Snow Today

What is Snow Today?

Snow Today 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 and New Zealand with additional regions coming online in 2026
• Scientific data analysis articles for the Western United States published during the winter and spring months of the snow season

Snow Today relies on satellite data collected hundreds of miles above the Earth and data from snow monitoring stations across mountain areas on the Earth’s surface. To map snow surface properties including snow cover extent and duration as well as snow albedo and snow darkening from light absorbing particles, our team takes daily satellite data and applies physically based techniques, which have been refined to map snow hidden from satellites, such as beneath clouds and forest canopies. While the satellite data show snow extent, duration, and albedo, they do 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 five years or more of SWE data, Snow Today also compares recent SWE and snowstorm events to average conditions for that time of year.

Data access

In addition to interactive data visualization via the Daily Snow Viewer application, individual data files are available for download via FTP. The files include gridded data for snow surface properties and summarized data for snow surface properties and SWE.

To access the data using an FTP client (e.g. FileZilla, Cyberduck, FireFTP, WinSCP, etc.), please use the following information as input:

Host Name: dtn.rc.colorado.edu
Username: anonymous
Password: pwd
Directory: /shares/snow-today
If you need further help, see the General instructions to access FTP help page.

For information on the different algorithms used to create these data, see the Algorithms section below.

Data variables

Remotely sensed snow surface properties

Notes:

1. Cloud-free snow surface properties are updated daily, with a typical lag of less than 24 hours.
2. Starting March of 2024, Snow Today uses data from the SPIReS algorithm. Data from the STC-MODSCAG/MODDRFS algorithm used by Snow Today from 2020 to 2023 will be added as an additional data stream pending processing using the input MODSCAG/MODDRFS data (renamed to MODSCGDRF by NSIDC DAAC). See the Algorithms section of this page for more information.
3. For full bibliographic information on the studies cited in these descriptions, see the References section of this page.

The following variables are or will be available on the Daily Snow Viewer application and are derived from MODIS and VIIRS satellite data. To fill gaps identified as clouds or missing data using spatial and temporal filters, this analysis interpolates between days with missing data. 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 ancillary information like forest height maps, forest cover percent, crown height, and crown width. See the Glossary section of this page for variable definitions and the References section for technical algorithm details.

• Snow cover percent
• Snow albedo
• Snow radiative forcing
• Snow cover duration
• Days since last observation (to be added in 2026)

In situ snow water equivalent

Note: Snow water equivalent (SWE) data are updated daily.

Snow water equivalent data come from in situ observations that use snow pillows to measure the amount of water present in the snow. See Data Sources for region-specific information. The following variables are available on the Daily Snow Viewer.

Snow water equivalent

This overlay shows daily SWE in centimeters on the date indicated on the image, based on snow station data.

Change in snow water equivalent

This overlay shows changes in daily snow water equivalent (SWE) in centimeters for the last 24 hours, based on snow station data.

Percentage of median snow water equivalent

This overlay gives an estimate of 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 5 years of data to make this calculation.

Regions

• Western US with state, HUC2, and HUC4 subregions
• New Zealand - no subregions at this time
• Alaska (planned future addition)
• Western Canada (planned future addition)
• Chile (planned future addition)
• European Alps (planned future addition)

Submit requests for other areas

If an area you are interested in is not listed above, you can submit a request for other areas to be processed by emailing NSIDC User Services at nsidc@nsidc.org with the following information:

• Subject: Request for processing of new Snow Today area
• Body: Provide the MODIS tile number, lat/lon, and name for the area you wish to have processed.

Algorithms

SPIReS

The Snow Property Inversion from Remote Sensing (SPIReS) algorithm is a physically based model for estimating snow cover and snow albedo. It improves on previous models by solving for grain size and concentration of light-absorbing impurities simultaneously and uses a background reflectance along with snow endmembers. The algorithm integrates cloud-masking with a machine-based learning approach. For more, see Bair et al. (2021) in the References section.

