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22 results for “Snow observations”
Dataset for the publication "Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site"
<p>This datasset contains data to reproduce the following figures of the paper <em>Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site</em>:</p> <ul> <li> <p>Time series data of Figures 1c and 2</p> </li> <li> <p>Data (*.asc) used for plotting Figures 1d and 1e (as well as Figure S3 and S4)</p> </li> <li>Pléiades snow depth map (Figure S1)</li> <li> <p>Data used for plotting Figure S2</p> </li> </ul> <p> </p>
Snow cover in the European Alps: Station observations of snow depth and depth of snowfall
<p>Auxiliary files, code, and data for paper published in The Cryosphere:</p> <p>Observed snow depth trends in the European Alps 1971 to 2019</p> <p> <a href="https://doi.org/10.5194/tc-15-1343-2021">https://doi.org/10.5194/tc-15-1343-2021</a></p> <p> </p> <p><strong>Auxiliary files:</strong></p> <ul> <li>aux_paper.zip: Auxiliary figures to the paper (time series showing the consistency of averaging monthly mean snow depth of stations within 500 m elevation bins; times of seasonal snow depth and snow cover duration indices).</li> <li>aux_paper_crocus_comparison.zip: Time series comparing spatial statistical gap filling from paper to gap filling using snow depth assimilation into Crocus snow model (only for subset of stations in the French Alps)</li> <li>aux_paper_monthly_time_series.zip: Plots of monthly time series of snow depth, for each station.</li> <li>aux_paper_spatial_consistency.zip: Aggregate results from spatial consistency (statistical simulation using neighboring stations), and time series of observed versus simulated monthly snow depths.</li> </ul> <p> </p> <p><strong>Code </strong>(working copy, not cleaned, all written in R statistical software): code.zip</p> <ul> <li>to read in the different data sources</li> <li>to do quality checks and data processing</li> <li>to perform statistical analyses as in paper</li> <li>to produce figures and tables as in paper</li> </ul> <p> </p> <p><strong>Data</strong>:</p> <ul> <li>> 2000 stations from Austria, Germany, France, Italy, Switzerland, and Slovenia</li> <li>Daily stations snow depth and depth of snowfall, as .zips, grouped by data provider. Information on column content is provided in "data_daily_00_column_names_content.txt".</li> <li>Monthly stations mean snow depth, sum of depth of snowfall, maximum snow depth, days with snow cover (1-100cm thresholds), as .zips, grouped by data provider. Information on column content is provided in "data_monthly_00_column_names_content.txt".</li> <li>Meta data (name, latitude, longitude, elevation) in "meta_all.csv", along with an interactive map "meta_interactive_map.html", and column information in "meta_00_column_names_content.txt".</li> <li>If you <strong>use the data you agree to adhere to the respective data provider's terms</strong> as listed in "00_DATA_LICENSE_AND_TERMS.PDF"</li> <li>The license terms especially (and additionally to any other terms of the single data providers) include: <strong>Attribution</strong> — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. [from <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a>] </li> </ul> <p> </p> <p> </p> <p><strong>Version history:</strong></p> <p>v1.3: added maxHS and SCD (with various 1-100cm thresholds) to monthly data</p> <p>v1.2: uploaded data</p> <p>v1.1: changes to aux-paper.zip and code.zip as consequence from submitting a revised manuscript</p> <p>v1.0: initial upload</p>
Terrestrial Laser Scanner observations of snow depth distribution at Col du Lautaret and Col du Lac Blanc mountain sites
<p>This dataset contains snow depth distribution observations obtained in two high mountain experimental sites, Col du Lac Blanc and Col du Lautaret, both located in French Alps. The snow depth distribution maps were generated using a Terrestrial Laser Scanner (TLS) for 10 acquisition dates. Observations obtained in Col du Lac Blanc were acquired in the 2014-15 snow season while Col du Lautaret observations were acquired in 2017-18 snow season. The snow depth maps have a grid cell size of 1x1m. The two study sites have extensions comprised between 17 and 31 ha with elevations ranging from 2000-2100 m a.s.l. (Col du Lautaret)and 2600-2800 m a.s.l. (Col du Lac Blanc)and show a patchy distribution of bare soil and alpine grass. The dataset allows a better understanding of snow related processes in mountain areas.</p>
UAV observations of the NDVI, snow depth and melt out date, retreived ar the Izas Experimental Catchment in 2020 and 2021
<p>This dataset includes very high spatial resolution observations at 1 m spatial resolution observations of the snow depth, the NDVI and the melt-out date (DOY of year) acquired with an Unmanned Aerial Vehicle at a sub-alpine site in the Pyrenees, the Izas Experimental Catchment. During two snow seasons (2019-2020 and 2020-2021), 14 NDVI and 17 snow depth distributions were acquired over 48ha. From the snow depth observations the melt-out dates have been derived. Also information on the main topographic variables (elevation, aspect and slope) is included, with same spatial resolution, in this dataset.</p>
Inputs (forcing, observations and config file) for the experiments included in "Spatio-temporal snow data assimilation with the ICESat-2 laser altimeter".
