Find research datasets worth reusing

opencc-by-4.0Dec 2021View details →

Dataset used in snow algae model

This is a data set for the numerical simulation using the snow algae model (Onuma et al., 2018; 2020). The content is as below. - data: algal cell concentration observed on the surface snow worldwide (CSV files). model input and output data (CSV files). output data simulated with Bio-MATSIRO (netCDF files). - python: programs for the visualization (python scripts) - figure: png files created by the python scripts The codes of the snow algae model can be downloaded below. https://github.com/YukihikoOnuma/SnowAlgaeModel

opencc-by-4.0Jan 2022View details →

Snow equi-temperature metamorphism described by a phase-field model applicable on micro-tomographic images: prediction of microstructural and transport properties

This dataset provides data described and used in the article submitted to Journal of Advances in Modeling Earth Systems "Snow equi-temperature metamorphism described by a phase-field model applicable on micro-tomographic images: prediction of microstructural and transport properties". It contains .csv files with different properties computed on outputs of the model Snow3D simulating equi-temperature metamorphism. This micro-scale model was used here with experimental micro-tomographic snow images as input and returns series of 3-D images of snow showing features of equi-temperature metamorphism at different time steps as output. In this dataset, you will find two types of files: - the microstructural properties (density, specific surface area, covariance lengths, mean curvature) computed on  the simulated images at different time steps. - the transport properties (effective conductivity, normalizes effective vapor diffusion coefficient, permeability) of the simulated images at different time steps. Finally, metadata_simulations.csv gather the information relative to the simulations.

opencc-by-4.0Oct 2022View details →

Data and code for: Precipitation Biases and Snow Physics Limitations Drive the Uncertainties in Macroscale Modeled Snow Water Equivalent

Code and data to reproduce figures in manuscript entitled "Precipitation Biases and Snow Physics Limitations Drive the Uncertainties in Macroscale Modeled Snow Water Equivalent" published in Hydrology and Earth System Sciences (https://hess.copernicus.org/preprints/hess-2022-136/). The contents include three folders, "Codes", "Data", and "Figures". In "Codes" folder, R scripts are listed in the order needed to reproduce the figures. All code is written in R version 4.2.0. Data sets needed to reproduce figures are provided in "Data" folder (Rdata format). The pdf files in "Figures" folder are outputs generated from the corresponding R scripts. Note that final figures in the article were produced by combining multiple figures using a vector graphics software (Inkscape) or PowerPoint. Please contact Eunsang Cho (<a href="mailto:eunsang.cho@nasa.gov">eunsang.cho@nasa.gov</a>) with any questions.  Preferred citation: Cho, E., Vuyovich, C. M., Kumar, S. V., Wrzesien, M. L., Kim, R. S., and Jacobs, J. M. (2022). Precipitation Biases and Snow Physics Limitations Drive the Uncertainties in Macroscale Modeled Snow Water Equivalent, Hydrol. Earth Syst. Sci., https://doi.org/10.5194/hess-2022-136. Corresponding author: Eunsang Cho (<a href="mailto:eunsang.cho@nasa.gov">eunsang.cho@nasa.gov</a>; <a href="mailto:escho@umd.edu">escho@umd.edu</a>)

