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.

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.

description:

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.; abstract:

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 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.

April 1 SWE is used across the western USA as a predictor of spring streamflow. Here, we use SNODAS data (https://nsidc.org/data/g02158) to map 10, 25, 50, 75, and 90th percentiles of April 1 SWE across the contiguous USA. This data is part of the data supplement for Lapides et al., 20XX.

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 data are 8-digit USGS Hydrologic Unit (HUC) level summaries of snow cover, snow depth, and snow water equivalent for 8-digit HUC units within the Upper Colorado River basin. The data were derived from gridded data sets for the continental United States at 1 km spatial resolution and 24 hour temporal resolution. This WaterOneFlow web service was created by Utah State University as part of a National Integrated Drought Information System (NIDIS) project sponsored by the United States Geological Survey.

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 data are 10-digit USGS Hydrologic Unit (HUC) level summaries of snow cover, snow depth, and snow water equivalent for 10-digit HUC units within the Upper Colorado River basin. The data were derived from gridded data sets for the continental United States at 1 km spatial resolution and 24 hour temporal resolution. This WaterOneFlow web service was created by Utah State University as part of a National Integrated Drought Information System (NIDIS) project sponsored by the United States Geological Survey.

This is a required system file for BAGIS-PRO that contains the layout definition for the SNODAS SWE map. DO NOT DELETE

SNODAS gridded daily SWE comparison - 3/20/2018 vs 3/20/2017. Test of netCDF upload

This data set includes 1 km resolution monthly timescale estimates of the effective recharge component of the water budget over the time period from October 2003 - December 2015. These estimates were developed as water budget residuals using previously published data sets for other water budget components: PRISM precipitation (Daly et al., 2004), SNODAS snow water equivalent (National Operational Hydrologic Remote Sensing Center, 2004), SSEBop-WB evapotranspiration (Reitz et al., 2017), a map of groundwater-sourced irrigation (Reitz et al., 2017), and monthly surface runoff maps (Reitz et al., 2019). The recharge data were estimated as the difference between water supply (precipitation plus snow melt plus irrigation) and the other water budget components (snow accumulation, surface runoff, and ET) for a given month. In locations / months where the SNODAS snow accumulation data indicated greater snow accumulation than PRISM precipitation for that month, the snow accumulation was capped to the precipitation value. The monthly recharge maps represent the implications of these water budget component estimates on resulting recharge rates, and are not accompanied by an evaluative and interpretive journal article or report, so ought to be taken as preliminary estimates. The authors plan to follow this work with further efforts that will result in updated versions of monthly recharge maps, and accompanying interpretive and evaluative work. The data set here includes two versions of the monthly recharge maps. The raw version (e.g., "2003_raw.zip") includes negative values where the water budget component estimates for ET, runoff, and snow accumulation exceeded the water supply from precipitation and snow melt. The positive version (e.g., "2003_positive.zip") replaces these negative values with zeros. The positive version is the one that should be used for application of these data sets, but the raw version can be useful as an indication of the quality of the water budget estimates in a given location.

This is a supporting dataset for the paper :

Descriptions and units for each column can be found in a dedicated page within the data file. Methods are decribed in the paper.

Whereas many independent methods are used to estimate snow water equivalent (SWE) and its spatial distribution and seasonal variability, a need exists for a systematic characterization of inter-model differences at annual, seasonal, and regional scales necessary to quantify the associated uncertainty in these datasets. This study conducts a multi-scale validation and comparison, based on Airborne Snow Observatory data, of five state-of-the-art SWE datasets in the Sierra Nevada, California, including three SWE datasets from retrospective models: an INiTial REConstruction model (REC-INT), an improved REConstruction model based on the ParBal energy balance model (REC-ParBal), and a Sierra Nevada SWE REConstruction with Data Assimilation (REC-DA), and two operational SWE datasets from the U.S. National Weather Service, including the Snow Data Assimilation System (SNODAS) and the National Water Model (NWM-SWE). The results show that REC-DA and REC-ParBal provide the two most accurate estimates of SWE in the snowmelt season, both with small positive biases. REC-DA provides the most accurate spatial distribution of SWE (R2 = 0.87, MAE = 66 mm, PBIAS = 8.3%) at the pixel scale, while REC-ParBal has the least basin-wide PBIAS (R2 = 0.79, MAE = 73 mm, PBIAS = 4.1%) in the snowmelt season. Moreover, REC-DA underestimates peak SWE by −5.8%, while REC-ParBal overestimates it by 7.5%, when compared with the measured peak SWE at snow pillow stations across the Sierra Nevada. The two operational SWE products—SNODAS and NWM-SWE—are less accurate. Furthermore, the inter-model comparison reveals a certain amount of disagreement in snow water storage across time and space between SWE datasets. This study advances our understanding of regional SWE uncertainties and provides critical insights to support future applications of these SWE data products and therefore has broad implications for water resources management and hydrological process studies.

