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966 results for “Snow”
ACS_Bayelva_class: 302 high-resolution snow cover maps covering the 2012-2017 snowmelt seasons in the Bayelva catchment (Svalbard, Norway)
<p>The ACS_Bayelva_class dataset contains 302 high-resolution binary snow cover images that were obtained by classifying orthrorectified photographs of a 1.77 km^2 area of interest in the Bayelva catchment. This latest version (2.0) of the dataset includes the orthorectified photographs that were used to classify the binary snow cover images. The catchment is close to Ny-Ålesund, the northernmost permanent civilian settlement in the world and a major hub for polar research, in the Norwegian high-Arctic Svalbard archipelago. The imagery has a (roughly) daily temporal resolution and a ground sampling distance (pixel spacing) of 0.5 m. The dataset spans 6 snowmelt seasons, covering the months May-August for the period 2012-2017. The orthophotos were obtained by processing oblique time-lapse photographs taken by a terrestrial automatic camera system (ACS) mounted at 562 m a.s.l. near the summit of Scheteligfjellet (719 m a.s.l.) a few kilometers west of Ny-Ålesund. The orthophotos were manually classified into binary snow cover images (0=no snow, 1=snow) by iteratively selecting a (visually) optimal threshold on the intensity in the blue-band for each image. More details are provided in the study of Aalstad et al. (2020) [a copy is available in this repository] where this dataset was created. The ACS was maintained by scientists from the group of Sebastian Westermann at the Section for Physical Geography and Hydrology in the Department of Geosciences at the University of Oslo, Oslo, Norway. </p>
Citizen science snow measurements
<p>Data set includes citizen science observations of snow collected mainly from Finland and Sweden. Data set is collected in CHARTER project with a simplified protocol which follows the international snow observational standards. Data set includes 47 measurement occasions. The protocol includes background information such as measurement date and time, location, description of surroundings, reindeer pasture type, and visible trampling or digging in snow. Measurements includes snow depth in 1-5 points and definition of ice and crust layers at 1-2 of the points. A hardness hand test is used for layer detection (pushing snow first with fist, then with 4 fingers, 1 finger, pencil and knife blade, until snow is too hard to be pierced). For each ice and crust layer, distance of the layer top and bottom from the ground is measured. In addition, it was optional to measure properties of all layers in snowpack (hardness, grain type and distances from the ground) and the snow water equivalent by using cylindrical tube to extract and weight sample of snow.</p> <p>Data set includes date, time, location, longitude, latitude, air temperature, signs of foraging, description of surroundings, type of reindeer pasture, ground, snow height, description of snow conditions with your own language, layer distances from ground, grain type for layers, hardness for layers, snow water equivalent (tube diameter, snow height, weight), recent rain on snow events, comments and links to photos.</p>
Dataset used in snow algae model
<p>This is a data set for the numerical simulation using the snow algae model (Onuma et al., 2018; 2020).<br> The content is as below.</p> <p>- 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).</p> <p>- python: programs for the visualization (python scripts)</p> <p>- figure: png files created by the python scripts</p> <p>The codes of the snow algae model can be downloaded below.<br> https://github.com/YukihikoOnuma/SnowAlgaeModel</p>
Snow equi-temperature metamorphism described by a phase-field model applicable on micro-tomographic images: prediction of microstructural and transport properties
<p>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".</p> <p>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.</p> <p>In this dataset, you will find two types of files:</p> <p>- the microstructural properties (density, specific surface area, covariance lengths, mean curvature) computed on the simulated images at different time steps.</p> <p>- the transport properties (effective conductivity, normalizes effective vapor diffusion coefficient, permeability) of the simulated images at different time steps.</p> <p>Finally, metadata_simulations.csv gather the information relative to the simulations.</p>