opencc-by-4.0Sep 2024View details →

NASA Eulerian Snow On Sea Ice Model Version 1.1 (NESOSIMv1.1) data: 1980 - 2024

Repository updates Update on Sep 12th 2024: The repository now includes NESOSIM v1.1 output from September 1st 2022 to April 30th 2023 and September 1st 2023 to April 30th 2024  Update on Sep 5th 2022: The repository now includes NESOSIM v1.1 output from September 1st 2021 to April 30th 2022 Update on June 7th 2022: The repository now includes NESOSIM v1.1 output from September 1st 2021 to March 31st 2022 Update on March 8th 2021: The gridded forcing files are now available in the gridded_forcings.zip file. Data are stored as Python pickles which can be easily read in by the core NESOSIM source code.  Update on March 8th 2021: The repository now includes zip files of gridded forcing (snowfall, winds, ice drift, ice concentration, initial conditions) as well as gridded Operation IceBridge snow depths.  Update on January 30th 2021: The repository now also includes a NESOSIM v1.1. daily gridded snow climatology using the mean (np.nanmean) of all data available between September 1 2010 and April 30 2020. Overview NESOSIM is a three-dimensional, two-layer (vertical), Eulerian snow on sea ice budget model developed with the primary aim of producing daily estimates of the depth and density of snow on sea ice across the polar oceans through the winter accumulation season, generally September through April (Petty et al., 2018). This repository contains model output from September 1st 1980 to April 30th 2021 [and September 1st 2021 to March 31st 2022 as of June 7th 2022] based on the NESOSIM v1.1 code release which is available on GitHub (https://github.com/akpetty/NESOSIM/tree/v1.1) and archived through Zenodo (10.5281/zenodo.4448355). More information about changes between the v1.0 and v1.1 model framework can be found in those links. A preprint is now available in The Cryosphere Discuss explaining these upgrades and their impacts on ICESat-2 winter Arctic sea ice thickness estimates (Petty et al., 2022).  Data production: Data are re-initialized at the end of summer each year (September 1st) using summer near-surface air temperature-scaled initial snow depths and run through until the end of April of the following year. The 1987-1988 winter is missing due to the lack of passive-microwave derived ice concentration data available during this period. Daily data are generated on a 100 km x 100 km North Polar Stereographic grid (EPSG: 3413) across the entire Arctic Ocean including the peripheral seas. Forcings: NB: Recent year runs often require the use of near-real-time data products, so the underlying forcings used in this v1.1 release can change in time, as noted below: <ul> <li>Snowfall: European Center for Medium Range Weather Forecasts (ECMWF) ERA5 (https://cds. climate.copernicus.eu, September 1 1980 onwards2) + CloudSat scaling (Cabaj et al., 2020).</li> <li>Near-surface winds: ECMWF ERA5 (https://cds. climate.copernicus.eu, September 1 1980 onwards).</li> <li>Sea ice drift: NSIDC Polar Pathfinder v4 (https://nsidc.org/data/nsidc-0116, September 1 1980 to April 30 2019), OSI SAF merged (https://osi-saf.eumetsat.int/products/osi-405-c, September 1 2019 onwards).</li> <li>Sea ice concentration: Final v3 NSIDC Climate Data Record (https://nsidc.org/data/g02202/versions/3/, September 1 1980 to December 31 2020), and near-real-time v2 NSIDC Climate Data Record (https://nsidc.org/data/g10016, January 1 2021 onwards).</li> <li>Near-surface air temperature (to derive temperature-scaled initial conditions): ECMWF ERA5 (https://cds. climate.copernicus.eu, September 1 1980 onwards).</li> </ul> The forcings used to generate each winter dataset are described in a new 'forcings' variable in each NetCDF file.  Operation IceBridge snow depths: The repository now also includes the gridded Operation IceBridge snow depths we used for calibration purposes, as described in Petty et al., (2022). The data contained within gridded_oib_snowdepths.zip includes the daily gridded data on the NESOSIM v1.1 100 km domain, ordered by day of collection. Data are stored as Python pickles and text files and include estimates derived from the following snow depth algorithms: SRLD (2009-2015): snow radar layer detection, JPL (2009-2015): Jet Propulsion Laboratory, GSFC (2009-2015): Goddard Space Flight Center, NSIDC (2009-2012): archived NASA GSFC data on the NSIDC, QL (2013-2019): NSIDC quick-look data based on the GSFC algorithm. MEDIAN (2010-2015): consensus snow depth from median of GSFC, JPL and SRLD.  References: Cabaj, A., P. J. Kushner, C. G. Fletcher, S. Howell, A. Petty (2020), Constraining reanalysis snowfall over the Arctic Ocean using CloudSat observations, Geophysical Research Letters, 47, doi:10.1029/2019GL086426. Petty, A. A., M. Webster, L. N. Boisvert, T. Markus (2018), The NASA Eulerian Snow on Sea Ice Model (NESOSIM) v1.0: Initial model development and analysis, Geosci. Model Dev., doi: 10.5194/gmd-11-4577-2018. Petty A. A., N. Keeney, A. Cabaj, P. Kushner, M. Bagnardi (2023), Winter Arctic sea ice thickness from ICESat-2: upgrades to freeboard and snow loading estimates and an assessment of the first three winters of data collection, The Cryosphere, 17, 127–156, doi: 10.5194/tc-17-127-2023.

opencc-by-4.0Dec 2022View details →

NH-SWE: Northern Hemisphere Snow Water Equivalent dataset based on in-situ snow depth time series and the regionalisation of the ΔSNOW model

