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148 results for “Snow cover”
Projected Snow Cover Reductions and Mid-latitude Cyclone Responses in the North American Great Plains, 1986 - 2005
Extratropical cyclones are responsible for major weather events and trends in the mid-latitudes and preferentially develop in regions of enhanced cyclogenesis and proceed along climatological storm tracks. It has been shown that terrestrial snow cover exerts considerable influence on atmospheric baroclinicity which is largely responsible for the aforementioned cyclogeneses and storm tracks. Research about the effect which terrestrial snow cover exerts on cyclones' intensities, trajectories, and precipitation characteristics is limited but indicates a robust relationship with these factors. Many examinations of climate model projections have generally shown a poleward shift in storm tracks by the late 21st century though none have determined the degree to which the coincident poleward shift in snow extent is responsible. A method of imposing 10th, 50th, and 90th percentile values of snow retreat between the late 20th and 21st centuries as projected by 14 models of the Coupled Model Intercomparison Project Phase Five (CMIP5) is used to alter 20 historical cold season cyclones which tracked over or adjacent to the North American Great Plains. Simulations by the Advanced Research version of the Weather Research and Forecast Model (WRF-ARW) are initialized at 0 to 4 days prior to cyclogenesis. Including control and sensitivity testing wherein snow is unaltered or removed entirely, each cyclone case is simulated 25 times for a total of 500 simulations.
Snow cover profile data for Niwot Ridge and Green Lakes Valley, 1993 - ongoing.
Snow pits were excavated at various locations on Niwot Ridge and within the Green Lakes Valley. Temperature and snow density were measured at various depths throughout the snow cover profiles to characterize the temperature and snow water equivalent (SWE) of the snowpack throughout the year. Snow density was measured at 10-cm intervals using a 1000-ml cutter. Data on snow grain qualities were collected beginning in the 1994-95 snow season.
Seefeld Cold-Air Pool Experiment (SEECAP): WRF Simulation Output without snow cover January 16 2020 0000 UTC to January 17 2020 1200 UTC
<p>The Seefeld Cold-Air Pool Experiment (SEECAP) focused on the cross-country skiing area Olympiaregion Seefeld and in particular the topographic setting in the Nordic ski arena which favors the formation of cold-air pools and took place between December 2019 and March 2020. The measurement data are described in Rudolph (2022) and Rauchöcker et al. (2024d) and meteorological measurement data associated with SEECAP are published in Rauchöcker et al. (2024c). This upload contains WRF simulation output data for the night between January 16 and January 17 2020 without snow cover and the plotting routines to reproduce the figures in Rauchöcker et al. (2024d). The night between January 16 and January 17 2020 initially featured an ideal cold-air pool formation followed by a interuption by a wind disturbance around midnight. Simulation output for the same night, but with snow cover is also available (Rauchöcker et al., 2024a). The temperature evolution of the measurements agreed much better with the simulation with snow cover and otherwise the same model setting compared to the simulation without snow cover (Rauchöcker et al. 2024d). Also available in a different dataset is output from a simulation with snow cover for the night between January 12 and January 13 2020 (Rauchöcker et al., 2024b), which featured an undisturbed cold-air pool for almost the entire night. This case was considered to feature in Rauchöcker et al. (2024d), but a different case was chosen because some measurement data was not available during this period.</p> <h3><strong>WRF Simulation Output</strong></h3> <p>This Dataset includes data generated with WRFlux v1.4.1 (Göbel et al., 2022), a fork of the Weather Research and Forecasting model WRF (Skamarock et al. 2021). WRFlux allows to calculate the contribution of different processes to the potential temperature tendency at each grid point. The data published here is from the innermost simulation domain with 40m horizontal resolution and 10m vertical resolution close to the surface. The simulations were initialized at 00:00 UTC January 16 2020 and run until 12:00 UTC January 17 2020.