Review the "ReadMe.txt" file included here for a detailed description of the files deposited here. Because of the volume of files contained here, this dataset is best understood using the "Tree" viewing option. In brief, this dataset includes: a master spreadsheet of the daily snowpack and temperature data used for the majority of figures and analysis in this study ("PersistentSnowZone_DailySnow+Temp_Data"), rasters of daily PNW snowpack from SNODAS ("DailySWE_Rasters"), daily and 30-year normals of daily mean temperature and mean temperature anomalies based on PRISM ("TemperatureRasters"), a shapefile of the Persistent Snow Zone used in the study ("zpsC_shapefile"), daily records of temperature observations and yearly summaries thereof of several weather observation stations in the PNW (including the Persistent Snow Zone, though not strictly speaking a station) ("StationTemperatureRecords"), and raw and summary data for a SNODAS validation effort that used 28 stations in the western US from 2003-2012 to analyze the accuracy of SNODAS ("SNODASValidation").

This study first compares two different passive microwave snow water equivalent (SWE) retrievals, namely the retrieval from the Suomi National Polar-orbiting Partnership (S-NPP) Advanced Technology Microwave Sounder (ATMS) and that from the Global Change Observation Mission – Water (GCOM-W1) Advanced Microwave Scanning Radiometer 2 (AMSR2); it further creates an optimal blending mechanism that merges the two retrievals with in situ observations from the Snow Telemetry (SNOTEL) and Cooperative Observer Program (COOP) networks. The assessments of the two products are done over conterminous United States (CONUS) for the snow seasons (November–June) of the water years 2017–2019 using in situ data and the SNOw Data Assimilation System (SNODAS) SWE analysis. Both satellite products tend to underestimate SWE. Between the two, AMSR2 retrieval outperforms in terms of correlation with observations and depth of saturation, but it exhibits a distinctive, seasonally varying bias that is not seen in ATMS retrieval. The negative bias over the early snow season, as further analysis indicates, most likely stems from AMSR2 retrieval’s use of a high frequency channel (i.e., 89 GHz) for shallow snow detection, while the impact of differing assumptions of snow density is marginal. The blending scheme, developed on the basis of the validation experiment, features a histogram-based bias correction as a supplement to optimal interpolation. Cross-validation suggests that interpolated station product without the satellite background broadly underperforms the blended in situ-satellite product, confirming the utility of the satellite retrievals. Furthermore, the a priori bias correction mechanism is shown to be effective in mitigating large fluctuations in bias. Finally, the bias-corrected, blended in situ-satellite product performs comparably or even favorably against SNODAS over many parts of the CONUS, with important implications for joint use of satellite and in situ observations for hydrological monitoring and forecasting.

These datasets are continuous parameter grids (CPG) of first-of-month snow water equivalent data for March through August, years 2004 through 2016, in the Pacific Northwest. Source snow water equivalent data was produced by the Snow Data Assimilation System (SNODAS) at the National Snow and Ice Data Center.

This resource is a repository of the 25- and 100-year return level design maps of 4-km gridded snow water equivalent (SWE) and 1- and 7-day snowmelt over the Contiguous United States (CONUS). The maps are developed using long-term observation-based 4-km gridded SWE developed by University of Arizona (UA SWE) incorporating the 1-km gridded national snow model product (SNOw Data Assimilation System; SNODAS). Please see Cho et al. (2020) in Water Resources Research (WRR) for full details.

Map Metadata (+proj=longlat +ellps=WGS84 +towgs84=0,0,0,-0,-0,-0,0 +no_defs) SWE maps (unit: mm) 1-day snowmelt maps (unit: mm/1-day) 7-day snowmelt maps (unit: mm/7-day)

Preferred citation: Cho, E. and Jacobs, J. M. (2020). Extreme Value Snow Water Equivalent and Snowmelt for Infrastructure Design over the Contiguous United States. Water Resources Research

Corresponding author: Eunsang Cho (ec1072@wildcats.unh.edu; eunsang.cho@nasa.gov)