<p>Inputs or the experiments included in the manuscript <a href="https://doi.org/10.5194/egusphere-2024-1404">Spatio-temporal snow data assimilation with the ICESat-2 laser altimeter</a>. </p> <p>Three experiment's inputs (forcing, observations and config file) for the Multiple Snow data Assimilation system (<a href="https://doi.org/10.5281/zenodo.11147258">MuSA</a>, v2.1) for the experimental catchment of Izas in the Spanish Pyrenees. All the experiments use ERA5 data downscaled to 20 m spatial resolution with the statistical downscaling tool <a href="https://doi.org/10.21105/joss.05059">TopoPySCALE</a>. The experiments assimilate different variables. </p> <p> C) assimilation of fSCA retrieved from Sentinel-2;</p> <p> D) assimilation of snow depth profiles retrieved with ICESat-2;</p> <p> J) joint assimilation of variables in C) and D).</p> <p> </p> <p>All the experiments assimilate the observations with the deterministic ensemble smoother with multiple data assimilation (DES-MDA) scheme.</p>
Sea ice, snow and melt pond example data from MOSAiC transect observations
<p>This data set contains in-situ observation of sea ice, snow and melt pond properties from two days in winter (January 23, 2020) and summer (July 7, 2020) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. It originates from two sensors:</p> <ol> <li>Broad-band electromagnetic induction sensor (Geophex GEM-2) measuring the combined thickness of the sea ice and snow layers</li> <li>A GPS snow depth probe (Snow-Hydro MagnaProbe) measuring the thickness of the snow layer and melt ponds depth during summer</li> </ol> <p>Both sensors were operated coincidently along transect loops while different loops were used in both days. This data set is a subset of similar weekly activities between October 2019 and September 2020. This publication intends to provide a preview of the data properties and approximate changes between the winter and summer periods. It also has to be noted, that the final data of the EM induction sensor might differ from this release, which is based on a quick-look processing directly after data acquisition.</p> <p><em>GEM-2 data files</em></p> <p>The file format of GEM-2 data is a text file with comma-separated values. Notable parameters are:</p> <ul> <li>‘time’: UTC time</li> <li>‘longitude’: Longitude in degrees east (fill value: 0.0)</li> <li>‘latitude’: Latitude in degrees north (fill value: 0.0)</li> <li>‘`f{frequency}Hz_hcp_{i:Inphase|q:Quadrature}`: total (ice + snow) thickness of the sea ice and snow layers in meter for different channels*</li> </ul> <p>*The channels correspond to the real (Inphase) or imaginary (Quadrature) part of the EM signal at a given frequency. The variable name in the csv file is to be read as `f{frequency}Hz_hcp_{i:Inphase|q:Quadrature}`. It is recommended to use the Inphase component of the 18.325 kHz frequency (variable `f18325Hz_hcp_i`) for analysis.</p> <p><em>MagnaProbe data files</em></p> <p>The file format of MagnaProbe data is a text file with comma-separated values. Notable parameters are:</p> <ul> <li>`timestamp`: Timestamp</li> <li>`longitude_a`: longitude degree in degrees east</li> <li>`longitude_b`: longitude minute</li> <li>`latitude_a`: latitude degree in degrees north</li> <li>`latitude_b`: latitude minute</li> <li>`depthCm`: Snow thickness or melt ponds depth in cm</li> <li>`flag`: flag value indicating the type or measurement*</li> </ul> <p>Flag values are:</p> <ul> <li>-1 : melt pond</li> <li>1 : snow or surface scattering layer depth,</li> <li>2 : mixed surface type when pond water pools at the base of a melting</li> </ul> <p>The filenames follow the naming convention of <sensor>-mosaic-transect-<date>-<device-operations-id>.csv with the device operation id as a unique identifier of the sensor raw data within the MOSAiC project.</p>