Snow Spotter Canopy-Snow Interception Dataset #1
<p>This dataset was used in a manuscript titled "<em>Evaluating multiple canopy-snow unloading parameterizations in SUMMA with time-lapse photography characterized by citizen scientists</em>" submitted to AGU WRR by the corresponding first-author, Cassie A. Lumbrazo (lumbraca@uw.edu). <br> An excerpt from the manuscript is provided below. Please see the published manuscript for the full dataset summary and the dataset ReadMe file for information on the dataset structure. </p> <p>Time-lapse images from high-resolution digital cameras were collected from the PhenoCam Network (Milliman et al., 2018), the 2017 NASA SnowEx Field Campaign (Currier et al., 2019; Kim et al., 2017), and the Olympic Mountain Experiment (OLYMPEX) ground validation campaign (Currier et al., 2017; Houze et al., 2017; Lundquist et al., 2018). These images were then uploaded to a citizen science platform called Zooniverse (<a href="https://www.zooniverse.org/"><em>Zooniverse.org</em></a>) and classified by thousands of volunteers as part of the <em>Snow Spotter</em> project. </p> <p>The goal of Snow Spotter was to mobilize citizen scientists to collect information about canopy interception. A total of 6,700 volunteer citizen scientists responded to questions about 13,600 images from sites across the United States to create this first canopy-snow interception Snow Spotter dataset. </p>
Range shifts of overwintering birds depend on habitat type, snow conditions and habitat specialization
<p>Data and R code accompanying the publication "Range shifts of overwintering birds depend on habitat type, snow conditions and habitat specialization"</p> <p>Bosco L, Xu Y, Deshpande P, Lehikoinen A</p> <p>2022</p> <p>---------</p> <p>The data and code to calculate range shifts based on the center of gravity are provided here.</p> <p>The RData files contains raw data from the winter bird counts with added average snow depth values downloaded from open source databases (described in the paper), 100x100km grid info (grid ID, centroid coordinates and average (geographical) coordinates).</p> <p>The csv file contains the route lengths from the winter bird count transects per habitat type.</p> <p>The R file contains the R code used to clean the data (see methods in the publication) and calculate the habitat specific center of gravity (based on bird densities) which were used to calculate shift direction and distance.</p>
A meteorology and snow dataset from adjacent forested and meadow sites at Crested Butte, CO, USA
<p>This dataset contains meteorology and snow observation data collected at sites in the southwestern Colorado Rocky Mountains during water years 2019-2021. Data collection had an emphasis on paired open-forest sites and included three forested elevations. In total, we present 270 snow pit observations, 4,019 snow depth measurements, and three years of meteorological forcing from two weather stations (one in a meadow, the other in an adjacent forest). The dataset is described in a forthcoming publication of the same name: <em>A meteorology and snow dataset from adjacent forested and meadow sites at Crested Butte, CO, USA</em> (Bonner et al., 2022).</p> <p>All snow observation and meteorological forcing data are available as both .nc and .mat files.<br> Additionally, original digitized copies of snow pit observations are provided as .gsheet/.xlxs files.</p> <p>This dataset will continue to be updated, via this repository, as additional years of data are collected.</p>
Data from: Cross-scale regulation of seasonal microclimate by vegetation and snow in the Arctic tundra
<p>The zip file contains data and code from the analyses for von Oppen et al. (2022) <em>Global Change Biology</em> (<a href="https://doi.org/10.1111/gcb.16426">https://doi.org/10.1111/gcb.16426</a>). Access through the provided R project file (e.g. with RStudio) is recommended for seamless running of the code. Please see the paper (link below) for methodological details, results and discussion, and the ReadMe included in the archive for further detail and usage policy.</p>
Dataset – Illuminating snow droughts
<p>This dataset contains snow pack, temperature, and precipitation information from GFDL's SPEAR MED large ensemble over the Western United States between 1914 and 2100 under SSP2-4.5 and SSP5-8.5 scenarios used in our paper <em>Illuminating snow droughts: The future of Western United States snowpack in a high-resolution coupled global climate model.</em> This dataset is connected with repository identified by DOI: 10.5281/zenodo.7130303 and can be found at: https://github.com/Julians42/Snow_Droughts. </p>