</p> <p>Three different simulations were performed: two simulations with modified snow cover as described in Rauchöcker (2022), one each with the MYNN 2.5-order and the SMS-3DTKE PBL parameterizations (a scheme that blends a PBL scheme and a LES subgrid parameteriztion in the greyzone of turbulence), and one without snow cover with the MYNN 2.5-order PBL parameterization. Otherwise the simulations were identical. This dataset includes the simulation without snow cover. A detailed description of the model setup can be found in Rauchöcker et al (2024d) and in the file <em>namelist.input</em> that was used to generate the simulation results.</p> <p>Standard WRF output can be found in <em>wrfout_40m_jan16_nosnow</em>. The mean wind speed components, which were necessary to rotate the tendencies in a coordinate system that is aligned with the valley orientation, are contained in <em>windout_40m_jan16_nosnow</em>. These variables were contained in the unprocessed<em> </em>output files produced by WRFlux; the full files were unfortunately too large to be included here. The postprocessed tendencies are stored in <em>tend_40m_jan16_nosnow.nc</em>.</p>
AIRBORNE SPECTROMETER MEASUREMENTS FMOM BOREAL SNOW-COVERED LANDSCAPE
<p>The dataset contains 10 meter resolution reflectance data from boreal snow-covered landscape. The purpose of the airborne measurements was to investigate the effect of forest canopy on optical remote sensing signals for snow-covered surfaces. The hyperspectral airborne data was acquired with an AisaDUAL imaging spectrometer on March 18 and on March 21, 2010 in Sodankylä, Finland. The image swath was 240 meters and flight lines were several kilometers long. The original spatial resolution of the data is 80 cm x 80 cm, but it was resampled to pixel size of 10 m x 10 m. All measurements were carried out in non-cloudy conditions (0/8 to 2/8 cloud cover). On 18 March, the tree canopy was snow-free and snow on the ground was several days old, while on 21 March, the tree canopy was snow-covered and snow on ground was fresh. The data contains mosaics of the flight lines for the bands 555 nm, 645 nm, 858.5 nm and 1640 nm for both days 18 March 2010 and 21 March 2010.</p>
Global MODIS-based snow cover monthly long-term (2000-2012) at 500 m, and aggregated monthly values (2000-2020) at 1 km
<p>The Global monthly snow cover repository contains multiple products (based on the MODIS/Terra MOD10A2):</p> <ol> <li>Global snow cover monthly long-term (2000–2012) P90 and standard deviation derived from the <a href="http://maps.elie.ucl.ac.be/CCI/viewer/index.php">ESA CCI snow cover weekly product</a>;</li> <li>Global snow cover monthly values P05, P50 and P95 for the period 2000–2020 derived using <a href="https://climate.esa.int/en/odp/#/project/snow">ESA snow cover fraction daily 1-km values</a>;</li> <li>Min and max geometric temperatures for the mid-month (dtm_temp.max_geom.*_m_1km_s0..0cm_xxxx_epsg4326_v1.tif);</li> </ol> <p>Quantiles (probability either 0.05, 0.5, 0.9 and/or 0.95) have been derived by matching dates in the filenames (daily or weekly values). After deriving quantiles, gaps were filled using temporal neighbors (e.g. missing values for year 2002 were filled using average of values between year 2001 and 2003). The gaps were especially large for months of November, December, January and February, northern Hemisphere. Important note: maps still contain some artifacts due to high reflections of white-sands e.g. Salar de Uyuni desert in Bolivia and similar. Processing steps are available <a href="https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/snow.cover"><strong>here</strong></a>. Antarctica is not included.</p> <p>To access and visualize global datasets use: <a href="https://openlandmap.org"><strong>https://openlandmap.org</strong></a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> </ul> <p>All files provided as Cloud-Optimized GeoTIFFs / internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>clm = theme: climate,</li> <li>snow.cover = variable: snow cover fractions,</li> <li>esa.modis = data source ESA snow product,</li> <li>p.90 = upper 90% quantile,</li> <li>1km = spatial resolution / block support: 1 km,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2000..2012 = time reference aggregated: from 2000 to 2012,</li> <li>v1 = version number: 1,</li> </ul>
Data and code for the manuscript "From white to green: Snow cover loss and increased vegetation productivity in the European Alps"
<p>Data and code used for the manuscript "From white to green: Snow cover loss and increased vegetation productivity in the European Alps" by Rumpf et al., submitted December 2021 to Science</p> <p>See file ReadMe.txt for a description of the content and the original publication for further explanations.</p> <p>You are free to use these data and code for scientific purposes but are obliged to cite the above-mentioned publication.<br> For further questions, contact sabine.rumpf@unibas.ch</p>
