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148 results for “Snow cover”
Air temperature measurements using autonomous self-recording dataloggers in mountainous and snow covered areas
<p>Data and sctripts employed on submitted article on Water Resources Research (AGU Journal)</p>
Improved cross-scale snow cover simulations by developing a scale-aware parameterization in the Noah-MP land surface model
<p>Noah-MP data used to support the analyses conducted by Abolafia-Rosenzweig et al.: <strong>Improved </strong><strong>cross-scale </strong><strong>snow cover simulations by developing a scale-aware parameterization in the Noah-MP land surface model</strong></p>
Random Forest fused MODIS and Landsat snow cover from spectral mixture analysis in the Sierra Nevada, USA
<p>This data is snow cover fraction from the Snow Covered Area and Grain Size (SCAG) model for Landsat OLI and Terra MODIS and well as a 2-stage random forest model to fuse the 2 datasets for improved temporal/spatial resolution. There are 170 scenes in 2001 to 2012. It was used in the a publication for Remote Sensing of the Environment titled: Multi-sensor fusion using random forests for daily fractional snow cover at 30 m, doi: to be assigned.</p> <p><strong>Inputs</strong>: [YYYYMMDD is year month day of month, $num is 5 or 7 for Landsat platform, $sens is sensor TM or ETM+]</p> <p>Landsat.zip:</p> <p>Snow cover from Landsat: SSN.p042r034_YYYYMMDD.Landsat$num-$sens.canopyadjusted_mask.v01.tif </p> <p> </p> <p>MODIS.zip:</p> <p>Snow cover from MODIS: SSN.SN_W$YYYYMMDD_$YYYYMMDD.Terra-MODIS.snow_cover_percent.v01.tif</p> <p> </p> <p>Predictors.zip<strong> </strong></p> <p>Static predictors (see RSE publication Table 2): SouthernSierraNevada*.tif [* here is the variable name]</p> <p><strong>Outputs [</strong> [YYYYMMDD is year month day of month]</p> <p>ProbabilityNot0Not100.zip</p> <p>SSN.prob.btwn.YYYYMMDD.v3.tif - from classification random forest, probability of being between 0 and 100</p> <p> </p> <p>Probability100fSCA</p> <p>SSN.pro.hundred.YYYYMMDD.v3.tif - from classification random forest, probability of being 100</p> <p> </p> <p>RegressionResult.zip</p> <p>SSN.regression.YYYYMMDD.v3.tif - from prediction random forest</p> <p> </p> <p>Final_Downscaled.zip</p> <p>SSN.downscaled.YYYYMMDD.v3.3e+05.tif - final product (combination of classification and prediction)</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>
Evaluation metrics for eight types of gap-filled snow cover products in China and four schemes of combining multiple products
<p>This dataset contains the station-based evaluation metrics in terms of CK, R of SCD, and CWR values of SSD and SED for all the eight types of gap-filled products and the proposed schemes for combining multiple products. "TP" means the Tibetan Plateau.</p>
Evaluation metrics for eight types of gap-filled snow cover products in China and four schemes of combining multiple products
<p>This dataset contains the station-based evaluation metrics in terms of CK, R of SCD, and CWR values of SSD and SED for all the eight types of gap-filled products and the proposed schemes for combining multiple products. "TP" means the Tibetan Plateau.</p>
Data from: Two decades of altered snow cover does not affect soil microbial ability to catabolize carbon compounds in an oceanic alpine heath
Open the record for dataset details and reuse information.
Long-term deepened snow cover alters litter layer turnover rate in temperate steppes
Open the record for dataset details and reuse information.
Data from: Short-term climate change manipulation effects do not scale up to long-term legacies: effects of an absent snow cover on boreal forest plants
Open the record for dataset details and reuse information.
Spatial variation in early-winter snow cover determines local dynamics in a network of alpine butterfly populations
Open the record for dataset details and reuse information.