Tahoe rain or snow precipitation phase observations
<p>These data include observations of rain, snow, and mixed precipitation from the Tahoe Rain or Snow citizen science project. Included with each observation is a set of ancillary variables, including latitude and longitude, elevation, modeled meteorological data, and additional info. Please see the metadata file for the description and units of each data column.</p> <p>For more info, please see Jennings et al. (2023) and Arienzo et al. (2021):</p> <ul> <li>Jennings, Keith S., Monica M. Arienzo, Meghan Collins, Benjamin Hatchett, Anne W. Nolin, and Graeme Aggett. "Crowdsourced Data Highlight Precipitation Phase Partitioning Variability in Rain-Snow Transition Zone." Earth and Space Science (2023). <a href="https://doi.org/10.1029/2022EA002714">https://doi.org/10.1029/2022EA002714</a> </li> <li>Arienzo, Monica M., Meghan Collins, and Keith S. Jennings. "Enhancing engagement of citizen scientists to monitor precipitation phase." Frontiers in Earth Science 9 (2021): 617594. <a href="https://doi.org/10.3389/feart.2021.617594">https://doi.org/10.3389/feart.2021.617594</a> </li> </ul> <p>For the code used to process these data: <a href="https://github.com/SnowHydrology/MountainRainOrSnow/tree/tahoe_ros">https://github.com/SnowHydrology/MountainRainOrSnow/tree/tahoe_ros</a></p>
Tahoe rain or snow precipitation phase observations
Open the record for dataset details and reuse information.
Sequence of snow maps produced from Sentinel-2 type of observations (SPOT-5 Take 5) over the Deux Alpes and Alpe d'Huez ski resorts
<p>This is a series of snow cover maps between April 11 and September 8, 2015 over a region that covers the Deux Alpes and Alpe d'Huez ski resorts in France. The snow maps were produced from a SPOT-5 Take 5 images using the "Let-it-snow" processor (v1.0, June 2016: http://tully.ups-tlse.fr/grizonnet/let-it-snow).</p> <p>The SEB folder contains 20 GeoTiff at 10 m résolution in Lambert-93 projection system coded as follows (cf. http://tully.ups-tlse.fr/grizonnet/let-it-snow#products-format):</p> <p> 0: No-snow<br> 100: Snow<br> 205: Cloud including cloud shadow<br> 254: No data</p> <p>The "anim_no05jul_opt" animated gif was produced from these data after performing a simple temporal interpolation given by the following rules:</p> <ul> <li>If a pixel masked by a cloud was marked as snow in the preceding image and in the following image, then it is reclassified as a snow pixel.</li> </ul> <ul> <li>If a pixel masked by a cloud was marked as no-snow in the preceding image and in the following image, then it is reclassified as a no-snow pixel.</li> </ul> <p>The July 05 image was removed from the animation due to a cloud/snow confusion. This type of error should be avoided with Sentinel-2 data thanks to an additional "high cloud" test (see http://tully.ups-tlse.fr/grizonnet/let-it-snow/blob/master/doc/tex/ATBD_CES-Neige.pdf).</p> <p>These data were featured in this blog post : Gascoin, S. "Monitoring the snow cover in ski resorts using Sentinel-2" (19-Sep-2016) http://www.cesbio.ups-tlse.fr/multitemp/?p=8676</p>
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.
