This data set provides daily 4 km and 800 m snow water equivalent (SWE) and snow depth over the conterminous United States. It was 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 the Parameter-elevation Regressions on Independent Slopes Model (PRISM). The finalized dataset is hosted at the National Snow and Ice Data Center (NSIDC) but this server also contains early, provisional, and higher resolution SWE and snow depth data from 1981-10-01 to present (with a latency of ~2 days). Directory Contents: SWE_Mask_4km.nc - Mask showing areas with valid data vs. no data (due to water, perminant snow cover, or areas outside the Conterminous US) for the 4km data SWE_Mask_800m.nc - Mask showing areas with valid data vs. no data for the 800m data DailyData_4km - Daily 4km NetCDF files (organized by water year) of SWE and snow depth since water year (WY) 1982 DailyData_800m - Daily 800m NetCDF files (organized by water year) of SWE and snow depth since water year (WY) 1982 WYData_4km - Combined 4km NetCDF files (where all data for each water year are combined) of SWE and snow depth since water year (WY) 1982. Note that these files also get archived on at the National Snow and Ice Data center (https://nsidc.org/data/nsidc-0719/versions/1). DailyData - Near real-time 4 km data files that have been generated for the past couple of years. It is recommended for new users to use the data in DailyData_4km or WYData_4km but this directory structure is provided for continuity to existing users of the near real-time data. Data Description: This data set was developed by consistently assimilating PRISM daily 1 and 4 km precipitation and temperature data, SWE and snow depth data from thousands of in-situ snow stations from the SNOTEL network, and snow depth data from the COOP network. The assimilation of the SWE and snow depth measurements in this data set was achieved by using the key idea in Broxton et al. (2016b) along with the new snow density model described in Dawson et al. (2017). A summary of how the method was additionally refined, as well as a trend/driver analysis of the data set, are provided in Zeng et al. (2018). The ratio between observed SWE, which is normalized by the accumulated snowfall, and modeled ablation (based on a temperature index snow model forced with PRISM data) is interpolated between the station locations. The results are then used to correct a background SWE field generated using a gridded version of the same PRISM-based snow model. The assimilation includes a new snow density parameterization, which is used to combine SWE and snow depth measurements from hundreds of SNOTEL sites with the snow depth measurements from thousands of COOP sites. In addition, snowfall is separated from rainfall using a temperature threshold, which is based on the occurrence of snow and rain at individual stations; the snow ablation is also estimated as a function of temperature, which is based on station data. The 4 km data are also downscaled to 800 m resolution (this downscaling is described in Broxton et al., 2024). First, the data are recomputed at 800 m resolution using same process that is used to generate the 4 km data but with 800m PRISM inputs. Next, these estimates are multiplied by adjustment factors that account for variable terrain and forest cover conditions, which are generated using artificial neural networks that are trained to predict the ratio of SWE on different landscape positions to that on flat, open (non-forested) areas (Broxton et al., 2019). These models use slope angle, aspect, and percent forest cover as predictors and are trained with airborne lidar data in the southwest US. Note that the recent data follow the convention of the Parameter-elevation Regressions on Independent Slopes Model (PRISM) dataset, in that data for the current month are labeled 'early', data for 1-6 months in the past are labeled 'provisional', data that are > 6 months old are labeled 'stable'. The 'early' data are generated once in near-real time, the 'provisional' data are reprocessed twice per month to reflect the latest available observations, and the 'stable' data are processed once, and are used in the finalized dataset. Literature Citations: Broxton, P. D., X. Zeng, and N. Dawson, 2016a: Why Do Global Reanalyzes and Land Data Assimilation Products Underestimate Snow Water Equivalent? Journal of Hydrometeorology, 17: 2743–2761, https://doi.org/10.1175/JHM-D-16-0056.1. Broxton, P. D., X. Zeng, and N. Dawson, 2016b: Linking snowfall and snow accumulation to generate spatial maps of SWE and snow depth, Earth and Space Science, 3(6): 246–256, https://doi.org/10.1002/2016EA000174. Dawson, N., P. D. Broxton, X. Zeng, M. Leuthold, M. Barlage, and P. Holbrook, 2016: An Evaluation of Snow Initializations in NCEP Global and Regional Forecasting Models, Journal of Hydrometeorology, 17: 1885–1901, https://doi.org/10.1175/JHM-D-15-0227.1. Zeng, X., P. Broxton, and N. Dawson. 2018. Snowpack Change From 1982 to 2016 Over Conterminous United States, Geophysical Research Letters. 45. 12940-12947. https://doi.org/10.1029/2018GL079621. Broxton, P. D., van Leeuwen, W. J., & Biederman, J. A. (2019). Improving snow water equivalent maps with machine learning of snow survey and lidar measurements. Water Resources Research, 55(5), 3739–3757. https://doi.org/10.1029/2018wr024146. Broxton, P., M. R. Ehsani, A. Behrangi, M. Reza. 2024. Improving mountain snowpack estimation using machine learning with Sentinel‐1, the Airborne Snow Observatory, and University of Arizona snowpack data. Earth and Space Science, https://doi.org/10.1029/2023EA002964. Dataset Citation: Broxton, P., X. Zeng, and N. Dawson. 2019. Daily 4 km Gridded SWE and Snow Depth from Assimilated In-Situ and Modeled Data over the Conterminous US, Version 1. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/0GGPB220EX6A. [Date Accessed].