Understanding snow conditions is key to developing a better understanding of hydrologic, biological, and ecosystem processes at work in northern Alaska, but these data currently do not exist at spatial or temporal scales needed by end users. To address this need, the Arctic LCC and Alaska Climate Science Center have partnered with researchers from Colorado State University to produce retrospective datasets simulating snow conditions for much of northern Alaska. The following snow products are provided: 1) First snow date 2) Last snow date 3) Snow free date 4) Snow up date 5) Total melt per day 6) Average 10m air temperature 7) Glacier melt 8) Total snow days 9) Snow depth 10) Snow density 11) Snow water equivalent depth 12) Solid precipitation 13) Rain on snow event 14) Rain precipitation 15) Total liquid water 16) Total precipitation 17) Snow days

Notice: Due to funding limitations, this data set was recently changed to a “Basic” Level of Service. Learn more about what this means for users and how you can share your story here: Level of Service Update for Data Products.This data set contains snow pack properties, such as depth and snow water equivalent (SWE), from the NOAA National Weather Service's National Operational Hydrologic Remote Sensing Center (NOHRSC) SNOw Data Assimilation System (SNODAS). SNODAS is a modeling and data assimilation system developed by NOHRSC to provide the best possible estimates of snow cover and associated parameters to support hydrologic modeling and analysis.

This data set contains snow observations (snow depth, snowfall, and snow water equivalent) from several networks (CA-Hydro, CoCoRaHS, US Bureau of Reclamation, Idaho Transportation Department, SNOTEL, CNRA, DRI, UUNET, BCHYDRO, RAWS and several avalanche centers) with over 6000 locations throughout the United States and Canada. The temporal resolution varies from 6 hourly to daily depending on the station. These data were quality controlled and provided by NOAA MADIS.

This dataset includes Rain On Snow Event(rose) for northern Alaska in GeoTiff format, covering the years 1980-2012. Rain On Snow Event is defined as number of days with rain on snow(days). The dataset was generated by the Arctic LCC SNOWDATA: Snow Datasets for Arctic Terrestrial Applications project.

The dataset is delivered in the ZIP archive file format. Each year is output in a separate GeoTiff file, where the year is indicated by the filename.

Over the last 20 years, under a variety of NOAA, NSF, and NASA research programs, a snow-evolution modeling system has been developed that includes the MicroMet micrometeorological model, the SnowModel snow-process model, and the SnowAssim data assimilation model. These modeling tools can be thought of as physically-based mathematical descriptions that create value-added information (e.g., snow depth, snow density, snow hardness, rain-on-snow events, and snow cover duration) from basic meteorological variables (e.g., air temperature, humidity, precipitation, and wind speed and direction). The resulting products are based on a physical understanding of environmental processes and features, and their interactions with the atmosphere and surrounding land surface. SnowModel is unique in its representation of blowing snow processes; it includes SnowTran-3D, a model developed initially for Arctic Alaska applications, and arguably the most widely used snow transport model in the world. The model formulations are general enough to allow simulations over temporal domains spanning years to decades, and spatial domains spanning from small watersheds to all of Alaska. MicroMet can use atmospheric forcing ranging from individual meteorological stations, to gridded atmospheric (re)analysis products, to climate change scenario datasets. SnowAssim is able to ingest snow data ranging from ground-based snow observations to remote-sensing data. This dataset is the result of using these meteorological- and snow-evolution models to ingest appropriate datasets and produce the required outputs. A total of 528 meteorological station sites obtained from the Imiq Database (http://arcticlcc.org/imiq) provided air temperature, relative humidity, and wind speed/direction. Daily met station data were converted to hourly using various sub-models, then all hourly data were aggregated to 3-hourly for the model simulations. Precipitation inputs were obtained from the NASA 3-hourly MERRA atmospheric reanalysis data set. Snow DATA outputs are at 2 km x 2 km resolution and cover all of mainland Alaska and portions of adjacent Yukon and Northwest territories north of 61.5° N, and west of 130.2° W. Snow Tran 3-D is not implemented in this instance, because grid cell size exceeds the scale at which wind transport is expected to operate.

Notice: If you are having difficulties subsetting SNODAS data via Polaris, please contact nsidc@nsidc.org. This data set contains output from the NOAA National Weather Service's National Operational Hydrologic Remote Sensing Center (NOHRSC) SNOw Data Assimilation System (SNODAS). SNODAS is a modeling and data assimilation system developed by NOHRSC to provide the best possible estimates of snow cover and associated parameters to support hydrologic modeling and analysis. The aim of SNODAS is to provide a physically consistent framework to integrate snow data from satellite, airborne platforms, and ground stations with model estimates of snow cover (Carroll et al. 2001). SNODAS includes procedures to ingest and downscale output from the Numerical Weather Prediction (NWP) models, and to simulate snowcover using a physically based, spatially-distributed energy- and mass-balance snow model. SNODAS also includes procedures to assimilate satellite-derived, airborne, and ground-based observations of snow covered area and Snow Water Equivalent (SWE).These data are not suitable for snow fall events or totals for specific regions. For snow fall data, please see the state climatology reports for a particular state. These are gridded data sets for the continental United States at 1 km spatial resolution and 24 hour temporal resolution. Data are stored in flat binary 16-bit signed integer big-endian format with header and metadata files, and are available from 1 October 2003 to present via FTP.