Complementary observations aid identification of the mountain rain-snow transition elevation
<p>If data have a time stamp, all data are in UTC</p> <p>Includes:</p> <ul> <li>precipitation gauge [meters] data from California Data Exchange (CDEC)</li> <li>SNOTEL data (soil moisture [%], snow water equivalent [meters])</li> <li>snow depth [meters] data from snow pillow sites from CDEC</li> <li>snow water equivalent [meters] from snow pillow sites from CDEC</li> <li>temperature [degrees C] data from snow pillow sites from CDEC</li> <li>FMCW radar freezing level [km above the surface] data from NOAA</li> <li>MODSCAG remote sensing data [rgb, grain size, snow covered area, zenith angle] from JPL</li> </ul>
Time-lapse observations of snow bedforms in the Colorado Front Range, 2016-2017
<p><strong>Description:</strong> Time-lapse imagery of snow surfaces on Niwot Ridge, in the Colorado Front Range, between March 2016 and April 2017.</p> <p><strong>Purpose:</strong> Videos show the evolution of snow bedforms, including the formation and movement of snow dunes, snow ripples, and sastrugi. Used to analyze the modes of bedform movement, and to constrain the weather conditions in which they form.</p> <p><strong>Equipment, details + weather data: </strong>Time-lapse cameras are Day6 Plotwatcher Pros. They take images every 10s during daylight hours. The cameras are located at -40.054307, -105.590764, 150m upwind of the Niwot Ridge LTER Saddle Station. Bedform movement can thus be correlated with climate and precipitation data from the station, provided at niwot.colorado.edu</p> <p><strong>Filenames:</strong> Files are named yymmddAA.avi, where yy/mm/dd is the date of the recording. When multiple recordings were made in one day, there will be a yymmddAB.avi, yymmddAC.avi, etc. Some filenames are annotated with a description, eg 170226AA-sastrugi.avi. On a few dates (November 2016) the cameras recorded incorrect dates; on these days, the dates in the filenames are to be preferred.</p> <p>Date and time in video: the date, time and temperature are recorded in the bottom of the videos. The date and time are generally accurate, but if they differ from the date in the filename, that date is to be preferred and the time disregarded. Temperature in the film is generally colder than temperature recorded by the nearby weather station, and we generally disregarded it in favor of climate data from niwot.colorado.edu.</p> <p><strong>Data format:</strong> Data are presented as .avi files, which may be run by most video players. Data were originally recorded with proprietary .tlv file extension. The .tlv format is equivalent to .avi; if any .tlv files are found, simply rename them as .avi files and play as usual.</p> <p><strong>Acknowledgements:</strong> This research was supported by a Department of Energy Computational Science Graduate Fellowship (DE-FG02-97ER25308), by a University of Colorado Chancellor's Fellowship, and by the National Science Foundation via support for the Boulder Creek Critical Zone Observatory (EAR-1331828).<br> Field equipment was funded by the American Alpine Club; the Colorado Scientific Society; and a Patterson Award from the University of Colorado Department of Geological Sciences. Field assistants were funded by the University of Colorado Undergraduate Research Opportunities Program. Logistical support and climate data were provided by the NSF-supported Niwot Ridge Long-Term Ecological Research Project and the University of Colorado Mountain Research station.</p>
Data from: Snow-cover seasonality in Kyrgyzstan: Variation and change over 20 years (2001-2021) as observed by the MODIS Terra snow product
Open the record for dataset details and reuse information.
Snow and sea ice observations during the field phase of MOSAiC
<p>Measurements of discrete and continous snow and sea ice measurements during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. The tables contain the observational dates from various methods as described in Nicolaus et al. (2021). The dates refer to Figure 3 in this publication.