INSTAAR SPIReS v1

From March 2024 to December 2025, data from this algorithm were displayed in Snow Today. The original SPIReS algorithm which was designed to run only after the snow season was complete was updated to run in near real time. SPIReS v1 was updated to combine snow grain size and dust concentration as well as to add snow radiative forcing using the dust concentration and theoretical clear sky solar radiation estimates. The code was parallelized more efficiently for high-performance computing to allow rapid processing after receiving observations.

INSTAAR SPIReS v2

Data from December 2025 to present are displayed in Snow Today from this algorithm. The new version 1) accounts for changes in non-snow surfaces from year to year such as might occur with a fire or drought, 2) adds false positive detection such as from dry lake bed (playa) or clouds using elevation dependent detection, 3) modifies our cloud persistence filter to increase performance of snow detection in near real time, and 4) removes the baseline elevation mask of 500 m to allow snow detection at any elevation. These improvements will allow implementation for other cloudy regions with snow elevation reaching the sea-level. This version also adds functionality to run VIIRS data from NOAA satellites such as VIIRS Suomi, NOAA-20, NOAA-21, and from future VIIRS JPSS-3 and JPSS-4.

DRFS

The Dust Radiative Forcing in Snow (DRFS) algorithm determines the spectral reflectance differences between measured and modeled clean snow of the same grain size. The algorithm uses these differences along with incoming solar irradiance to estimate snow radiative forcing. MODIS based MOD09GA surface reflectance used in the MODDRFS algorithm has been processed by the Jet Propulsion Laboratory for 2000 to 2023 and by the NSIDC DAAC starting in 2024. For more, see Painter et al. (2012) in the References section. Data from this algorithm have been used in Snow Today from 2020 through 2023.

SCAG

The Snow Cover And Grain (SCAG) size algorithm is a physically based model for estimating snow cover percent and snow grain size for clear sky surface reflectance using surface reflectance. MODIS based MOD09GA surface reflectance used in the MODSCAG algorithm has been processed by the Jet Propulsion Laboratory for 2000 to 2023 and by the NSIDC DAAC starting in 2024. For more detail see Painter et al. (2009) in the References section. Data from this algorithm has been used in Snow Today from 2020 through 2023.

SCGDRFS

Starting 2024, the NSIDC DAAC began running DRFS and SCAG as a combined algorithm dubbed SCGDRFS to produce data from MODIS Terra with plans to transition to VIIRS NOAA-20 in 2026. Data is created each day for portions of North America, New Zealand, and Chile with plans to add additional tiles for VIIRS.

STC-SCAG/DRFS

Spatially and Temporally Complete (STC) SCAG/DRFS data applies temporal and spatial interpolation to SCAG and DRFS data. The algorithm accounts for off-nadir viewing by weighting observations by satellite view angle. Viewable snow cover from the satellite is corrected to better estimate snow on the ground using vegetation height and concurrently measured viewable vegetation fraction. For more detail see Rittger et al., 2020, in the References section. Data from this algorithm have been used in Snow Today from 2020 through 2023.

Data sources

Moderate Resolution Imaging Spectroradiometer (MODIS) data

The primary input for the algorithms used on Snow Today to create the remotely sensed snow surface properties are surface reflectance from MODIS Terra. Historical reflectance data are from the LPDAAC while near-real-time data are from MODAPS/LANCE.

Visible Infrared Imagining Radiometer Suite (VIIRS) data

We plan on adding data from VIIRS Suomi-NPP and/or VIIRS NOAA-20 beginning in 2026. We plan to use historical reflectance data from the LPDAAC and near real time data from MODAPS.

Snow station data

Snow station data in the United States 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 available from the National Water and Climate Center.

In 2026, we plan to add western regions of Canada. Snow station data is sourced from stations operated by the Ministry of Environment, BC Hydro, Rio Tinto Alcan, and Metro Vancouver. They are publicly accessed from the British Columbia Snow survey data site.

Acknowledgements

The Snow Today website founded by Karl Rittger and Mark Raleigh 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. Major contributors to the site in 2026 led by Karl Rittger include Logan Stephenson, Alan Bourgeois, Ross Palomaki, Ann Windnagel, Agnieszka Gautier, Michon Scott, and Mark Serreze.