Time series of daily Snow Water Equivalent (SWE) and Snow Density over the Northern Hemisphere, based on in-situ station observations of snow depth converted to SWE using the ΔSNOW model (Winkler et al., 2021) and regionalised parameters.  An extensive description of the dataset and the method to generate it can be found in the data descriptor manuscript published in the journal Earth System Science Data: <a href="https://essd.copernicus.org/preprints/essd-2023-31/">https://essd.copernicus.org/articles/15/2577/2023/essd-15-2577-2023</a>  Dataset: A total of 11,0071 time series of modelled SWE and estimated snow density at the point scale, spanning 1950-2022, at daily resolution. "NH-SWE_dataset_MAP.png" shows a Northern Hemisphere map with the location of all stations in the NH-SWE dataset and their elevation in meters.  Files: The dataset is provided in two different formats: <ol> <li>Individual .csv files for each station in the NH-SWE dataset at "NH_SWE_dataset_vector_files.zip"</li> <li>Full-dataset .csv matrices with dates as rows and NH-SWE stations as columns at "NH_SWE_dataset_matrix_files.zip"</li> </ol> Metadata: "NH_SWE_METADATA.csv" Includes information on NH-SWE stations location (ID, country, station name, coordinates, elevation), data source, length of time series, model parameters and the climate variables used to estimate them, and average snow climatology such as average maximum snow depth, average peak SWE and average maximum snow cover duration. More details and units in the "README_fileformats.txt" file.  ΔSNOW model parameter regionalisation: The code to obtain the ΔSNOW model parameters based on climate variables for all the stations in the NH-SWE dataset is shared in "DeltaSNOW_parameter_regionalisation.zip". The method is extensively described in the data descriptor manuscript by Fontrodona-Bach et al., (2023) submitted to Earth System Science Data. More details in the "README_regionalisation.txt" file.  Data use: Free, provided adequate citation of both the data descriptor manuscript and the zenodo record. See "README_datausage.txt" Version history: v1: Initial upload. The ΔSNOW model regionalisation was missing. v2: Manuscript submission version. Updated dataset and includes the ΔSNOW model regionalisation code. Reported errors: The dataset accidentally contains one station from the Southern Hemisphere (NH-SWE ID 500001), located in Antarctica (Country code AY).  The longitude of a few stations exceeds +180 decimal degrees. To obtain the correct value within the [-180,180] decimal degree longitude bounds, the value exceeding +180 needs to be added to -180 degrees (e.g. +181.0 degrees is actually -179.0 degrees). Swedish stations have two different country codes, SE for the ECA&D stations, and SW for the GHCNd stations.  Japan country code is "JA" in the metadata, although the official country code should be JP.

opencc-by-4.0Oct 2023View details →

SNOWISO model snow- and firn core simulations for the EastGRIP drilling site in Greenland