This dataset includes Solid Precipitation(spre) for northern Alaska in GeoTiff format, covering the years 1980-2012. Solid Precipitation is defined as snowfall(m/yr). The dataset was generated by the Arctic LCC SNOWDATA: Snow Datasets for Arctic Terrestrial Applications project.

The dataset is delivered in the ZIP archive file format. Each year is output in a separate GeoTiff file, where the year is indicated by the filename.

Over the last 20 years, under a variety of NOAA, NSF, and NASA research programs, a snow-evolution modeling system has been developed that includes the MicroMet micrometeorological model, the SnowModel snow-process model, and the SnowAssim data assimilation model. These modeling tools can be thought of as physically-based mathematical descriptions that create value-added information (e.g., snow depth, snow density, snow hardness, rain-on-snow events, and snow cover duration) from basic meteorological variables (e.g., air temperature, humidity, precipitation, and wind speed and direction). The resulting products are based on a physical understanding of environmental processes and features, and their interactions with the atmosphere and surrounding land surface. SnowModel is unique in its representation of blowing snow processes; it includes SnowTran-3D, a model developed initially for Arctic Alaska applications, and arguably the most widely used snow transport model in the world. The model formulations are general enough to allow simulations over temporal domains spanning years to decades, and spatial domains spanning from small watersheds to all of Alaska. MicroMet can use atmospheric forcing ranging from individual meteorological stations, to gridded atmospheric (re)analysis products, to climate change scenario datasets. SnowAssim is able to ingest snow data ranging from ground-based snow observations to remote-sensing data. This dataset is the result of using these meteorological- and snow-evolution models to ingest appropriate datasets and produce the required outputs. A total of 528 meteorological station sites obtained from the Imiq Database (http://arcticlcc.org/imiq) provided air temperature, relative humidity, and wind speed/direction. Daily met station data were converted to hourly using various sub-models, then all hourly data were aggregated to 3-hourly for the model simulations. Precipitation inputs were obtained from the NASA 3-hourly MERRA atmospheric reanalysis data set. Snow DATA outputs are at 2 km x 2 km resolution and cover all of mainland Alaska and portions of adjacent Yukon and Northwest territories north of 61.5° N, and west of 130.2° W. Snow Tran 3-D is not implemented in this instance, because grid cell size exceeds the scale at which wind transport is expected to operate.

This resource contains snow metrics for a future climate scenario and represents a subset of the SnowClim Dataset (https://www.hydroshare.org/resource/acc4f39ad6924a78811750043d59e5d0/). The SnowClim Dataset was developed following the methods presented in Lute et al., (in prep). The future snow data was created by first downscaling 4 km climate forcings from the Weather Research and Forecasting (WRF) model (Rasmussen and Liu, 2017) over a thirteen year period representing conditions under RCP 8.5 during 2071-2100 and then using this climate data to force the SnowClim snow model. Snow model outputs were summarized into snow metrics at ~210 m spatial resolution for the western US.

Additional details about forcing data preparation, model physics, model calibration, and application to the western US domain can be found in: Lute, A. C., Abatzoglou, J., and Link, T.: SnowClim v1.0: high-resolution snow model and data for the western United States, Geosci. Model Dev., 15, 5045–5071, https://doi.org/10.5194/gmd-15-5045-2022, 2022.

This dataset includes Snow Water Equivalent Depth(swed) for northern Alaska in GeoTiff format, covering the years 1980-2012. Snow Water Equivalent Depth is defined as depth on 1 March(m). The dataset was generated by the Arctic LCC SNOWDATA: Snow Datasets for Arctic Terrestrial Applications project.

The dataset is delivered in the ZIP archive file format. Each year is output in a separate GeoTiff file, where the year is indicated by the filename.