Snow Today is dedicated to Jeff Dozier for his contributions to the remote sensing of snow using multispectral sensors.

This work uses 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.

Funding

Support for this webpage and data has come from several sources including NASA Terrestrial Hydrology program (80NSSC18K0427), NASA Terra Aqua Suomi program (80NSSC22K0703), NASA Water Resources (80NSSC22K0929), NASA US Participating Investigator program (80NSSC230793), and USBR Cooperative Agreement (AWD-24-02-003). The NSIDC DAAC supported the transition of the Snow Today website when the NSIDC website infrastructure was updated institution-wide.

Glossary of terms

Days since last observation

The number of calendar days that have elapsed between the date of the last clear-sky observation without any data errors in the MOD09GA surface reflectance input and the present date.

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 summaries for HUC2 and HUC4 subregions with potential to include HUC6 and HUC8 or user requested basins in future updates.

HydroSHEDS-HydroBASINS

This global vectorized dataset, HydroBASINS, is a hierarchically nested set of subbasins consistently sized at different scales from tens to millions of kilometers. It is derived from 90-meter digital elevation models below 60 degrees latitude and from coarser 1-kilometer resolution above 60 degrees latitude. Snow Today utilizes this data set for regions outside of the United States and may display different levels of the hierarchy depending on the region and average basin scale.

Surface reflectance (e.g. MOD09GA or VNP09GA)

Surface spectral reflectance of Terra MODIS visible, near-infrared, and shortwave infrared wavelengths corrected for atmospheric conditions such as gases, aerosols, and Rayleigh scattering. These data are used as input to our snow surface property algorithms. Snow Today plans on including data from Suomi VIIRS in the future.

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 duration

The number of days a region has been covered with snow, identified with snow cover percent greater than a specific snow cover threshold since a specific starting time. In the Daily Snow Viewer maps we use a region-specific start date. For example, the water year in the Western United States uses October 1 as the starting date. The start date for each region can be viewed in the Snow Viewer on the x-axis of the plots after selecting that region.

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 500-meter spatial resolution and plan on using VIIRS data at similar spatial resolution in the future. 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. Typically for snow cover this threshold is used to lower false positives which occur more abundantly below the threshold than true positives.

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 references

Rittger, K., L. Stephenson, R.T. Palomaki, E.H. Bair, J. Dozier, and S.J. Lenard. 2026. Near Real-Time MODIS/Terra L3 Global Daily 500m SIN Grid Snow Cover, Snow Albedo, and Snow Surface Properties. (SPIRES_NRT, Version 2). [Data Set]. Boulder, Colorado USA. National Snow and Ice Data Center, doi: 10.7265/vnp4-1423.

Rittger, K., S.J. Lenard, L. Stephenson, R.T. Palomaki, E.H. Bair, and J. Dozier. 2025. Near Real-Time MODIS/Terra L3 Global Daily 500m SIN Grid Snow Cover, Snow Albedo, and Snow Surface Properties. (SPIRES_NRT, Version 1). [Data Set]. Boulder, Colorado USA. National Snow and Ice Data Center, doi: 10.7265/hs6b-zg21.

Rittger, K., S.J. Lenard, R.T. Palomaki, E.H. Bair, J. Dozier, and K. Mankoff. 2025. Historical MODIS/Terra L3 Global Daily 500m SIN Grid Snow Cover, Snow Albedo, and Snow Surface Properties. (SPIRES_HIST, Version 1). [Data Set]. Boulder, Colorado USA. National Snow and Ice Data Center, doi: 10.7265/a3vr-c014.

Rittger, K., M.J. Brodzik, M.A. Hardman, H. Wilcox, D.J. Scott, and T. Painter. 2024. Near Real-Time MODIS/Terra L3 Global Daily 500m SIN Grid Snow Cover, Grain Size, and Dust Radiative Forcing. (MODSCGDRF_NRT, Version 1). [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center, doi: 10.5067/HNVFRD5ICZN1.