This dataset (.csv) includes four SNOWISO v2 snowpack simulations of the stable water isotopes (δ18O, δD, d-excess) and is the result of snowpack simulations in: Dietrich, L.J., Steen-Larsen, H.C., Wahl, S., Jones, T.R., Town, M. and Werner, M., 2023. Snow-atmosphere humidity exchange at the ice sheet surface alters annual mean climate signals in ice core records. Geophysical Research Letters, <a href="https://doi.org/10.1029/2023GL104249">http</a><a href="https://doi.org/10.1029/2023GL104249">s://doi.org/10.1029/2023GL104249</a> The SNOWISO model is a 1-D isotope-enabled snowpack and surface exchange model. The model accumulates snowfall (input) and applies water vapor exchange (input) at the snow surface with subsequent isotopic fractionation of the surface snow. In addition, diffusion of water isotopes in the accumulated snowpack is applied. This dataset is simulated in a 1 cm vertical layer resolution. The scientific theory of the SNOWISO model is described in: Wahl, S., Steen‐Larsen, H.C., Hughes, A.G., Dietrich, L.J., Zuhr, A., Behrens, M., Faber, A.K. and Hörhold, M., 2022. Atmosphere‐Snow Exchange Explains Surface Snow Isotope Variability. Geophysical Research Letters, 49(20), p.e2022GL099529. The documentation of the SNOWISO model v2 operational set-up is given in: Dietrich, L.J., Steen-Larsen, H.C., Wahl, S., Jones, T.R., Town, M. and Werner, M., 2023. Snow-atmosphere humidity exchange at the ice sheet surface alters annual mean climate signals in ice core records. Geophysical Research Letters, <a href="https://doi.org/10.1029/2023GL104249">https://doi.org/10.1029/2023GL104249</a> This model dataset consists of simulations for two model configurations each, with (control) and without (no_frac) fractionation during vapor exchange:   <ol> <li>daily average isotopes in the surface snow (top 2 cm) for the periods 11/05/2018-5/8/2018 and 17/5/2019-31/7/2019 <ul> <li>surface_snow_simulation_2018-2019_control.csv</li> <li>surface_snow_simulation_2018-2019_no_frac.csv</li> </ul> </li> <li>three 1-m long snow cores ending in 2017, 2018, and 2019, respectively <ul> <li>snowpack_core_simulation_2017_control.csv</li> <li>snowpack_core_simulation_2018_control.csv</li> <li>snowpack_core_simulation_2019_control.csv</li> <li>snowpack_core_simulation_2017_no_frac.csv</li> <li>snowpack_core_simulation_2018_no_frac.csv</li> <li>snowpack_core_simulation_2019_no_frac.csv</li> </ul> </li> <li>one firn core simulation in the period 1990-2011 (~6 m) <ul> <li>snowiso_model_1990-2012_control.csv</li> <li>snowiso_model_1990-2012_no_frac.csv</li> </ul> </li> <li>one firn core simulation in the period 1990-2020 (~8.5 m) <ul> <li>snowiso_model_1990-2020_control.csv</li> <li>snowiso_model_1990-2020_no_frac.csv</li> </ul> </li> </ol> Model input: <ul> <li>6-hourly precipitation rate, vapor, and precipitation water stable isotopes from ECHAM6-wiso simulation nudged to the ERA-5 reanalysis (https://zenodo.org/record/8341390)</li> <li>hourly latent heat flux, near-surface meteorological variables, and snowpack variables from MARv3.12 simulation driven by the ERA-5 reanalysis (https://zenodo.org/record/8335402)</li> </ul> Please be encouraged to contact me (Laura.Dietrich@uib.no) if you have any questions or ideas regarding these SNOWISO model simulations. Data usage notice: When using the SNOWISO model, you should refer to: Wahl, S., Steen‐Larsen, H.C., Hughes, A.G., Dietrich, L.J., Zuhr, A., Behrens, M., Faber, A.K. and Hörhold, M., 2022. Atmosphere‐Snow Exchange Explains Surface Snow Isotope Variability. Geophysical Research Letters, 49(20), p.e2022GL099529. If you use any of these simulations, you should refer to: Dietrich, L.J., Steen-Larsen, H.C., Wahl, S., Jones, T.R., Town, M. and Werner, M., 2023. Snow-atmosphere humidity exchange at the ice sheet surface alters annual mean climate signals in ice core records. Geophysical Research Letters, <a href="https://doi.org/10.1029/2023GL104249">https://doi.org/10.1029/2023GL104249</a>

opencc-by-4.0Dec 2021View details →

Glaciological data (point mass balance, SWE, snow depth, bulk snow density, modelled runoff) from Werenskioldbreen (Svabard) 2009-2020

This repository contains supporting data associated to the manuscript to Earth System Science Data:  Ignatiuk D., Błaszczyk M., Budzik T., Grabiec M., Jania J., Kondracka M., Laska M., Małarzewski Ł., Stachnik Ł. A decade of glaciological and meteorological observations in the High Arctic (Werenskioldbreen, Svalbard) In 2009-2020, 9 ablation stakes were installed on the Werenskioldbreen. Based on the data collected, the following glaciological variables are available for Werenskioldbreen: annual and seasonal point ablation and accumulation, snow cover depth, bulk snow density and SWE (snow water equivalent) at the measuring points and modelled total runoff from the surface ablation.

opencc-by-4.0Jan 2022View details →

GABLS4, snow model intercomparison.

Content of the archive: FORCING: The near-surface variable forcing dataset used to drive the offline simulations. Time step of 30 minutes, start date 1/12/2009, 15 days. SIMULATIONS: NetCDF output files from participating models. OBSERVATIONS: Surface temperature observation time series and observations of snow temperature in the snowpack at different depths used for the validation. FIGURES: The datasets and the python scripts used to prepare the figures.

opencc-by-4.0Jan 2018View details →

Datasets for "Assessing satellite derived radiative forcing from snow impurities through inverse hydrologic modeling"

This dataset contains observations and model output used in  Matt, F. N., & Burkhart, J. F. (2018). Assessing satellite-derived radiative forcing from snow impurities through inverse hydrologic modeling. Geophysical Research Letters, 45. https://doi.org/10.1002/2018GL077133

opencc-by-4.0Dec 2019View details →

Datasets for the publication "Simulation of snow management in Alpine ski resorts using three different snow models"

Snow model simulation results used for the paper "Simulation of snow management in Alpine ski resorts using three different snow models". The following results are available for each of the nine ski resorts: <ul> <li><resort>_swe_<date>.tif or <resort>_swe.geojson: Spatially distributed SWE outputs for 2016-12-24 and 2017-12-24 in GeoJSON (for the two French resorts) or GeoTIFF (for all other resorts) format. Unit: kg m-2</li> <li><resort>_point_depth.csv, <resort>_point_swe.csv: Time series of snow depth (in m) and SWE (in kg m-2) for the respective points of interest. The column names correspond to the snow management configurations as shown in Fig. 5 of the paper.</li> </ul>

opencc-by-4.0Oct 2021View details →

Upslope migration of snow avalanches in a warming climate: data and model source files