Seefeld Cold-Air Pool Experiment (SEECAP): WRF Simulation Output with snow cover January 12 2020 0000 UTC to January 13 2020 1200 UTC
<p>The Seefeld Cold-Air Pool Experiment (SEECAP) focused on the cross-country skiing area Olympiaregion Seefeld and in particular the topographic setting in the Nordic ski arena which favors the formation of cold-air pools and took place between December 2019 and March 2020. The measurement data are described in Rudolph (2022) and Rauchöcker et al. (2024d) and meteorological measurement data associated with SEECAP are published in Rauchöcker et al. (2024c). This upload contains WRF simulation output data for the night between January 12 and January 13 2020 with snow cover. The night between January 12 and January 13 2020 featured an undisturbed cold-air pool for almost the entire night. This case was considered to feature in Rauchöcker et al. (2024d), but a different case was chosen because some measurement data was not available during this period. Also available in a different dataset are data from simulations of the night between January 16 and January 17 2020, which initially featured ideal condition for cold-air pool formation followed by a disturbance around midnight, with snow cover (Rauchöcker et al., 2024a) and also without snow cover (Rauchöcker et al., 2024b).</p> <h3><strong>WRF Simulation Output</strong></h3> <h3><strong> </strong></h3> <p>This Dataset includes data generated with WRFlux v1.4.1 (Göbel et al., 2022), a fork of the Weather Research and Forecasting model WRF (Skamarock et al. 2021). WRFlux allows to calculate the contribution of different processes to the potential temperature tendency at each grid point. The data published here is from the innermost simulation domain with 40m horizontal resolution and 10m vertical resolution close to the surface. The simulations were initialized at 00:00 UTC January 12 2020 and run until 12:00 UTC January 13 2020 and results for the same night but a coarser grid spacing are described in Rauchöcker (2022). Compared to the simulation with 200m grid spacing presented there, this simulation offers a significantly improved resolution. As input, we used ERA5 reanalysis data, 1-arc second SRTM terrain data and Corine 2018 land cover classification. The simulation was performed with modified snow cover as described in Rauchöcker (2022) and the MYNN 2.5-order PBL parameterization. A detailed description of the model setup can be found in Rauchöcker et al (2024d) and in the file <em>namelist.input</em> that was used to generate the simulation results.</p> <p>Standard WRF output can be found in <em>wrfout_40m_jan12</em>. The mean wind speed components, which were necessary to rotate the tendencies in a coordinate system that is aligned with the valley orientation, are contained in <em>windout_40m_jan12</em>. These variables were contained in the unprocessed output files produced by WRFlux; the full files were unfortunately too large to be included here. The postprocessed tendencies are stored in <em>tend_40m_jan12</em>.</p>
CESM2 Idealized Experiment Output: Summer atmospheric response to zero May North American snow cover
<p>The National Center for Atmospheric Research’s Community Earth System Model version 2.2 (CESM2) (Danabasoglu et al., 2020) was run in the Atmospheric Model Intercomparison Project (AMIP) configuration. SSTs and sea-ice were prescribed as monthly varying seasonal cycles based on the observed climatology from 2005 to 2015 (i.e., component set: F2010climo) (Hurrell et al., 2008). We employed the Community Atmosphere Model version 6 (CAM6) (Bogenschutz et al., 2018)<span> </span>as the atmospheric component and the Community Land Model version 5 (CLM5) (Lawrence et al., 2019) as the land-surface component. Each model was run with a horizontal resolution of 0.9˚ latitude by 1.25˚ longitude.</p> <p>We ran a control simulation in this configuration for ten consecutive years. We then modified the land-surface restart files for May 1st of each year by reducing the snow cover over North America to zero. Using these modified files, we then completed a reduced snow simulation by rerunning three-month simulations from May through July for each of the ten years. </p>
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>
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>
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>
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>
Seefeld Cold-Air Pool Experiment (SEECAP): WRF Simulation Output with snow cover January 16 2020 0000 UTC to January 17 2020 1200 UTC