</p> <p> </p> <p>Reference:</p> <p>Nicolaus M, Perovich D, Spreen G, Granskog M, Albedyll L, Angelopoulos M, Anhaus P, Arndt S, Belter H, Bessonov V, Birnbaum G, Brauchle J, Calmer R, Cardellach E, Cheng B, Clemens-Sewall D, Dadic R, Damm E, Boer G, Demir O, Dethloff K, Divine D, Fong A, Fons S, Frey M, Fuchs N, Gabarró C, Gerland S, Goessling H, Gradinger R, Haapala J, Haas C, Hamilton J, Hannula H-R, Hendricks S, Herber A, Heuzé C, Hoppmann M, Høyland K, Huntemann M, Hutchings J, Hwang B, Itkin P, Jacobi H-W, Jaggi M, Jutila A, Kaleschke L, Katlein C, Kolabutin N, Krampe D, Kristensen S, Krumpen T, Kurtz N, Lampert A, Lange B, Lei R, Light B, Linhardt F, Liston G, Loose B, Macfarlane A, Mahmud M, Matero I, Maus S, Morgenstern A, Naderpour R, Nandan V, Niubom A, Oggier M, Oppelt N, Pätzold F, Perron C, Petrovsky T, Pirazzini R, Polashenski C, Rabe B, Raphael I, Regnery J, Rex M, Ricker R, Riemann-Campe K, Rinke A, Rohde J, Salganik E, Scharien R, Schiller M, Schneebeli M, Semmling M, Shimanchuk E, Shupe M, Smith M, Smolyanitsky V, Sokolov V, Stanton T, Stroeve J, Thielke L, Timofeeva A, Tonboe R, Tavri A, Tsamados M, Wagner D, Watkins D, Webster M, Wendisch M. 2021. Overview of the MOSAiC expedition – Snow and sea ice. Elementa Science of the Anthropocene 9. doi:10.1525/elementa.2021.000046.</p> <p> </p> <p> </p>
Inputs (forcing and observations) ready for use by 'MuSA: The Multiscale Snow Data Assimilation System (v1.0)'
<p>MuSA is a snow data assimilation system that can fuse observations with snowpack simulations generated by the Flexible Snow Model (FSM2). This repository contains meteorological forcing developed with the Micromet model and 5 m resolution snow depth observations in the Izas basin (central Spanish Pyrenees) generated with drones, ready to be used in MuSA.</p>
Dataset from "How does a warm and low-snow winter impact the snow cover dynamics in a humid and discontinuous boreal forest? Insights from observations and modeling in eastern Canada"
<p>The dataset presented below is described in the publication “<em>How does a warm and low-snow winter impact the snow cover dynamics in a humid and discontinuous boreal forest? An observational study in eastern Canada.</em>” from Bouchard et al. (submitted) in the journal Hydrology and Earth System Science.</p> <p>The original dataset includes <strong>monitoring data</strong> collected at Montmorency Forest (47.29°N, 71.17°W) from 15 October 2020 to 15 June 2021 (W20-21) and from 15 October 2021 to 15 June 2021 (W21-22) in a medium-size gap, the small-size gap and under the canopy. The study site is a balsam fir – whit birch stand on a 12° slope of north-east aspect. In the monitoring dataset you can find at the hourly timestep:</p> <ul> <li>Snow depth (cm)</li> <li>Soil temperature at 20 cm, 10 cm and 5 cm below ground surface (°C)</li> <li>Soil-snow interface temperature (°C)</li> <li>Snow temperature every 15 cm from the ground surface (°C)</li> <li>Snow surface temperature (°C)</li> <li>Air temperature (°C)</li> <li>Relative humidity (%)</li> <li>Soil volumetric water content at 15 cm below the ground surface (0 – 1)</li> </ul> <p>The dataset also includes <strong>snow pit observations</strong> taken at Montmorency Forest during W20-21 and during W21-22. Each winter, four (4) snow pits were dug inside medium-size gaps, small-size gaps and at subcanopy locations. Snow pit measurement dates are presented in Bouchard et al. (submitted). Each snow pit includes the vertical profile of:</p> <ul> <li>Snow stratigraphy</li> <li>Snow temperature</li> <li>Snow density</li> <li>Snow specific surface area (SSA)</li> </ul> <p> </p> <p>The snow pit height corresponds to the upper boundary of the topmost snow layer in the stratigraphy profile. For density measurements, the height value corresponds to the center of the 3-cm thick box cutter. For the SSA, the value is measured optically at the top of the sample. This value is representative of the top 1 cm of the snow sample, as this is the typical e-folding depth of 1310 nm radiation in snow. Grain type codes for the snowpack stratigraphy correspond to the <em>International Classification for Seasonal Snow </em>(Fierz et al., 2009):</p> <ul> <li>PP: precipitation particles </li> <li>DF: decomposed and fragmented precipitation particles</li> <li>RG: rounded grains</li> <li>FC: faceted crystals</li> <li>FCxr: rounding faceted particles</li> <li>DH: depth hoar</li> <li>MFpc: melt forms – rounded polycrystals</li> <li>MF: melt forms – clustered rounded grains</li> <li>MFcr: melt forms – melt-freeze crusts</li> <li>IF: ice formations</li> </ul>
IT-SNOW: a snow reanalysis for Italy blending modeling, in-situ data, and satellite observations