Complete data and model source files corresponding to: Giacona, F., Eckert, N., Corona, C., Mainieri, R., Morin, S., Stoffel, M., Martin, B., Naaim, M. (2021). Upslope migration of snow avalanches in a warming climate. Proceedings of the National Academy of Sciences America, Nov 2021, 118 (44) e2107306118; DOI: 10.1073/pnas.2107306118

opencc-by-4.0Jul 2023View details →

Model output from Snow Ensemble Uncertainty Project (SEUP) as used in Seasonal Snow Predictability Derived from Early-Season Snow in North America

The files provided here are the output from the median peak snow water equivalent (peak_SWE.mat), 1 December snow water equivalent (Dec1_SWE.mat), and 1 January snow water equivalent (Jan1_SWE.mat) model simulations for the Noah-MP run with MERRA-2 forcing, as used in Lundquist et al. (2023) and described in Kim et al. (2021).  We also include the model grid's latitude, longitude and elevation data (SEUPlatlon.mat), and example code for plotting the data (Plotmodeldata.m) as in the Lundquist et al. (2023) paper.  Kim, R. S., Kumar, S., Vuyovich, C., Houser, P., Lundquist, J., Mudryk, L., et al. (2021). Snow Ensemble Uncertainty Project (SEUP): Quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling. The Cryosphere, 15(2), 771-791. Lundquist, J. D., R. S. Kim, M. Durand, and L. R. Prugh, 2023, Seasonal Peak Snow Predictability Derived from Early-Season Snow in North America, Geophysical Research Letters, (submitted 2023)

opencc-by-4.0Jan 2020View details →

Winter snow depths for initializing a glacio-hydrological model in high mountain Chile

