This map is a collection of official reports from around West Texas and SE New Mexico on total snowfall received over the course of the winter storm on February 4th and 5th, 2020. Pictures provided are from local residents and the general public. Each point provides the snowfall total, picture, and location. This map is used in conjuncture with the Winter Storm story map, found here. If you would like to view only the web map with the snowfall totals, you can find that here.

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.

Snow residence time (in days) and April 1 snow water equivalent (in mm) were modeled using the spatial analog models of Luce et al., 2014 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844); see also Lute and Luce, 2017 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from the TopoWx dataset (https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.4127).

Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

This map displays the expected total accumulation of new snow over the next 72 hours across the contiguous United States. Data are updated hourly from the National Digital Forecast Database produced by the National Weather Service.The dataset includes incremental and cumulative snowfall data in 6-hour intervals. In the ArcGIS Online map viewer you can enable the time animation feature and select either the amount by time (incremental) or accumulation by time (cumulative) layers to view a 72-hour animation of forecast precipitation. All times are reported according to your local time zone.Where is the data coming from?The National Digital Forecast Database (NDFD) was designed to provide access to weather forecasts in digital form from a central location. The NDFD produces gridded forecasts of sensible weather elements. NDFD contains a seamless mosaic of digital forecasts from National Weather Service (NWS) field offices working in collaboration with the National Centers for Environmental Prediction (NCEP). All of these organizations are under the administration of the National Oceanic and Atmospheric Administration (NOAA).Source: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.conus/VP.001-003/ds.snow.binWhere can I find other NDFD data?The Source data is downloaded and parsed using the Aggregated Live Feeds methodology to return information that can be served through ArcGIS Server as a map service or used to update Hosted Feature Services in Online or Enterprise.What can you do with this layer?This map service is suitable for data discovery and visualization. Identify features by clicking on the map to reveal the pre-configured pop-ups. View the time-enabled data using the time slider by Enabling Time Animation.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

How much snow can you expect during the next snow storm?

This web mapping application is designed to answer the question "When, where, and how much snow will accumulate in the next two days?". This map contains snowfall predictions from for the United States as indicated by the legend. You can navigate this map by panning and zooming, using the places button, or go to your location by clicking on this feature in the upper left corner of the app. The source of this information is the National Weather Service National Digital Forecast Database, but here we have published a time-enabled map service for you to interact with. See the NDFD website for more details.

Contained within the 3rd Edition (1957) of the Atlas of Canada is a map that shows the snow cover data, referring primarily to the presence and total depth of a snow cover on the surface of the earth, across Canada. This is in contrast to data characteristics of snow cover depth, which increases by the occurrence of freshly fallen snow, but decreases by melting, wind action and settling. Two maps of these maps show the mean dates of the occurrence of first and last snow covers by one inch (2.54 cm) or greater. These are not necessarily the average dates to the beginning and ending of a continuous snow cover, since the snow cover may form and later disappear once or several times during a winter season. A third map showing the mean annual number of days with a snow cover of one inch (2.54 cm) or greater, only includes those days on which there was a snow cover. For the last map, the mean annual maximum depth of snow data was obtained by averaging the maximum depth reported for each snow season record. Snow cover data is mainly based on the ten-year period from 1941 to 1950.

(2) binary snow maps derived from the land cover maps

Over southern Canada maximum snow depth usually occurs in January or February, while the time of maximum accumulation occurs much later in mountain areas and in the Arctic. The main features of the map are the pronounced maximum in snow accumulation over the western Cordillera (British Columbia and Yukon), where snow depths can exceed several metres, with a secondary maximum over Quebec and Labrador. These maxima are related to their proximity to oceans, which acts as sources of moisture and winter storms, and to the orographic effect of the mountains in the case of western Canada. The two maxima are linked by a band of higher snow accumulation that follows the boreal forest zone; this is a preferred track for winter storms. To the north of this zone is the relatively shallow snow cover of the Arctic (low snowfall with extensive wind packing). To the south, the depth of snow is limited by the shorter accumulation season and the substantial sublimation of snow over the Canadian Prairies. An inset map shows the average maximum snow depth where it is deepest in Canada: central Vancouver Island (British Columbia). A second inset shows the distribution of Canada's daily snow depth station network, 1997.