Over the last 20 years, under a variety of NOAA, NSF, and NASA research programs, a snow-evolution modeling system has been developed that includes the MicroMet micrometeorological model, the SnowModel snow-process model, and the SnowAssim data assimilation model. These modeling tools can be thought of as physically-based mathematical descriptions that create value-added information (e.g., snow depth, snow density, snow hardness, rain-on-snow events, and snow cover duration) from basic meteorological variables (e.g., air temperature, humidity, precipitation, and wind speed and direction). The resulting products are based on a physical understanding of environmental processes and features, and their interactions with the atmosphere and surrounding land surface. SnowModel is unique in its representation of blowing snow processes; it includes SnowTran-3D, a model developed initially for Arctic Alaska applications, and arguably the most widely used snow transport model in the world. The model formulations are general enough to allow simulations over temporal domains spanning years to decades, and spatial domains spanning from small watersheds to all of Alaska. MicroMet can use atmospheric forcing ranging from individual meteorological stations, to gridded atmospheric (re)analysis products, to climate change scenario datasets. SnowAssim is able to ingest snow data ranging from ground-based snow observations to remote-sensing data. This dataset is the result of using these meteorological- and snow-evolution models to ingest appropriate datasets and produce the required outputs. A total of 528 meteorological station sites obtained from the Imiq Database (http://arcticlcc.org/imiq) provided air temperature, relative humidity, and wind speed/direction. Daily met station data were converted to hourly using various sub-models, then all hourly data were aggregated to 3-hourly for the model simulations. Precipitation inputs were obtained from the NASA 3-hourly MERRA atmospheric reanalysis data set. Snow DATA outputs are at 2 km x 2 km resolution and cover all of mainland Alaska and portions of adjacent Yukon and Northwest territories north of 61.5° N, and west of 130.2° W. Snow Tran 3-D is not implemented in this instance, because grid cell size exceeds the scale at which wind transport is expected to operate.

This data set consists of modeled snow water equivalent (SWE) data for 10 mountain ranges in North America, simulated by the Weather Research and Forecasting (WRF) regional climate model.

This resource contains snow metrics for the present-day period and represents a subset of the SnowClim Dataset (https://www.hydroshare.org/resource/acc4f39ad6924a78811750043d59e5d0/). The SnowClim Dataset was developed following the methods presented in Lute et al., (in prep). The present-day snow data was created by first downscaling 4 km climate forcings from the Weather Research and Forecasting (WRF) model (Rasmussen and Liu, 2017) over a thirteen year period (1 Oct 2000 to 30 Sep 2013) and then using this climate data to force the SnowClim snow model. Snow model outputs were summarized into snow metrics at ~210 m spatial resolution for the western US.

Additional details about forcing data preparation, model physics, model calibration, and application to the western US domain can be found in: Lute, A. C., Abatzoglou, J., and Link, T.: SnowClim v1.0: high-resolution snow model and data for the western United States, Geosci. Model Dev., 15, 5045–5071, https://doi.org/10.5194/gmd-15-5045-2022, 2022.

During the blowing snow events (Oct.17, 2007 and Nov. 5, 2007), some photos and movies were taken at the Environment Canada Weather Office in Iqaluit, NU. Time stamps on actual pictures are UTC reported time minus 1 (because of daylight savings). The camera was set to the wrong time. This had been corrected for future photos.

This dataset includes gridded data on snow depth (m), snow water equivalent (mm), runoff from snow melt (mm) and snow cover fraction for Swtzerland. The data is spanning the water years 2016-2022 at a high spatial resolution of 250 m. Data are stored as daily results.

This data set contains snow pack properties, such as depth and snow water equivalent (SWE), from the NOAA National Weather Service's National Operational Hydrologic Remote Sensing Center (NOHRSC) SNOw Data Assimilation System (SNODAS). SNODAS is a modeling and data assimilation system developed by NOHRSC to provide the best possible estimates of snow cover and associated parameters to support hydrologic modeling and analysis.

This resource contains snow metrics for the pre-industrial period and represents a subset of the SnowClim Dataset (https://www.hydroshare.org/resource/acc4f39ad6924a78811750043d59e5d0/). The SnowClim Dataset was developed following the methods presented in Lute et al., (in prep). The pre-industrial snow data was created by first downscaling 4 km climate forcings from the Weather Research and Forecasting (WRF) model (Rasmussen and Liu, 2017) over a thirteen year period (1 Oct 2000 to 30 Sep 2013) and then perturbing the downscaled data using multi-model mean deltas from CMIP5 to create climate forcing data that reflects conditions during 1850-1879. This climate data was then used to force the SnowClim snow model. Snow model outputs were summarized into snow metrics at ~210 m spatial resolution for the western US.

Additional details about forcing data preparation, model physics, model calibration, and application to the western US domain can be found in: Lute, A. C., Abatzoglou, J., and Link, T.: SnowClim v1.0: high-resolution snow model and data for the western United States, Geosci. Model Dev., 15, 5045–5071, https://doi.org/10.5194/gmd-15-5045-2022, 2022.

simulated by the Weather Research and Forecasting (WRF) regional climate model.