The following dataset consists of the forcings, initial conditions, model grids and parameters used to run the TOPKAPI-ETH model (Finger et al., 2011; Ragettli and Pellicciotti, 2012) for the Rio Yeso catchment of central Chile (33.44°S, 69.93°W - Burger et al., 2018). The data and model grids were used to investigate the importance of accurate snow depth maps for initialising the physically-oriented model in a high elevation catchment - For a manuscript submitted to Water Resources Research (WRR) - January 2020.    Data and file repository for the submitted article: %------------------------------------------------------------- %-------------------------------------------------------------  On the utility of optical satellite winter snow depths for modelling the  glacio-hydrological behaviour of a high elevation, Andean catchment. Thomas E. Shaw1, Alexis Caro1,2, Pablo Mendoza3, Álvaro Ayala4, Francesca Pellicciotti5,6, Simon Gascoin7,  James McPhee1,3 1 Advanced Mining Technology Center, Universidad de Chile, Santiago, Chile 2 Univ. Grenoble Alpes, CNRS, IRD, Grenoble-INP, Institut des Géosciences de l’Environnement (IGE, UMR 5001), Grenoble, France 3 Department of Civil Engineering, Universidad de Chile, Santiago, Chile 4 Centro de Estudios Avanzados en Zonas Áridas (CEAZA), La Serena, Chile 5 Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland 6 Department of Geography, Northumbria University, Newcastle, UK 7 CESBIO, Université de Toulouse, CNES/CNRS/INRA/IRD/UPS, Toulouse, France %------------------------------------------------------------- %------------------------------------------------------------- The following sub-folders are separated into forcings, grids, initial model conditions, model files and parameters. This file describes briefly the contents of each sub-folder. %------------------------------------------------------------- FORCINGS: CloudCover_TPK.csv - A timeseries of hourly cloud cover fraction (-) derived NASA POWER archives. Discharge_TPK.csv - A timeseries of hourly discharge (m3 s-1) from the outlet station F_TdP. Master_Data_TPK.mat - a Matlab structure (written in version 2017a) for all data availble to the catchment for the considered model period. Precipitation_TPK.csv - A timeseries of hourly precipitation (mm/hr) from AWS TdP. Temperature_TPK.csv - A timeseries of hourly temperature (degC) from AWS TdP.  TemperatureGradient_TPK.csv - A timeseries of calibrated temperature gradients based upon forcing from AWS TdP. %------------------------------------------------------------- GRIDS: 42 ascii files for various grids (primary or secondary) use to derive the .TES file (see TOPKAPI-ETH sub-folder) for running the model. Associated projection (.prj) files are given. Naming convention is provided in the manual (see TOPKAPI-ETH sub-folder) except:     rdy_SoilDepth.asc - An adjusted top layer soil depth map based upon Ragettli et al. (2012).     rdy_debris_v.asc - A debris thickness map for Piramde Glacier and the tongue of Bello Glacier. Values adjusted slightly from Ayala et al. (2016) to account for areas that are not debris, but bedrock (Bello Glacier). %------------------------------------------------------------- INITIAL_CONDITIONS: Sub-folder 'Albedo':      Albedo_Pleiades.asc - An albedo map derived from the model spin up and limited to the snow-covered pixels of the Pléiades snow depth map. Sub-folder 'Snow':     XXX_snow_mmwe.asc - A snow water equivalent map (mm w.e.) given by the initialisation method 'XXX' (Pléiades, TOPO or DBSM). TPK is derived solely from the model spin up (an input grid not required).      XXXeq_snow_mmew.asc - As above, though considering the equal means approach described in the manuscript. TPK included here. Sub-Folder 'SpinUp_State':     201709040000.stt - The system state file that contains information on the equiblibrium state of catchment (as read by the model upon initialisation). Running the model with a spinup shuld call upon this file within the command prompt. %------------------------------------------------------------- PARAMETERS: Sub-folder 'Calibration'     TPK_ParameterAllocation - A Matlab script for the establishing the Monte Carlo parameter simulation and running the model n times. The current script is considered for soil parameters. Sub-folder 'Sensitivity'     TPK_Sensivity_Analysis - A Matlab script for establishing the upper and lower boundaries of parameter/forcing sensitivities for a one-at-a-time analysis.   %------------------------------------------------------------- RESULTS: Model_Output_Comparison.mat - A matlab file with output grids and vectors for model intercomparisons (i.e. Pléiades (Pléiades-Uncertainty and Pléiades+Uncertainty), TOPO, TPK, DBSM + equal means equivalents). Files are:     All_S - Daily snow mass balance grids (mm w.e.)     Bias_Month - Monthly bias (row) of modelled vs measured streamflow at F_aP site for each model run (column).     Date_Daily - Numeric date of daily grids     DateTPK - Numeric date of hourly model simulations     Gla_Map - Daily cumulative glacier modelled mass balance grids (mm w.e.) for x,y,t,MOD - such that the 4th dimension is the model simulation     GMB_Bello - Cumulative modelled mass balance (mm w.e.) of Bello Glacier AWS grid cell     GMB_Piramide - Cumulative modelled mass balance (mm w.e.) of Piramide Glacier AWS grid cell     GMB_Yeso - Cumulative mass modelled balance (mm w.e.) of Yeso Glacier AWS grid cell     KGE_Month - KGE values per month (row) and for each model run (column)     M3AP - Measured streamflow at F_aP     M3TP - Measured streamflow at F_TdP     MeltG_Avg_all - Mean hourly ALL-glacier melt rate (mm w.e./hr) for each model run (column)     MeltS_Avg_all - Mean hourly catchment-wide melt rate (mm w.e./hr) for each model run (column)     MOD_SnowCC - Daily MODIS snow cover fraction     Model_Name - .... well, its the name of the model run :=)     MODIS_SLE - The calculated Snow Line Elevation (m a.s.l.) for each daily MODIS scene     PlanetSLE - As above, but for PlanetScope images (17 days total)     Planet_SnowObs - The numeric dates of the equivalent PlanetSLE data     Q_Mod_aP - The modelled hourly streamflow at F_aP     Q_Mod_TdP - The modelled hourly streamflow at F_TdP     Q_Prc_Month - The percentage difference in monthly (row) modelled-measured streamflow by model run (column)     R_Month - Correlation values per month (row) and for each model run (column)     RelVar_Month - Relative variance per month (row) and for each model run (column)     Snow_Map - Daily snow water equivalent grids (mm w.e.) for x,y,t,MOD - such that the 4th dimension is the model simulation     SnowCC - The hourly modelled snow cover fraction for the catchment for each model run (column)     TPKSLE - The daily modelled TOPKAPI-ETH model SLE from each model run (column)   %------------------------------------------------------------- TOPKAPI-ETH: FIUME - A generic file type that is called by the model to ID the name of the study site. I this case 'rdy'. rdy.TES - A vectorised file of all grids required by the model to run. rdy.TPK - The TPK parameter and command file. This is adjusted to change input parameters and forcing files etc. The current file is the optimised version for this catchment. TManual_Aug2013.pdf - A PDF instruction file (semi-complete) for the model written by Stefan Rimkus (2013). The naming conventions and grid names are given here.     CITED MATERIAL REGARDING THE MODEL Burger, F., Ayala, A., Farias, D., Shaw, T. E., Macdonell, S., Brock, B., McPhee, J., Pellicciotti, F. (2018a). Interannual variability in glacier contribution to runoff from a high ‐ elevation Andean catchment: understanding the role of debris cover in glacier hydrology. Hydrological Processes, SI-Latin(January), 1–16. <a href="https://doi.org/10.1002/hyp.13354">https://doi.org/10.1002/hyp.13354</a> Finger, D., Pellicciotti, F., Konz, M., Rimkus, S., & Burlando, P. (2011). The value of glacier mass balance, satellite snow cover images, and hourly discharge for improving the performance of a physically based distributed hydrological model. Water Resources Research, 47(7), 1–14. <a href="https://doi.org/10.1029/2010WR009824">https://doi.org/10.1029/2010WR009824</a> Ragettli, S., & Pellicciotti, F. (2012). Calibration of a physically based, spatially distributed hydrological model in a glacierized basin: On the use of knowledge from glaciometeorological processes to constrain model parameters. Water Resources Research, 48(3), n/a-n/a. <a href="https://doi.org/10.1029/2011WR010559">https://doi.org/10.1029/2011WR010559</a>