The daily gridded, snow and ice coverage map is a blended product generated by the Automated Snow and Ice Mapping System (GMASI). The map is produced from Level-1 observations obtained by the AVHRR, AMSR2, and GMI instruments, onboard the MetOp-B, MetOp-C, GCOM-W, and GPM satellites. GMASI was developed to facilitate the operational monitoring of snow and ice cover and to provide Cryosphere status information to Numerical Weather Prediction (NWP) models. The system employs a completely automated data processing algorithm to infer the distribution of global snow and ice cover from satellite observations. The retrieval algorithm utilizes combined observations from the visible/infrared and microwave spectral bands. The daily snow map provides a continuous (gap free) characterization of global snow and ice cover generated on a latitude-longitude grid (Plate Carree) with a resolution of 0.02 degrees (or about 2 km grid cell size). The data are produced by the NOAA Environmental Satellite, Data, and Information Service (NESDIS) and are distributed by the Comprehensive Large Array-Data Stewardship System (CLASS) as daily gridded files in netCDF-4 file format with attributes included.

The BOREAS RSS-08 team utilized Landsat TM images to perform mapping of snow extent over the SSA. This data set consists of two Landsat TM images which were used to determine the snow-covered pixels over the BOREAS SSA on 18-Jan-1993 and on 06-Feb-1994. Companion files include example thumbnail images that may be viewed using a convenient viewer utility.

The snow risk refers to the consequences induced by permanent snowfall on the ground in quantities such as to generate difficulties in the activities ordinarily carried out by the population, slowdowns and interruptions of public and private transport and service lines (electricity, water, gas, telecommunications, etc..) as well as damage to structures.

The map shows isolines on four different dates: 1) the day of the year when there are less than or equal to 2.5 centimetres of snow that occurs and remain absent for 7 or more days; 2) the standard deviation of the mean date of snow cover loss; 3) the day of year when greater than or equal to 2.5 centimetres of snow occur and remain for more than 7 days; and 4) the standard deviation of the mean date of snow cover formation. The beginning and end of the winter season in Canada is usually identified with the formation and disappearance of snow cover in autumn and spring respectively. The duration of snow cover varies considerably from year to year in the southern fringes of the country and adjacent to the east and west coasts. However, even in northern continental areas early and late snowfalls which leave an ephemeral snow cover are common. The beginning and end of the Canadian snow season is the earliest and latest dates when 2.5 centimetres or more snow was measured. However, this definition is difficult to apply to the Prairie Provinces, where snow cover is relatively shallow and is subjected to sudden melting and new accumulations at the season extremities. For the Prairie Provinces, the definition can be modified by specifying that the initial cover of 2.5 centimetres (or more) should remain for at least 7 days, and that the date of loss would occur at the beginning of a 7-day period with less than 2.5 centimetres on the ground in spring. This definition was adopted for the present map. Snow cover data have been regularly measured at principal meteorological stations since 1941. The present map is based on the 1955-1972 period. Unfortunately, records prior to 1955 are not available in the form required for the selected computer analysis. To generate data for the map, a total file of over 500 meteorological station records was accessed and the means, standard deviations, and other arithmetic data were calculated. The mean dates of formation and loss and their standard deviations were plotted on a 1 : 5 000 000 scale orographic base map to guide isopleth interpolation.

Snow depth data series contain records obtained by high frequency GPR on selected glaciers of Hornsund area (S Svalbard) since 2008. Currently the largest collection regards Hansbreen. Data for other glaciers are successively appended. The GPR survey on Hansbreen is regularly carried out approximately along the same tracks. Due to dynamically changing glacier surface topography influencing different survey abilities the some parts of profiles are modified in consecutive seasons. The total distance of Hansbreen profiles are as follows (Fig.1): 63.9 km (2008), 117,5 km (2011), 105,1 km (2013), 103,9 km (2014), 98,5 km (2015), 91,1 km (2016), 101,0 km (2017) and 108,4 km (2018).

The map is derived from a compilation of field measurements. Satellite observations from AMSR-E and AVHRR instruments are used to guide the interpolation. The effective resolution of the map is approximately 100 km. The estimates of root mean square percentage error apply to regional averages at scales of around 100 km by 100 km. On smaller scales, additional deviations of 30% r.m.s. are likely. Values for locations subject to melt may be unreliable. Units are (kg/m2/a), or (mm/a) water equivalent.