Rittger, K., S.J. Lenard, R.T. Palomaki, M.J. Brodzik, T. Stillinger, E.H. Bair, J. Dozier, and T.H. Painter. 2024. Historical MODIS/Terra L3 Global Daily 500m SIN Grid Snow Cover, Snow Albedo, and Snow Physical Properties. (STC_MODSCGDRF_HIST, Version 1). [Data Set]. Boulder, Colorado USA. National Snow and Ice Data Center, doi: 10.7265/f6j3-f387.

Rittger, K., S.J. Lenard, R.T. Palomaki, M.J. Brodzik, T. Stillinger, E.H. Bair, J. Dozier, and T.H. Painter. 2024. Historical MODIS/Terra L3 Global Daily 500m SIN Grid Snow Cover, Snow Albedo, and Snow Physical Properties. (STC_MODSCGDRF_HIST, Version 1). [Data Set]. Boulder, Colorado USA. National Snow and Ice Data Center, doi: 10.7265/f6j3-f387.

References

These references are listed first in reverse chronological order and then in alphabetical order.

Palomaki, R.T., K. Rittger, S.J.P. Lenard, E. Bair, J. Dozier, S.M. Skiles, and T.H. Painter. 2025. Assessment of methods for mapping snow albedo from MODIS. Remote Sensing of Environment 326: 114742, doi: 10.1016/j.rse.2025.114742.

Jensen, A.S., K. Rittger, and M.S. Raleigh. 2024. Spatio-temporal patterns and trends in MODIS-retrieved radiative forcing by snow impurities over the Western US from 2001 to 2022. Environmental Research: Climate 3: 025001, doi: 10.1088/2752-5295/ad285a.

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.

Bair, E.H., T. Stillinger, and J. Dozier. 2021. Snow property inversion from remote sensing (SPIReS): A generalized multispectral unmixing approach with examples from MODIS and Landsat 8 OLI. IEEE Transactions on Geoscience and Remote Sensing 59: 7270-7284, doi: 10.1109/TGRS.2020.3040328.

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.

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.

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.

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

This section lists published research using Snow Today data products for applications ranging from streamflow forecasts to Sierra Bighorn mortality to large-scale climate modeling, and more. These references are listed first in reverse chronological order and then in alphabetical order.

Abolafia-Rosenzweig, R., C. He, C. Liu, C., T.-S. Lin, D. Mocko, K. Rittger, W. Rudisill, Y. Cheng, M. Barlage, R. Palomaki, J.W. Wegiel, and S.V. Kumar. 2025. Snow cover plays a non-dominant role in WRF/Noah-MP simulated surface air temperature cold biases over the western U.S. Journal of Geophysical Research: Atmospheres 130(22): e2025JD044191, doi: 10.1029/2025JD044191 

Abolafia-Rosenzweig, R., C. He, T.-S. Lin, M. Barlage, and K. Rittger. 2025. Improved cross-scale snow cover simulations by developing a scale-aware ground snow cover fraction parameterization in the Noah-MP Land Surface Model. Journal of Advances in Modeling Earth Systems 17(6): e2024MS004704, doi: 10.1029/2024MS004704.

Bair, E. H., D.A. Roberts, D.R. Thompson, P.G. Brodrick, B.A. Wilder, N. Bohn, C.J. Crawford, N. Carmon, C.M. Vuyovich, and J. Dozier. 2025. Brief communication: Not as dirty as they look, flawed airborne and satellite snow spectra. The Cryosphere 19(6): 2315-2320, doi: 10.5194/tc-19-2315-2025. 

Chandel, A.S., C. Sarangi, K. Rittger, R.K. Hooda, and A.-P. Hyvärinen. 2025. In situ characterization of dust storms and their snow-darkening effect over Himalayas. Journal of Geophysical Research: Atmospheres 130(2): e2024JD041874, doi: 10.1029/2024JD041874. 

Koshkin, A., A.M. Marshall, and K. Rittger. 2025. Impact of current and warmer climate conditions on snow cover loss in burned forests. Science Advances 11(38): eadt9866. doi: 10.1126/sciadv.adt9866. 

Lang, O.I., P. Naple, D. Mallia, T. Hosler, B. Adams, and S. McKenzie Skiles. 2025. Two decades of dust radiative forcing on snow cover across the Great Salt Lake Basin. Journal of Geophysical Research: Earth Surface 130(2): e2024JF007957, doi: 10.1029/2024JF007957. 