Data and code for: Precipitation Biases and Snow Physics Limitations Drive the Uncertainties in Macroscale Modeled Snow Water Equivalent
<p>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/).</p> <p>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. </p> <p>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.</p> <p>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>)</p>
Supporting data for "Forest carbon uptake as influenced by snowpack and length of photosynthesis season in seasonally snow-covered forests of North America"
<p>This is a supporting dataset for the paper :</p> <div> <div>Yang, J. C., Bowling, D. R., Smith, K. R., Kunik, L., Raczka, B., Anderegg, W. R. L., Bahn, M., Blanken, P. D., Richardson, A. D., Burns, S. P., Bohrer, G., Desai, A. R., Arain, M. A., Staebler, R. M., Ouimette, A. P., Munger, J. W., and Litvak, M. E.: Forest carbon uptake as influenced by snowpack and length of photosynthesis season in seasonally snow-covered forests of North America, Agricultural and Forest Meteorology, 353, 110054, <a href="https://doi.org/10.1016/j.agrformet.2024.110054">https://doi.org/10.1016/j.agrformet.2024.110054</a>, 2024.</div> </div> <p>Descriptions and units for each column can be found in a dedicated page within the data file. Methods are decribed in the paper.</p>
Supplementary data: Rain-on-snow events in mountainous catchments under climate change
<p>The file in this record represents supplementary data for the journal paper Hotovy, O., Nedelcev, O., Seibert, J., Jenicek, M. (2024): Rain-on-snow events in mountainous catchments under climate change submitted to Hydrology and Earth System Sciences.<br>The presented files contain daily simulations of the HBV rainfall-runoff model for 93 mountain catchments in Czechia, Germany and Switzerland. The model simulated different water balance components, such as runoff, base flow, snow water equivalent, evapotranspiration, and soil and groundwater storages for the study period 1980-2010 as well as hydrological projections assuming different increases in air temperature and precipitation.</p>
Satellite-derived monthly Arctic winter sea ice thickness, snow depth, freeboards, ice draft, and bulk ice density (2011-2022) and validation datasets
<h1><strong>[Description]</strong></h1> <p>This dataset is curated for a manuscript published in Earth and Space Science by Hoyeon Shi and his colleagues in April 2024. </p> <blockquote> <p>Shi, H., Tonboe, R., Lee, M., Dybkjær, G., Sohn, J., Singha, S., & Baordo, F. (2024). A Simple and Robust CryoSat-2 Radar Freeboard Correction Method Dedicated to TFMRA50 for the Arctic Winter Snow Depth and Sea Ice Thickness Retrieval. <em>Earth and Space Science</em>, <em>11</em>(10), e2024EA003715. https://doi.org/10.1029/2024EA003715</p> </blockquote> <p>Here, version 2 is uploaded, corresponding to the revised manuscript during the revision. The main changes compared to version 1 are:<br> 1) Update of the CryoSat-2 radar freeboard dataset (from v2p4 to v2p6)<br> 2) Update of the coefficients for the radar freeboard correction equations<br> 3) Extension of the retrieval period for the CS2IS2 method (April is now included)<br> 4) Removal of OIB data points used for the regression from the validation datasets<br> 5) Inclusion of the Fram Strait mooring dataset in the validation dataset</p> <p>It consists of three directories, each described below.</p> <h2><strong>01_retrieval_results</strong></h2> <p>This directory includes CryoSat-2-based monthly fields of Arctic sea ice thickness, snow depth, total freeboard, ice freeboards, ice draft, and bulk sea ice density for the winter months of the 2011-2022 period (January-March for alpha method and January-April for CS2IS2 method). Those variables are obtained using six combinations of two retrieval methods and three radar freeboard correction methods.