This map shows the average maximum snow depth in centimetres computed over 18 winter seasons (1979 to 1997). Over southern Canada this usually occurs in January or February, while the time of maximum accumulation occurs much later in mountain areas and in the Arctic. The main features of the map are the pronounced maximum in snow accumulation over the western Cordillera, where snow depths can exceed several metres, with a secondary maximum over Quebec and Labrador. These maxima are related to their proximity to oceans, which act as sources of moisture and winter storms, and to the orographic effect of the mountains in the case of western Canada. The two maxima are linked by a band of higher snow accumulation that follows the boreal forest zone; this is a preferred track for winter storms. To the north of this zone is the relatively shallow snow cover of the Arctic (low snowfall with extensive wind packing). To the south, the depth of snow is limited by the shorter accumulation season and the substantial sublimation of snow over the Canadian Prairies.

This data set contains (at least) weekly maps of snow depth observations collected by the North Central River Forecast Center (NCRFC). The data are over the Upper Mississippi River basin area.

This data contains location information for 1 of Ontario’s snow monitoring networks:Surface Water Monitoring Centre (SWMC)Snow course data is collected by:conservation authoritiesOntario Power GenerationMinistry of Natural Resources (MNR) districtsData is collected twice a month from November 15 until May 15. The Surface Water Monitoring Centre uses this data to assess:current snow coverfrozen ground conditionssnowpackpotential snowmeltcontributions to streamflowThe snow data is located in a corporate water and climate database. This data helps MNR and conservation authorities assess the potential for flood at the local and provincial scale.Additional DocumentationOntario Snow Survey location and data - Data Dictionary (Excel)Historic and Current Snow Survey Metadata (1933-2024) (CSV) StatusPlanned: fixed date has been established upon or by which the data will be created or updatedMaintenance and Update FrequencyAnnually: data is updated every yearContactSurface Water Monitoring Centre, surface.water@ontario.ca

This AutoSnow product provides daily, gridded, snow and ice coverage maps for the Northern and Southern Hemispheres. The maps are produced from Level-1 observations obtained by the AVHRR, AMSU, GMI, and GMS instruments, onboard MetOp, GOES, and Meteosat satellites. The daily snow map provides a continuous (gap free) characterization of hemispheric snow and ice cover generated on a latitude-longitude grid at a 4 km resolution. The data are produced by the NOAA Environmental Satellite, Data, and Information Service (NESDIS) and are distributed by the Comprehensive Large Array-Data Stewardship System (CLASS) as daily gridded files in the GRIB format.