<p>The Seefeld Cold-Air Pool Experiment (SEECAP) focused on the cross-country skiing area Olympiaregion Seefeld and in particular the topographic setting in the Nordic ski arena which favors the formation of cold-air pools and took place between December 2019 and March 2020. The measurement data are described in Rudolph (2022) and Rauchöcker et al. (2024d) and meteorological measurement data associated with SEECAP are published in Rauchöcker et al. (2024c). This upload contains WRF simulation output data for the night between January 16 and January 17 2020 with snow cover and the plotting routines to reproduce the figures in Rauchöcker et al. (2024d). The night between January 16 and January 17 2020 initially featured ideal condition for cold-air pool formation followed by a disturbance around midnight. There is also an upload with simulation output for the same night, but without snow cover (Rauchöcker et al., 2024a). Also available in a different dataset are data from a simulation with snow cover for the night between January 12 and January 13 2020 (Rauchöcker et al., 2024b), which featured an undisturbed cold-air pool for almost the entire night. This case was considered to feature in Rauchöcker et al. (2024d), but a different case was chosen because some measurement data was not available during this period.</p> <h3><strong>WRF Simulation Output</strong></h3> <p>This Dataset includes data generated with WRFlux v1.4.1 (Göbel et al., 2022), a fork of the Weather Research and Forecasting model WRF (Skamarock et al. 2021). WRFlux allows to calculate the contribution of different processes to the potential temperature tendency at each grid point. The data published here is from the innermost simulation domain with 40m horizontal resolution and 10m vertical resolution close to the surface. The simulations were initialized at 00:00 UTC January 16 2020 and run until 12:00 UTC January 17 2020.</p> <p>Three different simulations were performed: two simulations with modified snow cover as described in Rauchöcker (2022), one each with the MYNN 2.5-order and the SMS-3DTKE PBL parameterizations (a scheme that blends a PBL scheme and a LES subgrid parameteriztion in the greyzone of turbulence), and one without snow cover with the MYNN 2.5-order PBL parameterization. Otherwise the simulations were identical. This dataset includes the two simulation with snow cover, where <em>jan126_sms.zip</em> contains the files relating to the simulations with the SMS-3DTKE scheme and <em>jan16.zip</em> those for the simulation with the MYNN 2.5-order PBL parameterization. A detailed description of the model setup can be found in Rauchöcker et al (2024d) and in the files <em>namelist.input</em> and <em>namelist_sms.input</em> that were used for the simulations. </p> <p>Standard WRF output can be found in <em>wrfout_40m_jan16</em> and <em>wrfout_40m_jan16_sms</em>. The mean wind speed components, which were necessary to rotate the tendencies in a coordinate system that is aligned with the valley orientation, are contained in <em>windout_40m_jan16</em> and <em>windout_40m_jan16_sms</em>. These variables were contained in the unprocessed output files produced by WRFlux; the full files were unfortunately too large to be included here. The postprocessed tendencies are stored in <em>tend_40m_jan16.nc</em> and <em>tend_40m_jan16_sms.nc</em>.</p> <h3><strong>Plotting routines</strong></h3> <p>Python scripts and environment files to reproduce most figures in Rauchoecker et al. (2024d) are included in <em>code.zip</em>. To reproduce plots involving measurement data, which is available in Rauchöcker et al. (2024c), is also needed.</p> <p>Due to conflicts between some packages, two different environment were needed. To reproduce Figure 2, install the <em>orthoplot</em> environment by running "<em>conda env create orthoplot.yml</em>" in a terminal window, activate it ("<em>conda activate orthoplot</em>") and then run <em>ortho_plot.py</em>. All other plots require the wrfstuff environent (installed by running "<em>conda env create wrfstuff.yml</em>" and activated by "<em>conda activate wrfstuff</em>") and are produced by <em>paper_plots.py</em>. Functions used to load data and plot the figures are included in <em>dataload.py</em> and <em>plotting_routines.py</em>, respectively<em>.