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 provides daily 4 km snow water equivalent (SWE) and snow depth over the conterminous United States. It was developed at the University of Arizona (UA) under the support of the NASA MAP and SMAP Programs. The data were created by assimilating in-situ snow measurements from the National Resources Conservation Service's SNOTEL network and the National Weather Service's COOP network with modeled, gridded temperature and precipitation data from PRISM.

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 record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

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 data set presents snow depth, snow water equivalent (SWE), and bulk snow density data collected during the NASA SnowEx 2023 field campaign between March 13-16 2023. Samples were collected using an Adirondack snow sampler (SWE tube) from two study sites: Upper Kuparuk and Toolik (UKT), an arctic tundra environment in Northern Alaska, and Farmers Loop Creamers Field (FLCF), a boreal forest near Fairbanks, Alaska.

Weather, snow, stream, topographic, and vegetation data are presented from the South Mountain Experimental Catchments from water years 2007-2013 (10-1-2007 to 9-30-2013). The data provide detailed information on the weather and hydrologic response for four highly instrumented catchments in the late stages of woodland encroachment. Hourly data from six meteorologic stations and four weirs have been carefully processed and quality checked, are serially complete, and ideal for hydrologic, ecosystem, and biogeochemical modeling. Topographic and vegetation data, as well as stream and drainage area delineations are Lidar-derived. This study site was established in 2007 as a collaborative, long-term research laboratory to address the impacts of western juniper (Juniperus occidentalis Hook) encroachment and treatments in the interior Great Basin region of the western USA. For more information about this dataset, contact: Patrick R. Kormos: patrick.kormos@ars.usda.gov Danny G. Marks: ars.danny@gmail.com

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 data table contains snowpack and frost data measured at the Marcell Experimental Forest from 1962–ongoing. The data came from five peatland/upland forest watersheds instrumented for hydrologic monitoring. Frost thickness and snowpack (snow water content, snowpack depth) are measured at 10 snowcourses that encompass three cover types (conifer, deciduous, open). The Marcell Experimental Forest in Itasca County, Minnesota, is operated and maintained by the USDA Forest Service, Northern Research Station, and was formally established in 1962 to study the ecology and hydrology of peatlands.

Snow

## Overview

Snow is a dataset for object detection tasks - it contains Abc 0UdW annotations for 684 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

Multi-model ensembles of snow depth based on projections from twenty-eight Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1900-2100. Specifically, the 5th, 25th, 50th, 75th and 95th percentiles of the monthly, seasonal and annual ensembles of snow depth (m) are available for the historical time period, 1900-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Note: Projections among climate models can vary because of differences in their underlying representation of earth system processes. Thus, the use of a multi-model ensemble approach has been demonstrated in recent scientific literature to likely provide better projected climate change information.

This data set contains a time series of snow depth maps and related intermediary snow-on and snow-off DEMs for Grand Mesa and the Banded Peak Ranch areas of Colorado derived from very-high-resolution (VHR) satellite stereo images and lidar point cloud data. Two of the snow depth maps coincide temporally with the 2017 NASA SnowEx Grand Mesa field campaign, providing a comparison between the satellite derived snow depth and in-situ snow depth measurements. The VHR stereo images were acquired each year between 2016 and 2022 during the approximate timing of peak snow depth by the Maxar WorldView-2, WorldView-3, and CNES/Airbus Pléiades-HR 1A and 1B satellites, while lidar data was sourced from the USGS 3D Elevation Program.

This Western United States snow reanalysis data set contains daily estimates of posterior snow water equivalent (SWE), fractional snow-covered area (fSCA) and snow depth (SD) at 16 arc-second (~500 m) resolution from water years 1985 to 2021. This data set was developed to be compared to SnowEx data sets but its utility reaches beyond that since its spatial and temporal bounds extend over the entire Western U.S. and over several decades.

The Tibetan Plateau has an average altitude of over 4000 m and is the region with the highest altitude and the largest snow cover in the middle and low latitudes of the Northern Hemisphere regions. Snow cover is the most important underlying surface of the seasonal changes on the Tibetan Plateau and an important composing element of ecological environment. Ice and snow melt water is an important water resource of the plateau and its downstream areas. At the same time, plateau snow, as an important land-surface forcing factor, is closely related to disastrous weather (such as droughts and floods) in East Asia, the South Asian monsoon and in the middle and lower reaches of the Yangtze River. It is an important indicator of short-term climate prediction and one of the most sensitive responses to global climate change. The snow depth refers to the vertical depth from the surface of the snow to the ground. It is an important parameter for snow characteristics and one of the conventional meteorological observation elements. It is the key parameter of snow water equivalent estimation, climate effect studies of snow cover, the basin water balance, the simulation and monitoring of snow-melt, and snow disaster evaluation and grading. In this data set, the Tibetan Plateau boundary was determined by adopting the natural topography as the leading factor and by comprehensive consideration of the principles of altitude, plateau and mountain integrity. The main part of the plateau is in the Tibetan Autonomous Region and Qinghai Province, with an area of 2.572 million square kilometers, accounting for 26.8% of the total land area of China. The snow depth observation data are the monthly maximum snow depth data after quality detection and quality control. There are 102 meteorological stations in the study area, most of which were built during the 1950s to 1970s. The data for some months or years for sites existing during this period were missing, and the complete observational records from 1961 to 2013 were adopted. The temporal resolution is daily, the spatial coverage is the Tibetan Plateau, and all the data were quality controlled. Accurate and detailed plateau snow depth data are of great significance for the diagnosis of climate change, the evolution of the Asian monsoon and the management of regional snow-melt water resources.