Abstract:Rain on Snow is a statewide coverage of rain-on-snow zones. Rain-on-snow zones are based on average amounts of snow on the ground in early January, relative to the amount of snow that could reasonably be melted during a model storm event. Five Rain on Snow zones are defined in Washington State and are based on climate, elevation, latitude, and vegetation. Rain on Snow was digitized from 1:250,000 USGS quads.Purpose:The Rain-on-snow coverage was created as a screening tool to identify forest practice applications that may be in a significant rain-on-snow zone (WAC 222-22-100).Description:Five ROS zones are defined in Washington State and are based on climate, elevation, latitude, and vegetation. Rain on snow is a process that exhibits spatial and temporal variation under natural conditions, with the effects of vegetation on snow accumulation and melt adding additional complications in prediction. There is no map that shows the magnitude and frequency of water inputs to be expected from rain on snow events, so we have attempted to create an index map based on what we know about the process controls and their effects in the various climatic zones. If we assume that, averaged over many years, the seasonal storm tracks that bring warm, wet cyclonic storms to the Northwest have access to all parts of Washington , then the main factors controlling and/or reflecting the occurrence and magnitude of a R/S event in any particular place are:1) Climatic region: especially the differences between windward and leeward sides of major mountain ranges, which control seasonal climatic patterns;2) Elevation: controls temperature, thus the likelihood and amount of snow on the ground, and affects orographic enhancement of storm precipitation; 3) Latitude: affects temperature, thus snow;4) Aspect: affects insolation and temperature (especially in winter), thus melting of snow; 5) Vegetation: the species composing forest communities can reflect the climate of an area (tolerance of warmth or cold, wet or dry conditions, deep and/or long lived snowpacks); the height and density of vegetation also partly controls the amount of snow on the ground. As natural vegetation integrates the effects of all of these controls, we tried to find or adapt floral indicators of the various zones of water input. We designed the precipitation zones to reflect the amount of snow likely to be on the ground at the beginning of a storm. We assumed that some middle elevation area would experience the greatest water input due to Rain on Snow, because the amount of snow available would be likely to be approximately the amount that could be melted. Higher and lower elevation zones would bear diminished effects, but for opposite reasons (no snow to melt, vs too cold to melt much). These considerations suggested a three or five zone system. We chose to designate five zones because a larger number of classes reduces the importance of the dividing lines, and thus of the inherent uncertainties of those lines. The average snow water equivalents (SWE) for the early January measurements at about 100 snow courses and snow pillows were compiled; snow depths for the first week in January at about 85 weather stations were converted into SWE. For each region (western North Cascades, Blue Mountains, etc.), the snow amounts were sorted by station elevation to derive a rough indicator of the relationship between snow accumulation and elevation. (Sub regional differences in snow accumulation patterns were also recognized.) After trying various combinations of ratios for areas where the snow hydrology is relatively well known, we adopted the following designations: 5. Highlands: >4 5 times ideal SWE; high elevation, with little likelihood of significant water input to the ground during storms (precipitation likely to be snow, and liquid water probably refreezes in a deep snow pack); effects of harvest on snow accumulation are minor; 4. Snow dominated zone: from "1.25 1.5 ideal SWE, up to "4; melt occurs during R/S (especially during early season storms), but effects can be mitigated by the lag time of percolation through the snowpack; 3. Peak rain on snow zone: "0.5 0.75 up to "1.25 ideal SWE; middle elevations: shallow snow packs are common in winter, so likelihood and effects of R/S in heavy rainstorms are greatest; typically more snow accumulation in clearings than in forest; 2. Rain dominated zone: "0.1 0.5 ideal SWE; areas at lower elevations, where rain occasionally falls on small amounts of snow; 1. Lowlands: <0.1 ideal SWE; coastal, low elevation, and rain shadow areas; lower rainfall intensities, and significant snow depths are rare. Precipitation zones were mapped on mylar overlays on 1:250,000 scale topographic maps. Because snow depth is affected by many factors, the correlation between snow and elevation is crude, and it was not possible to simply pick out contour markers for the boundaries. Ranges of elevations were chosen for each region, but allowance was made for the effects of sub regional climates, aspect, vegetative indicators of snow depth, etc. Thus, a particular boundary would be mapped somewhat lower on the north side of a ridge or in a cool valley (e.g. below a glacier), reflecting greater snow accumulations in such places. The same boundary would be mapped higher on the south side of the ridge, where inter-storm sunshine could reduce snow accumulation. Conditions at the weather stations and snow courses were used to check the mapping; but in areas where measurements are scarce, interpolation had to be performed. The boundaries of the precipitation zones were entered in the DNR's GIS. Because of the small scale of the original mapping and the imprecision of the digitizing process, some errors were introduced. It should not be expected that GIS images can be projected to large scales to define knife edge zone boundaries (which don't exist, anyway), but they are good enough to locate areas tens of acres in size. Some apparent anomalies in the map require explanation. Much of western Washington is mapped in the lowland or highland zones. This does not mean that R/S does not occur in those areas; it does, but on average with less frequency and hydrologic significance than in the middle three zones. Most of central and eastern Washington is mapped in the rain dominated zone, despite meager precipitation there; this means only that the amount of snow likely to be on the ground is small, and storm water inputs are composed dominantly of the rain itself, without much contribution from snow melt. Much of northeastern Washington is mapped in the peak Rain Snow zone, despite the fact that such events are less common there than in western Washington. This is due to the fact that there is less increase in snow depth with elevation (i.e. the snow wedge is less steep), so a wider elevation band has appropriate snow amounts; plus, much of that region lies within that elevation band where the 'ideal' amount of snow is liable to be on the ground when a model Rain Snow event occurs. This does not reflect the lower frequency of such storms in that area.

This is the updated snow phase map

These snow depth raster maps were generated from digital elevation models (DEMs) derived from light detection and ranging (lidar) data collected during multiple field campaigns in the three study areas near Winter Park, Colorado. Small, uncrewed aircraft systems (sUAS) collected lidar datasets to represent snow-covered and snow-free periods. More information regarding the sUAS used and data collection methods can be found in the Supplemental Information and process step sections of each study area individual metadata file.