opencc-by-4.0Dec 2023View details →

Evaluating and Improving Snow in the National Water Model, using Observations from the New York State Mesonet

This dataset repository contains the dataset that supports the study of Minder et al. (2024): Evaluating and Improving Snow in the National Water Model, using Observations from the New York State Mesonet. The datasets in this archive include output from distributed simulations of the WRF Hydro model, output from point simulations with the Noah-MP land surface model, and manual and automated snow water equivalent (SWE) observations at New York State Mesonet (NYSM) station locations. The formatting and file naming conventions for each dataset are described in detail in the minder_etal_NWM_snow_dataset_ReadMe.pdf file.

opencc-by-4.0Jul 2024View details →

Global snow water equivalent product derived from machine learning model trained with in situ measurement data

This dataset is a global snow water equivalent dataset using machine learning trained with in-situ measurements. The temporal resolution of the SWEML product is daily, and the spatial resolution is 0.25˚ (approximately 25km). It covers latitudes of 90S to 90N and longitudes of 180W to 180E with global scales, excluding Antarctica. The dataset is provided in NetCDF format, organized by year. Each year contains daily SWE data, including leap days in leap years.

dryad36/100

Data from: Dynamic models for impact-initiated stress waves through snow columns

The objective of this research is to model snow's response to dynamic, impact loading. Two constitutive relationships are considered: elastic and Maxwell-viscoelastic. These material models are applied to laboratory experiments consisting of 1000 individual impacts across 22 snow column configurations. The columns are 60 cm tall with a 30 cm by 30 cm cross-section. The snow ranges in density from 135-428 kg m-3 and is loaded with both short-duration (~1 ms) and long-duration (~10 ms) impacts. The Maxwell-viscoelastic model more accurately describes snow's response because it contains a mechanism for energy dissipation, which the elastic model does not. Furthermore, the ascertained model parameters show a clear dependence on impact duration; shorter duration impacts resulted in higher wave speeds and greater damping coefficients. The stress wave's magnitude is amplified when it hits a stiffer material because of the positive interference between incident and reflected waves. This phenomenon is observed in the laboratory and modeled with the governing equations.

opencc-zeroFeb 2024View details →

opencc-by-4.0Mar 2022View details →

Assimilation of NASA's Airborne Snow Observatory snow measurements for improved hydrological modeling: A case study enabled by the coupled LIS/WRF-Hydro system

Data Analysis Scripts and Post-Processed Model Data for a case study using assimilation of ASO Snow Data into the NASA LIS/WRF-Hydro Model.  Manuscript Citation: Lahmers T. M., S. V. Kumar, D. Rosen, A. L Dugger, D. Gochis, J. A. Santanello, C. Gangodagamage, and R. Dunlap, 2020: Assimilation of NASA’s Airborne Snow Observatory snow measurements for improved hydrological modeling: A case study enabled by the coupled LIS/WRF-Hydro system,Water Resour. Res.,

opencc-by-4.0Jun 2022View details →

Modelling snowpack bulk density using snow depth, cumulative degree-days and climatological predictor variables -- data set