This data report is a summary of snow-survey information collected during a trip to the Arctic Slope April 12-15, 2004. The data were all collected as part of the Biocomplexity of Frost-Boil Ecosystems study (Walker et al. 2004). Snow is an important factor affecting soil-surface temperatures during the winter. These data will be used to help model the influence of snow on frost heave. The data collected included

Snow depth and soil temperature information from 97 of the 117 permanent plots (releves) that are part of a vegetation classification study.

Snow density and snow-water-equivalent (SWE) measurements from the midpoints of the four sides of each of ten 10x10-m grids at Happy Valley, Sagwon, Franklin Bluffs, and Deadhorse. We were not able to access the grids on B.P.-leased lands atWest Dock, and Howe Island, because we did not have B.P.'s "authorization to proceed".

Snow depths at every meter within each of the ten grids.

Snow profile descriptions from each of the ten grids.

Heave measurements from iron re-bar at releve sites and V. Romanovsky heave meters.

Snow depth measurements every 100 m within 1 x 1 km plot and at 45 permanent plots at Happy Valley.

This dataset comprises the climatology on gridded data of snow water equivalent and snow melt runoff spanning 1998-2022, with a spatial resolution of 1 km and daily temporal resolution. This data was produced with the conceptual OSHD model (Temperature Index Model).

The Version 1 AMSR-E/2 Rain-on-Snow Data Record (ROS) was generated from daily brightness temperatures (Tb) at vertically and horizontally polarized 36.5 GHz and 18.7 GHz from the Advanced Microwave Scanning Radiometer for EOS and Advanced Microwave Scanning Radiometer 2 (AMSR-E/2) across Alaska, USA for water years (WY) 2003 to 2016. The AMSR-E/2 orbital swath Tb data were spatially re-sampled to a 6-km resolution on a polar EASE-Grid (version 2) format using an inverse distance squared weighting method (Brodzick et al. 2012, Du et al. 2017). To ensure cross-sensor consistency, the gridded AMSR-2 Tb data were empirically calibrated against the same AMSRE frequencies using a Double-Differencing method and similar overlapping observations from the FY3B MWRI sensor record (Du et al. 2014). ROS events and associated snow wetness, induced by atmospheric conditions, were detected using a spectral Gradient Ratio to exploit the 19 and 37 GHz dielectric properties in response to rain-on-snow events and enhanced liquid water content (LWC) within the surface snowpack (Grenfell and Putkonen 2008). The Gradient Ratio is applied to vertical and horizontal polarizations, respectively. The proceedings Gradient Ratios are then ratioed together using a Gradient Ratio Polarization (GRP) (Dolant et al. 2016, Langlois et al. 2017). A threshold value is then applied to the GRP to detect ROS. The threshold is set to < 1 for elevations below 900 m and < -5 for elevations above 900 m. The satellite derived data record allows for the identification of ROS events at a relatively fine spatial resolution and high temporal resolution. Data are provided at daily acquisitions, allowing for the identification of single ROS events; and monthly annual aggregations from November through March.

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

The United States Historical Climatology Network (USHCN) is a high-quality data set of daily and monthly records of basic meteorological variables from 1218 observing stations across the 48 contiguous United States. Daily data include observations of maximum and minimum temperature, precipitation amount, snowfall amount, and snow depth; monthly data consist of monthly-averaged maximum, minimum, and mean temperature and total monthly precipitation. Most of these stations are U.S. Cooperative Observing Network stations located generally in rural locations, while some are National Weather Service First-Order stations that are often located in more urbanized environments. The USHCN has been developed over the years at the National Oceanic and Atmospheric Administration's (NOAA) National Climatic Data Center (NCDC) to assist in the detection of regional climate change. Furthermore, it has been widely used in analyzing U.S. climte. The period of record varies for each station. USHCN stations were chosen using a number of criteria including length of record, percent of missing data, number of station moves and other station changes that may affect data homogeneity, and resulting network spatial coverage. Collaboration between NCDC and CDIAC on the USHCN project dates to the 1980s (Quinlan et al. 1987). At that time, in response to the need for an accurate, unbiased, modern historical climate record for the United States, the Global Change Research Program of the U.S. Department of Energy and NCDC chose a network of 1219 stations in the contiguous United States that would become a key baseline data set for monitoring U.S. climate. This initial USHCN data set contained monthly data and was made available free of charge from CDIAC. Since then it has been comprehensively updated several times [e.g., Karl et al. (1990) and Easterling et al. (1996)]. The initial USHCN daily data set was made available through CDIAC via Hughes et al. (1992) and contained a 138-station subset of the USHCN. This product was updated by Easterling et al. (1999) and expanded to include 1062 stations. In 2009 the daily USHCN dataset was expanded to include all 1218 stations in the USHCN.