This dataset contains the CryoClim Daily Snow Cover Fraction (snow on ground) product, produced by the Snow project of the ESA Climate Change Initiative programme.

Fractional snow cover (FSC) on the ground indicates the area of snow observed from space on land surfaces, in forested areas compensated for the effect of trees hiding the ground surface snow cover under the forest canopy. The FSC is given in percentage (%) per grid cell.

The global snow_cci CryoClim fractional snow cover (FSC) product is available at 0.05° grid size (about 5 km) for all land areas, excluding Antarctica and Greenland ice sheet. The coastal zones of Greenland are included.

The CryoClim FSC time series provides daily products for the period 1982 – 2019.

The CryoClim FSC product is based on a multi-sensor time-series fusion algorithm combining observations by optical and passive microwave radiometer (PMR) data. The product combines an historical record of AVHRR sensor data with PMR data from the SMMR, SSM/I and SSMIS sensors.

The overall aim of the CryoClim FSC climate data record is to provide one of the longest snow cover extent time series available with global coverage and without hindrance from clouds and polar night. This has been achieved by utilising the best features of optical and passive microwave radiometer observations of snow using a sensor-fusion algorithm generating a consistent time series of global FSC products (Solberg et al. 2014, 2015; Rudjord et al. 2015).

The snow_cci project has advanced the original CryoClim binary product to an FSC product. The thematic variable represents snow on the ground (SCFG).

AVHRR sensors aboard the satellites NOAA-7, -9, -11, -14, -16, -18, -19 have been used as the optical data source, and SMMR, SSM/I and SSMIS sensors aboard the Nimbus-7, DMSP F8, DMSP F10, DMSP F11, DMSP F13, DMSP F14, DMSP F15, DMSP F16, DMSP F17 and DMSP F18 satellites, respectively, have been used as PMR data source. To have the best possible input data quality, we have used fundamental climate data records (FCDRs) developed by EUMETSAT CM SAF for AVHRR (Karlson et al. 2020) and PMR (Fenning et al. 2017).

The optical algorithm component processes all available swaths from AVHRR GAC. The calculations are based on a Bayesian approach using a set of signatures (instrument channel combinations) and statistical coefficients. For each pixel of the swath, the probabilities for the surface classes snow, bare ground and cloud are estimated. The statistical coefficients are based on pre-knowledge of the typical behaviour of the surface classes in the different parts of the electromagnetic spectrum.

The algorithm for PMR is also based on a Bayesian estimation approach. For SSM/I and SSMIS four snow classes were defined to model the snow surface state. For SMMR two classes were considered. The algorithm estimates the probability for each snow class given the PMR measurements. Land cover data are included to improve the performance of the Bayesian algorithm. This made it possible to construct a Bayesian estimator for each land cover regime.

The multi-sensor multi-temporal fusion algorithm (Rudjord et al. 2015; Solberg et al. 2017) is based on a hidden Markov model (HMM) simulating the snow states based on observations with PMR and optical sensors. The basic idea is to simulate the states the snow surface goes through during the snow season with a state model. The states are not directly observable, but the remote sensing observations give data describing the snow conditions, which are related to the snow states. The HMM solution represents not only a multi-sensor model but also a multi-temporal model. The sequence of states over time is conditioned to follow certain optimisation criteria.

The advancement from binary to fractional snow cover carried out by snow_cci has followed two main paths: First, we introduced more HMM states to be able to classify the snow cover into 10% FSC intervals. However, introducing 100 primary states to obtain 1% FSC intervals would not give a stable model. For obtaining higher precision, we have interpolated between HMM states using a secondary Viterbi sequence. The two probabilities are used as weights to estimate the FSC.

Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the FSC product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.

The FSC product aims to serve the needs of users working with the cryosphere and climate research and monitoring activities, inc... For full abstract see: https://catalogue.ceda.ac.uk/uuid/f4654030223445b0bac63a23aaa60620.