</p> <p><em>Retrieval methods</em></p> <ul> <li>alpha method: A simultaneous retrieval method based on Shi et al. (2020) and Shi et al. (2023), combining CryoSat-2, AVHRR, and AMSR data</li> <li>CS2IS2 method: A simultaneous retrieval method based on Kwok and Marcus (2018) and Kwok et al. (2020), combining CryoSat-2 and ICESat-2 data</li> </ul> <p><em>Radar freeboard correction methods</em></p> <ul> <li>Wave speed correction method: Mallet et al. (2020)</li> <li>Empirical correction method: An empirical correction derived from the CS2_OIB_matchup data, using snow depth as a predictor</li> <li>Bias correction method: An empirical correction derived from the CS2_OIB_matchup data, doing bias correction</li> </ul> <p>The datasets used for generating this dataset are as follows:</p> <ul> <li>CryoSat-2 <br>- AWI CryoSat-2 sea ice thickness v2p6 (doi: <a href="https://doi.org/10.5281/zenodo.10044554" target="_blank" rel="noopener">10.5281/zenodo.10044554</a>)</li> <li>ICESat-2<br>- NSIDC ATL20 dataset (doi: <a href="https://doi.org/10.5067/ATLAS/ATL20.004" target="_blank" rel="noopener">10.5067/ATLAS/ATL20.004</a>)</li> <li>AVHRR<br>- Copernicus Marine Service's surface temperature datasets (doi: <a href="https://doi.org/10.48670/MOI-00130" target="_blank" rel="noopener">10.48670/MOI-00130</a>, doi: <a href="https://doi.org/10.48670/MOI-00123" target="_blank" rel="noopener">10.48670/MOI-00123</a>)</li> <li>AMSR<br>- JAXA AMSR-E 6.9 GHz brightness temperature (doi: <a href="https://doi.org/10.57746/EO.01gs73ayng11rpwk7n54aynyj1" target="_blank" rel="noopener">10.57746/EO.01gs73ayng11rpwk7n54aynyj1</a>)<br>- JAXA AMSR2 6.9 GHz brightness temperature (doi: <a href="https://doi.org/10.57746/EO.01gs73b1nzeh3g66jr4p04mr0j" target="_blank" rel="noopener">10.57746/EO.01gs73b1nzeh3g66jr4p04mr0j</a>)</li> <li>Auxiliary data<br>- Sea ice concentration: OSI SAF (doi: <a href="https://doi.org/10.15770/EUM_SAF_OSI_0013" target="_blank" rel="noopener">10.15770/EUM_SAF_OSI_0013</a>, doi: <a href="https://doi.org/10.15770/EUM_SAF_OSI_0014" target="_blank" rel="noopener">10.15770/EUM_SAF_OSI_0014</a>)<br>- Sea ice type: OSI SAF (doi: <a href="https://doi.org/10.15770/EUM_SAF_OSI_NRT_2006" target="_blank" rel="noopener">10.15770/EUM_SAF_OSI_NRT_2006</a>)</li> </ul> <p>The naming convention is 'RetrievalMethod_CorrectionMethod_yyyymm.bin'. The 'RetrievalMethod' is either 'alpha' or 'CS2IS2', and the 'CorrectionMethod' is either 'WaveSpeed,' 'Empirical,' or 'BiasCorrection.' The data format is a 32-bit floating point array in the shape of 6 x 448 x 304 (25 km polar stereographic grid). The first dimension indicates the variables (in the order of snow depth (0), sea ice thickness (1), ice freeboard (2), total freeboard (3), sea ice draft (4), and bulk sea ice density (5)). For example, to read the sea ice thickness of January 2020 based on the alpha method with an empirical correction, you may write this Python command:</p> <p><code>import numpy as np</code><br><code>data = np.fromfile('alpha_Empirical_202001.bin', dtype=np.float32).reshape(6,448,304)</code><br><code>hi = data[1,:,:]</code></p> <p>The unit of thickness-related variable is cm, and the unit of density is kg/m3. The 25 km polar stereographic grid information is available on the NSIDC website (doi: <a href="https://doi.org/10.5067/N6INPBT8Y104" target="_blank" rel="noopener">10.5067/N6INPBT8Y104</a>).</p> <h2><strong>02_valdiation data </strong></h2> <p>This directory includes reference data used for quality assessment of retrievals. There are three sub-directories:</p> <p>'Mooring_draft_psn25_monthly' includes sea ice draft measurements from the moorings in the Beaufort Sea (https://www2.whoi.edu/site/beaufortgyre/data/mooring-data/), Fram Strait (doi: <a href="https://doi.org/10.21334/npolar.2022.b94cb848" target="_blank" rel="noopener">10.21334/npolar.2022.b94cb848</a>), and the Laptev Sea (doi: <a href="https://doi.org/10.1594/PANGAEA.912927" target="_blank" rel="noopener">10.1594/PANGAEA.912927</a>, doi: <a href="https://doi.org/10.1594/PANGAEA.899275" target="_blank" rel="noopener">10.1594/PANGAEA.899275</a>).</p> <p>'OIB_SD_psn25_monthly' and 'OIB_TFB_psn25_monthly' include airborne snow depth and total freeboard measurements from NASA's Operation IceBridge campaign (doi: <a href="https://doi.org/10.5067/G519SHCKWQV6" target="_blank" rel="noopener">10.5067/G519SHCKWQV6</a>, doi: <a href="https://doi.org/10.5067/GRIXZ91DE0L9" target="_blank" rel="noopener">10.5067/GRIXZ91DE0L9</a>).</p> <p>Original data were processed to become monthly gridded data to make a comparison with satellite retrievals. The OIB data points used for the regression were excluded when processing the monthly gridded data. The naming convention of each file is 'Var_yyyymm.bin,' where 'Var' is the variable name (SD: snow depth, TFB: total freeboard, Di: ice draft). For example, you can use the following code to read the OIB snow depth in March 2014.