Naple, P., S.M. Skiles, O.I. Lang, K. Rittger, S.J.P. Lenard, A. Burgess, and T.H. Painter. 2025. Dust on snow radiative forcing and contribution to melt in the Colorado River Basin. Geophysical Research Letters 52(5): e2024GL112757, doi: 10.1029/2024GL112757.

Raleigh, M.S., E.E. Small, E.H. Bair, C. Wobus, and K. Rittger. 2025. Snow monitoring at strategic locations improves water supply forecasting more than basin-wide mapping. Communications Earth & Environment 6(1): 665, doi: 10.1038/s43247-025-02660-z.

Tarricone, J., R. Palomaki, K. Rittger, A. Nolin, H.-P. Marshall, and C. Vuyovich. 2025. Investigating the impact of optical snow cover data on L-band InSAR snow water equivalent retrievals. Journal of Remote Sensing 5: 0682, doi:1 0.34133/remotesensing.0682. 

Fleming, S.W., K. Rittger, C.M. Oaida Taglialatela, and I. Graczyk. 2024. Leveraging next-generation satellite remote sensing-based snow data to improve seasonal water supply predictions in a practical machine learning-driven river forecast system. Water Resources Research 60(4): e2023WR035785, doi: 10.1029/2023WR035785. 

Gayler, J.M., and S.M. Skiles. 2024. Response of land surface albedo to fire disturbance in the Sierra Nevada seasonal snow zone over the MODIS record. Earth's Future 12(6): e2023EF004172, doi: 10.1029/2023EF004172 

Mahanthege, S., W. Kleiber, K. Rittger, B. Rajagopalan, M.J. Brodzik, and E. Bair. 2024. A spatially-distributed machine learning approach for fractional snow covered area estimation. Water Resources Research 60(11): e2023WR036162, doi: 10.1029/2023WR036162. 

Meyer, J., A. Hedrick, and S. McKenzie Skiles. 2024. A new approach to net solar radiation in a spatially distributed snow energy balance model to improve snowmelt timing. Journal of Hydrology 638: 131490, doi: 10.1016/j.jhydrol.2024.131490. 

Bair, E.H., J. Dozier, K. Rittger, T. Stillinger, W. Kleiber, and R.E. Davis. 2023. How do tradeoffs in satellite spatial and temporal resolution impact snow water equivalent reconstruction? The Cryosphere 17: 2629-2643, doi: 10.5194/tc-17-2629-2023.

Feldman, D.R., A.C. Aiken, W.R. Boos, R.W.H. Carroll, V. Chandrasekar, S. Collis, J.M. Creamean, G. de Boer, J. Deems, P.J. DeMott, J. Fan, A.N. Flores, D. Gochis, M. Grover, T.C.J. Hill, A. Hodshire, E. Hulm, C.C. Hume, R. Jackson, F. Junyent, A. Kennedy, M. Kumjian, E.J.T. Levin, J.D. Lundquist, J. O’Brien, M.S. Raleigh, J. Reithel, A. Rhoades, K. Rittger, W. Rudisill, Z. Sherman, E. Siirila-Woodburn, S.M. Skiles, J.N. Smith, R.C. Sullivan, A. Theisen, M. Tuftedal, A.C. Varble, A. Wiedlea, S. Wielandt, K. Williams, and Z. Xu. 2023. The Surface Atmosphere Integrated Field Laboratory (SAIL) campaign. Bulletin of the American Meteorological Society doi: 10.1175/BAMS-D-22-0049.1.

Hao, D., G. Bisht, K. Rittger, T. Stillinger, E. Bair, Y. Gu, and L.R. Leung. 2023. Evaluation of E3SM land model snow simulations over the western United States. The Cryosphere 17: 673-697, doi: 10.5194/tc-17-673-2023.