</em></p> <p>Two variables decide which figures are plotted for which dataset: <em>dataname</em> and <em>doplot</em>. The variable <em>dataname</em> defines the path to the <em>dataset</em> that should be used to produce the figures, while the value <em>doplot</em> defines which figure to reproduce. By setting doplot="fig1", Figure 1 is reproduced, while doplot="fig7" and doplot="fig9" reproduce Figures 7 and 9, respectively. For all other values for <em>doplot</em>, Figures 4, 5, 6, 8 and 11 are reproduced. We decided not to include a script to plot Figure 3 because the data the climatology is based on is owned by GeoSphere Austria - the agency operating the permanent weather station. Further, no script for reproducing Figure 10 is included because it was not created within the framework of Python.</p> <h3><strong>Geofiles</strong></h3> <p>The files included in <em>g</em><em>eofiles.zip</em> are needed to plot Figure 1 and Figure 2, although not of particular interest on their own. Included are output files from <em>geogrid.exe</em>, whicih are necessary to plot the domains overview (Figure 1), as well as an orthophoto (<em>orthophoto.tif</em>) and high-resolution digital elevation model (<em>topo_hr.tif</em>) which are both based on data from Land Tirol.</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>
MODIS Snow-Cover Frequency Maps
<p>The 365 global snow cover frequency maps were derived from MODIS MOD10C1 data. There is one map for each day of the year (leap year days are excluded). Each map displays the frequency of snow cover for the 20-year time series, Hydrological Years 2000 - 2020, on a per-grid cell basis. Each grid cell is approximately 5 X 5 km. The dataset is described in detail in Riggs et al. (in press).</p>
Daily MODIS snow cover maps for the European Alps from 2002 onwards at 250m horizontal resolution along with a nearly cloud-free version
<p><strong>NOTE: We discovered some errors in the data for images after February 2019. They will be fixed in version >= 1.1.x, until then, usage of the data after Feb 2019 is not advised. The rest of the data is fine.</strong></p> <p> </p> <p>This is the data to the same-titled Data paper, which can be found at <a href="https://doi.org/10.3390/data5010001">https://doi.org/10.3390/data5010001</a>.</p> <p>Along with auxilary files for the <a href="https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal">cloudremoval package</a>, and example scripts on how to access chunks of the data.</p> <p>The files contain:</p> <ol> <li><strong>python-cloudremoval-aux-data.tar.gz</strong> : auxilary data (altitude, aspect, ...) to run the cloudremoval module which can be found at <a href="https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal">https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal</a></li> <li><strong>python-example-data-access.html </strong>: Example script how to access parts of the data using python</li> <li><strong>R-example-data-access.html</strong> : Example script how to access parts of the data using R</li> <li><strong>zenodo_01_original.tar.gz</strong> : time series of snow cover maps, developed at the Institute for Earth Observation, Eurac Research, Bolzano, Italy. More information in same-title Data paper (<a href="https://doi.org/10.3390/data5010001">https://doi.org/10.3390/data5010001</a>), and for algorithm at <a href="https://doi.org/10.3390/rs5010110">https://doi.org/10.3390/rs5010110</a>.</li> <li><strong>zenodo_02_cloudremoval.tar.gz</strong> : time series of cloud filtered maps, based on 2. above, using code mentioned in 1. More information in same-titled Data paper.</li> </ol> <p> </p> <p>The maps are GeoTIFF with integer based values:</p> <p>0 = no data; 1 = snow; 2 = land; 3 = cloud; 4&5 = water bodies / nodata</p> <p> </p> <p>Version history:</p> <p>1.0.0 : initial upload<br> 1.0.1 : changes after revision of Data paper<br> 1.0.2 : added example scripts</p> <p> </p> <p> </p>
Snow depth and land surface cover in Tuolumne basin (California) from Pléiades images
<p>This dataset contains products calculated from Pléiades images.</p> <p>Details about the products are available in https://doi.org/10.5194/tc-2020-15.</p> <p>These products were used in Figure 4.</p> <p>- pleiades_elevation_difference_raw_winter_minus_summer.tif : raw difference of digital elevation models (DEMs) calculated from Pléiades stereo images.</p> <p>- pleiades_snow_depth_winter.tif : difference of DEMs on snow terrain only (where pleiades_land_surface_cover_winter.tif==1 with morphological erosion)</p> <p>- pleiades_land_surface_cover_winter.tif : land cover surface in the winter images (1= snow, 2=forest, 3= stable terrain, 4=water)</p> <p>- pleiades_land_surface_cover_summer.tif : land cover surface in the summer images (1= snow, 2=forest, 3= stable terrain, 4=water) </p> <p>- elevation_difference_style.qml : qgis style used for elevation difference and snow depth.</p> <p>- land_surface_cover_style.qml : qgis style used for land cover surface.</p> <p> </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.