</p> <p><code>import numpy as np</code><br><code>hs = np.fromfile('SD_201403.bin', dtype=np.float32).reshape(448,304)</code></p> <h2><strong>03_CS2_OIB_matchup</strong></h2> <p>This directory includes a match-up of AWI's CryoSat-2 L2P track data and OIB track data. The matching was done by resampling two high-resolution data on a coarser-resolution common grid (25 km polar stereographic grid) using a drop-in-a-bucket resampling method. The file format is CSV, and it is straightforward to understand when it is opened.</p> <h1><strong>[Abbreviations]</strong></h1> <p>AMSR: Advanced Microwave Scanning Radiometer<br>AVHRR: Advanced Very High Resolution Radiometer<br>AWI: Alfred Wegener Institute<br>CS2: CryoSat-2<br>JAXA: Japan Aerospace Exploration Agency<br>NASA: National Aeronautics and Space Administration<br>NSIDC: National Snow and Ice Data Center<br>OIB: Operation IceBridge<br>OSI SAF: Ocean and Sea Ice Satellite Application Facility</p> <p> </p>
Evaluation of Heracleum sosnowskyi Manden. survivial after snow cover removing in early spring
<p>The results of an experiment on the effect of snow cover removing on the areas occupied by Heracleum sosnowskyi stands in the early spring period. The experimental (impact) and control plots located in the Syktyvkar city suburb (Komi Republic, Russia).</p> <p>Most of calculation were performed in R. Find the file "FrozenHogweed_R_script.r" for calculation reproducing.</p>
The intermittency regions of powder snow avalanches [Data-set]
<p>This data repository contains the data-sets presented in the publication:</p> <p>Sovilla, B., McElwaine, J. N., & Köhler, A. (2018). The intermittency regions of powder snow avalanches. Journal of Geophysical Research: Earth Surface, 123, <a href="https://doi.org/10.1029/2018JF004678">https://doi.org/10.1029/2018JF004678</a>.</p> <p>This data set should be cited, together with the publication, as a:</p> <p>B. Sovilla, J. N. McElwaine, and A. Köhler (2018), The intermittency regions of powder snow avalanches [Data set]. Zenodo. <a href="https://doi.org/10.5281/">https://doi.org/10.5281/</a> zenodo.1415456.</p> <p>Information on the data can be found in the Readme file or can be obtained by writing an e-mail at: avalanche.data@slf.ch.</p>
Snow-Cloud Validation Masks for Multispectral Satellite Data.
<p>Geotiffs of manually validated snow, cloud, & clear-sky snow free pixels for 13 Landsat 8 images. These acquisitions are of mid-latitude mountainous regions that contain both snow and cloud cover. Four spectral libraries of snow and cloud are also provided. These are the snow and cloud spectra extracted from both these 13 scenes and the 13 L8 SPARCS Cloud Validation Masks that contained both snow and cloud. 1&2.) Snow and cloud top-of-atmosphere reflectance for the eight Landsat 8 OLI 30 meter optical bands, aggregated from the 26 scenes. 3&4.) The top-of-atmosphere reflectance for the eight Landsat 8 OLI 30 meter optical bands of all snow misidentified as cloud and cloud misidentified as snow by CFMASK, the cloud mask that ships in the BQA file of Landsat 8 Collection 1.</p>
Snow cover for Sierra Nevada
<p>The time series contains daily snow cover maps computed with the EURAC algorithm applied to MODIS Terra and Aqua images from 2002 onwards.</p>
Wet snow cover for Gran Paradiso
<p>Series of wet snow cover area maps derived from Sentinel-1 using the algorithm proposed in Nagler T, Rott H, Ripper E, Bippus G, Hetzenecker M. Advancements for snowmelt monitoring by means of Sentinel-1 SAR. Remote Sensing. 2016 Apr 20;8(4):348.</p>
Snow cover duration maps for Gran Paradiso
<p>Number of days for which a pixel is covered by snow. The snow cover duration (SCD) is generated from the 250 meter resolution snow cover maps realized with the algorithm described in Notarnicola, Claudia, et al. "Snow cover maps from MODIS images at 250 m resolution, Part 1: Algorithm description." Remote Sensing 5.1 (2013): 110-126.</p>
Snow cover duration maps for Bayerischer Wald
<p>Number of days for which a pixel is covered by snow. The snow cover duration (SCD) is generated from the 250 meter resolution snow cover maps realized with the algorithm described in Notarnicola, Claudia, et al. "Snow cover maps from MODIS images at 250 m resolution, Part 1: Algorithm description." Remote Sensing 5.1 (2013): 110-126.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.