Snow Segmentation 2

## Overview

Snow Segmentation 2 is a dataset for instance segmentation tasks - it contains Fully Covered SnowPanel X2sz annotations for 807 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

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

This dataset contains v3.0 of the Daily Snow Water Equivalent (SWE) product from the ESA Climate Change Initiative (CCI) Snow project, at 0.1 degree resolution.

Snow water equivalent (SWE) indicates the amount of accumulated snow on land surfaces, in other words the amount of water contained within the snowpack. The SWE product time series covers the period from 1979/01 to 2022/12. Northern Hemisphere SWE products are available at daily temporal resolution with alpine areas masked.

The product is based on data from the Scanning Multichannel Microwave Radiometer (SMMR) operated on National Aeronautics and Space Administration’s (NASA) Nimbus-7 satellite, the Special Sensor Microwave / Imager (SSM/I) and the Special Sensor Microwave Imager / Sounder (SSMI/S) carried onboard the Defense Meteorological Satellite Program (DMSP) 5D- and F-series satellites. The satellite bands provide spatial resolutions between 15 and 69 km. The retrieval methodology combines satellite passive microwave radiometer (PMR) measurements with ground-based synoptic weather station observations by Bayesian non-linear iterative assimilation. A background snow-depth field from re-gridded surface snow-depth observations and a passive microwave emission model are required components of the retrieval scheme.

The dataset is aimed to serve the needs of users working on climate research and monitoring activities, including the detection of variability and trends, climate modelling, and aspects of hydrology and meteorology.

The Finnish Meteorological Institute is responsible for the SWE product development and generation.

For the period from 1979 to May 1987, the products are available every second day. From October 1987 till December 2022, the products are available daily. Products are only generated for the Northern Hemisphere winter seasons, usually from beginning of October till the middle of May. A limited number of SWE products are available for days in June and September; products are not available for the months July and August as there is usually no snow information reported on synoptic weather stations, which is required as input for the SWE retrieval. Because of known limitations in alpine terrain, a complex-terrain mask is applied based on the sub-grid variability in elevation determined from a high-resolution digital elevation model. All land ice and large lakes are also masked; retrievals are not produced for coastal regions of Greenland.

Passive microwave radiometer data are obtained from the recalibrated enhanced resolution CETB ESDR dataset (MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature (CETB) Earth System Data Record (ESDR) https://nsidc.org/pmesdr/data-sets/). Spatially and temporally varying snow density fields are implemented into the SWE retrieval, dry snow detection algorithm has been updated and snow masking in post-production has been improved. The time series has been extended from snow_cci version 2 by two years with data from 2020 to 2022 added.

The ESA CCI phased product development framework allowed for a systematic analysis of these changes in the snow density parameterization, snow dry detection and snow masking that occurred between v2 and v3 using a series of step-wise developmental datasets. In comparison with in-situ snow courses, the correlation and RMSE of v3 improved 0.014 and 0.6 mm, respectively, relative to v2. The timing of peak snow mass is shifted two weeks later compared to v1 and reduction in peak snow mass presented in v2 is removed in v3.

This dataset has been deprecated due to data errors in the v3.0 product.

This service consists of two maps of snow characteristics for the coterminous U.S.: snow water equivalent, snow depth. The data can be viewed as a graphic at the NOHRSC website.Link to graphical web page: https://www.nohrsc.noaa.gov/nsa/Link to data download: https://www.nohrsc.noaa.gov/archived_data/Link to metadataQuestions/Concerns about the service, please contact the DISS GIS teamTime Information:This service is not time enabled

however, USNIC, who creates this product, does not guarantee availability or timely delivery of data via the NIC Web server. NSIDC, as the data archive, does not guarantee availability of this product via the NSIDC Web server. These servers should not be used to support operational observation, forecasting, emergency, or disaster mitigation operations, either public or private. Users with real-time operational needs should visit the USNIC Web site and contact the USNIC Liaison to request access to their operational server.

Seasonal and annual multi-model ensembles of projected change (also known as anomalies) in snow depth based on an ensemble of twenty-eight Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1900-2100. Projected change in snow depth is with respect to the reference period of 1986-2005 and expressed as a percentage (%). The 5th, 25th, 50th, 75th and 95th percentiles of the ensemble of snow depth change are available for the historical time period, 1900-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Twenty-year average changes in snow depth (%) for four time periods (2021-2040; 2041-2060; 2061-2080; 2081-2100), with respect to the reference period of 1986-2005, for RCP2.6, RCP4.5 and RCP8.5 are also available in a range of formats. The median projected change across the ensemble of CMIP5 climate models is provided. Note: Projections among climate models can vary because of differences in their underlying representation of earth system processes. Thus, the use of a multi-model ensemble approach has been demonstrated in recent scientific literature to likely provide better projected climate change information.