<p>IT-SNOW is a serially complete and multi-year snow reanalysis for Italy. The dataset includes daily maps of Snow Water Equivalent (SWE), snow depth (HS), bulk-snow density (RhoS), and liquid water content (Theta_W). </p> <p>Data are organized in monthly netCDF files, each providing time and lat/lon information for georeference. Units are as follows: HS is in cm, SWE is in mm w.e., RhoS is in kg/m3, and Theta_W is in %. Note that maps are instantaneous snapshots at 11AM UTC, here assumed as representative values for the day. </p> <p>As the output of an operational chain employed in real-world civil-protection applications (S3M Italy), IT-SNOW ingests input data from thousands of automatic weather stations, snow-covered-area maps from Sentinel 2, MODIS, and H-SAF products, and maps of snow depth from the spazialization of 1000+ on-the-ground snow-depth sensors. Additional information are available in the following paper submitted to Earth System Science Data: </p> <p>"IT-SNOW: a snow reanalysis for Italy blending modeling, in-situ data, and satellite observations (2009-2021)", Francesco Avanzi et al., 2022. </p> <p>The initial time span of data is September 1, 2010 to August 31, 2021, with future updates envisaged on an annual basis (see updates below).</p> <p><strong>UPDATES</strong></p> <ul> <li>September 29, 2025: released v5 with the complete 2025 water year (September 2024 - August 2025).</li> <li>November 12, 2024: released v4 with the complete 2024 water year (September 2023 - August 2024).</li> <li>September 02, 2024: released v3.1 with the complete 2023 water year (September 2022 - August 2023) AND all previous water years (which were inadvertently NOT carried over while creating v3).</li> <li>September 02, 2024: released v3 with the complete 2023 water year (September 2022 - August 2023).</li> <li>December 20, 2023: released v2 with the complete 2022 water year (September 2021 - August 2022).</li> </ul> <p>LICENSE INFORMATION</p> <p>IT-SNOW is distributed under a CC BY-NC 4.0 license. you are free to: </p> <p>1. Share — copy and redistribute the material in any medium or format; <br>2. Adapt — remix, transform, and build upon the material;</p> <p>under the following terms: </p> <p>a. Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.<br>b. NonCommercial — You may not use the material for commercial purposes.</p> <p><br>DATA ARE PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THESE DATA, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.</p> <p>For details about the CC BY-NC 4.0 license, see: https://creativecommons.org/licenses/by-nc/4.0/deed.en</p>
Large-eddy simulation model data for article Study of surface layer characteristics in the presence of suspended snow particles using observational data and large-eddy simulation
<p>Large-eddy simulation model data [8. Mortikov E.V., Glazunov A.V., <br> Lykosov V.N. Numerical study of plane Couette flow: <br> turbulence statistics and the structure of pressure-strain correlations <br> // Russ. J. Numer. Analysis Math. Model. 2019. V. 34. № 2. P. 119–132.]<br> The setup of experiments was based<br> on the GABLS-1. The height, width and length of the domain was 4000 m<br> with spatial resolution of 11.7m. U18, U16 - geostrophic wind 18 and 16 m/s,<br> CR - cooling rate 0K/h, 1K/h, 2K/h.<br> Two series of experiments were performed: “NS“ and “SS“.<br> In the experiment “NS“ the surface layer was described according to the<br> Monin-Obukhov similarity theory. The experiments “SS“<br> utilized the parameterization, which takes into account the effect<br> snow particles.</p>
CanSISE Observation-Based Ensemble of Northern Hemisphere Terrestrial Snow Water Equivalent, Version 2
This data set is a daily gridded terrestrial snow water equivalent (SWE) dataset based on five component SWE products:<ul><li><a href="http://www.globsnow.info">GlobSnow combined SWE product (passive microwave/ground-based weather station, version 2)</a></li><li><a href="http://apps.ecmwf.int/datasets/.">ERA-Interim/Land reanalysis SWE product</a></li><li><a href="http://gmao.gsfc.nasa.gov/pubs/docs/Reichle541.pdf">MERRA reanalysis SWE product </a></li><li>Crocus SWE data set: output from the Crocus snowpack model, driven by ERA-Interim meteorology (<a href="http://dx.doi.org/10.1175/JHM-D-12-012.1">Brun et al. 2013</a>)</li><li>GLDAS SWE product (version 2) (<a href="http://dx.doi.org/10.1175/BAMS-85-3-381">Rodell et al. 2004</a>; <a href="http://dx.doi.org/10.5067/0JNJQ8ZDZRBA">Rodell and Beaudoing 2013</a>)</li></ul>
CLPX-Ground: Snow Measurements at the Local Scale Observation Site (LSOS), Version 1
This data set presents snow depth, snow water equivalence (SWE), snow wetness data, and snow pit data from two pine sites and a small clearing at the Local Scale Observation Site (LSOS) of the Cold Land Processes Field Experiment (CLPX) in northern Colorado.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.