Hatchett, B.J., A.L. Koshkin, K. Guirguis, K. Rittger, A.W. Nolin, A. Heggli, A.M. Rhoades, A.E. East, E.R. Siirila-Woodburn, W.T. Brandt, A. Gershunov, and K. Haleakala. 2023. Midwinter dry spells amplify post-fire snowpack decline. Geophysical Research Letters 50: e2022GL101235, doi: 10.1029/2022GL101235.

McGrath, D., L. Zeller, R. Bonnell, W. Reis, S. Kampf, K. Williams, M. Okal, A. Olsen-Mikitowicz, E. Bump, M. Sears, and K. Rittger. 2023. Declines in peak snow water equivalent and elevated snowmelt rates following the 2020 Cameron Peak Wildfire in northern Colorado. Geophysical Research Letters 50: e2022GL101294, doi: 10.1029/2022GL101294.

Yang, K., K. Rittger, K.N. Musselman, E.H. Bair, J. Dozier, S.A. Margulis, T.H. Painter, and N.P. Molotch. 2023. Intercomparison of snow water equivalent products in the Sierra Nevada California using airborne snow observatory data and ground observations. Frontiers in Earth Science 11, doi: 10.3389/feart.2023.1106621.

Berger, D.J., D.W. German, C. John, R. Hart, T.R. Stephenson, and T. Avgar. 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.

Hao, D., G. Bisht, C. He, E. Bair, H. Huang, C. Dang, K. Rittger, Y. Gu, H.Wang, Y. Qian, and L.R. Leung. 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. Geoscientific Model Development Discussions doi: 10.5194/gmd-2022-67.

Huang, H., Y. Qian, C. He, E.H. Bair, and K. Rittger. 2022. Snow albedo feedbacks enhance snow impurity-induced radiative forcing in the Sierra Nevada. Geophysical Research Letters 49: e2022GL098102, doi: 10.1029/2022GL098102.

Ackroyd, C., S.M. Skiles, K. Rittger, and J. Meyer. 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.

Bair, E., T. Stillinger, K. Rittger, and M. Skiles. 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.

Micheletty, P., D. Perrot, G. Day, and K. Rittger. 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 doi: 10.1111/1752-1688.12975.

Rittger, K., M. Krock, W. Kleiber, E.H. Bair, M.J. Brodzik, T.R. Stephenson, B. Rajagopalan, K.J. Bormann, T.H. Painter. 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., K.N. Musselman, K. Rittger, S.A. Margulis, T.H. Painter, and N.P. Molotch. 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.

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

Hill, A. F., K. Rittger, T. Dendup, D. Tshering, and T.H. Painter. 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.

Khan, A., K. Rittger, P. Xian, J.J. Katich, R.L. Armstrong, R. Kayastha, J. Dana, D.M. McKnight. 2020. Biofuel burning influences refractory black carbon concentrations in seasonal snow at lower elevation of the Dudh Koshi River basin of Nepal. Frontiers in Earth Science doi: 10.3389/feart.2020.00371.

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

Zhao, H., X. Hao, J. Wang, H. Li, G. Huang, S. Donghang, B. Su, L. Huajin, and X. Hu. 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.

Armstrong, R.L., K. Rittger, M.J. Brodzik, A. Racoviteanu, A.P. Barrett, S.J.S. Khalsa, B. Raup, A.F. Hill, A.L. Khan, A.M. Wilson, R.B. Kayastha, F. Fetterer, and B. Armstrong. 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.

Bair, E.H., A. Abreu Calfa, K. Rittger, and J. Dozier. 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.

Hill, A.F., R.F. Stallard, K. Rittger. 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, doi: 10.1525/elementa.290.

Painter, T.H., S.M. Skiles, J.S. Deems, W.T. Brandt, and J. Dozier. 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.

Bair, E.H., K. Rittger, R.E. Davis, T.H. Painter, and J. Dozier. 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.

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

Micheletty, P.D., A.M. Kinoshita, and T.S. Hogue. 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., T.H. Painter, J.S. Deems, A.C. Bryant, and C.C. Landry. 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.

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, USGS Imagery, USGS Shaded Relief, and 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. https://www.sciencebase.gov/catalog/item/581d051fe4b08da350d523ee

ArcGIS Dark Gray and 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).