Khan, A.L., Wilson, A.M., Kayastha, R.B., Fetterer, F., Armstrong, B., (2018) Contributions to High Asia runoff from glacier ice and seasonal snow: separating melt water sources in river flow. Regional Environmental Change. doi: 10.1007/s10113-018-1429-0.

Hill, A.F., Stallard, R.F., Rittger, K., (2018), Clarifying regional hydrologic controls of the Maranon River, Peru through rapid assessment to inform system-wide basin planning approaches. Elementa: Science of the Anthropocene, 6, Art. No 37, doi: 10.1525/elementa.290.

Bair, E. H., Abreu Calfa, A., Rittger, K., and Dozier, J. (2018), Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan, The Cryosphere, 12(5), 1579-1594, doi: 10.5194/tc-12-1579-2018.

Rittger, K., Bair, E., Kahl, A., Dozier, J. (2016), Spatial estimates of snow water equivalent from reconstruction, Advances in Water Resources, 94, 345-363, doi: 10.1016/j.advwatres.2016.05.015.

Bair, E.H., Rittger, K., Davis, R.E., Painter, T.H., & Dozier, J. (2016). Validating reconstruction of snow water equivalent in California's Sierra Nevada using measurements from the NASA Airborne Snow Observatory. Water Resources Research, 52, 8437-8460. doi: 10.1002/2016WR018704.

Micheletty, P.D., Kinoshita, A.M., & Hogue, T.S. (2014). Application of MODIS snow cover products: wildfire impacts on snow and melt in the Sierra Nevada. Hydrology and Earth System Sciences Discussions, 11, 7513-7549, doi: 10.5194/hessd-11-7513-2014.

Skiles, S.M., Painter, T.H., Deems, J.S., Bryant, A.C., & Landry, C.C. (2012). Dust radiative forcing in snow of the Upper Colorado River Basin: 2. Interannual variability in radiative forcing and snowmelt rates. Water Resources Research, 48, doi: 10.1029/2012WR011986.