This file constitutes the data set containing the snow course survey, North American Regional Reanalysis (NARR)-derived degree-day indices, and climatological variables data used to conduct the analysis, and generate the figures and tables in the manuscript titled "Modelling snowpack bulk density using snow depth, cumulative degree-days and climatological predictor variables" by Andras J. Szeitz and R. Dan Moore. The manuscript was submitted for publication in the journal 'Hydrological Processes'. Due to the size of the NARR data files used to derive the air temperature time series for each snow course location, we recommend acquiring them from the National Oceanic and Atmospheric Administration's data portal directly (<a href="https://psl.noaa.gov/data/gridded/data.narr.html">https://psl.noaa.gov/data/gridded/data.narr.html</a>). Likewise, the ClimateNA software application used to extract the climatological variables for each snow course location can be obtained from the Centre for Forest Conservation Genetics, Department of Forest and Conservation Sciences, UBC, directly (<a href="https://climatena.ca/">https://climatena.ca/</a>).

opencc-by-4.0Apr 2018View details →

Dataset: Direct insertion of NASA Airborne Snow Observatory-derived snow depth time-series into the iSnobal energy balance snow model

This dataset is a companion to the submitted WRR publication entitled ‘Direct insertion of NASA Airborne Snow Observatory-derived snow depth time-series into the iSnobal energy balance snow model’. The file structure is organized as follows: <ul> <li>ASO_50m_depth_surfaces - This folder contains the Airborne Snow Observatory lidar-derived snow depth products aggregated to 50m gridded spatial resolution. Each file is titled with a date such as ‘TBYYYYMMDD_SUPERsnow_depth.asc’. The coordinates are in UTM zone 11N and use the WGS84 coordinate system.</li> <li>static_grids <ul> <li>Static grids are used in each of the subsequent folders and are not changed between years.</li> <li>init0000.ipw <ul> <li>Initialization file to begin the model run. Contains the digital elevation model in band 1, surface roughness raster in band 2, and zeroed images of snow properties in bands 3-7.</li> </ul> </li> <li>maxus.nc <ul> <li>netCDF file of 72 separate images of maximum upwind slope for all upwind directions from 0 (north) to 355 degrees in 5-degree increments. Derived using Adam Winstral’s Sx algorithm.</li> </ul> </li> <li>tuolx_dem_50m.ipw <ul> <li>Digital elevation model from ASO snow-free acquisition aggregated to 50m gridded spatial resolution. Same information as band 1 in the init0000.ipw file.</li> </ul> </li> <li>tuolx_hetchy_mask_50m.ipw <ul> <li>Basin mask of the Tuolumne River Basin above Hetch Hetchy Reservoir. Out-of-basin cells denoted as 0, and in-basin cells denoted as 1.</li> </ul> </li> <li>tuolx_vegheight_50m.ipw <ul> <li>Vegetation height raster in meters. Derived from NLCD dataset of vegetation type..</li> </ul> </li> <li>tuolx_vegk_50m.ipw <ul> <li>Emissivity of the vegetation canopy. Derived from NLCD dataset of vegetation type.</li> </ul> </li> <li>tuolx_vegnlcd_50m.ipw <ul> <li>Vegetation type from the National Land Cover Database.</li> </ul> </li> <li>tuolx_vegtau_50m.ipw <ul> <li>Fractional transmissivity of the vegetation canopy. Derived from NLCD dataset of vegetation type.</li> </ul> </li> </ul> </li> <li>level1_raw_data <ul> <li>{Hourly data interpolated to nearest hour from downloaded raw data (CDEC/MesoWest)}</li> <li>air_temp_level1.csv</li> <li>precip_accum_level1.csv</li> <li>relative_humidity_level1.csv</li> <li>solar_radiation_level1.csv</li> <li>wind_direction_level1.csv</li> <li>wind_speed_level1.csv</li> </ul> </li> </ul> The directories for each water year contain the configuration file for that year along with the vector meteorological data from measurement sites and site metadata in .csv format. <ul> <li>wy2013</li> <li>wy2014</li> <li>wy2015</li> <li>wy2016 <ul> <li> <ul> <li>backup_config.ini {Initialization file used to distribute station data over a regular grid for each water year.}</li> <li>air_temp.csv</li> <li>cloud_factor.csv</li> <li>metadata.csv</li> <li>precip.csv</li> <li>vapor_pressure.csv</li> <li>wind_direction.csv</li> <li>wind_speed.csv</li> <li>data/ <ul> <li>[subdirectory containing all future created forcing grid files]</li> </ul> </li> <li>runs/ <ul> <li>[subdirectory containing all iSnobal output files in addition to reinitialization scripts for ASO snow depth updates]</li> </ul> </li> </ul> </li> </ul> </li> </ul>