Photobiological Effects on Ice Algae of a Rapid Whole-Fjord Loss of Snow Cover during Spring Growth in Kangerlussuaq, a West Greenland Fjord
<p>A full data set related to the publication entitled "Photobiological Effects on Ice Algae of a Rapid Whole-Fjord Loss of Snow Cover during Spring Growth in Kangerlussuaq, a West Greenland Fjord" published in JMSE - Journal of Marine Science and Engineering located at <a href="https://www.mdpi.com/2077-1312/9/8/814">JMSE | Free Full-Text | Photobiological Effects on Ice Algae of a Rapid Whole-Fjord Loss of Snow Cover during Spring Growth in Kangerlussuaq, a West Greenland Fjord (mdpi.com)</a></p> <p> </p>
Photobiological Effects on Ice Algae of a Rapid Whole-Fjord Loss of Snow Cover during Spring Growth in Kangerlussuaq, a West Greenland Fjord
<p>A data set related to the publication entitled "Photobiological Effects on Ice Algae of a Rapid Whole-Fjord<br> Loss of Snow Cover during Spring Growth in Kangerlussuaq,<br> a West Greenland Fjord" published in JMSE.</p>
Fig. 1 in The Distribution of Soil Testate Amoebae under Winter Snow Cover at the Plot-scale Level in Arctic Tundra (Qeqertarsuaq/Disko Island, West Greenland)
Fig. 1. The map of soil sampling (57 samples) for the study on distribution of soil testate amoebae within an 8 × 15 m plot located in an arctic tundra on Qeqertarsuaq/Disko Island (West Greenland).
Fig. 3 in The Distribution of Soil Testate Amoebae under Winter Snow Cover at the Plot-scale Level in Arctic Tundra (Qeqertarsuaq/Disko Island, West Greenland)
Fig. 3. Maps of the univariate characteristics of the soil testate amoeba assemblage (A – log (total concentration, × 103 empty shells g–1 of e dry soil weight); B – species number (taxa sample–1); C – Shannon-Wiener's diversity index; D – Pielou's evenness index) plotted against their spatial coordinates at 57 sampling locations within an 8 × 15 m plot in an arctic tundra in Qeqertarsuaq/Disko Island (West Greenland). All data are centred on 0, so square sizes are proportional to the deviations from the mean values at the plot. Open symbols are used for negative values and the filled symbols are used for positive values. Spatial patterns are visualised as aggregations of similar size and colour.
ISLSCP II Northern Hemisphere Monthly Snow Cover Extent
This ISLSCP data set is derived from the National Snow and Ice Data Center (NSIDC) Northern Hemisphere EASE-Grid Weekly Snow Cover and Sea Ice Extent product which combines snow cover and sea ice extent at weekly intervals for October 1978 through June 2001, and snow cover alone from 1966 through June 2001. The original data set was the first representation of combined snow and sea ice measurements derived from satellite observations for the period of record. Designed to facilitate study of Northern Hemisphere seasonal fluctuations of snow cover and sea ice extent, the original NSIDC data set also includes monthly climatologies describing average extent, probability of occurrence, and variance.This data set shows the extent of snow on the land at a variety of scales (1.0 degree, 0.5 degree, 0.25 degree). The values represent the percentage of days in each month where snow was present -- 100 means 100% of the month, 80 means 80% of the month, etc. There are 4 .zip files provided. Missing data is represented by -99 for water and -88 for land. The data were originally in a yearly tabular format. The file was converted to multi-scale maps by plotting each point in the tabular data onto a map of -99 (water) and -88 (land) created from the standard ISLSCP II Land/Sea Mask.
CLPX-Satellite: EO-1 Hyperion Surface Reflectance, Snow-Covered Area, and Grain Size, Version 1
This data set consists of apparent surface reflectance, subpixel snow-covered area, and grain size collected from the Hyperion hyperspectral imager. The Hyperion imager has a spectral range of 400-2500 nm, a spectral resolution of 10 nm, spatial resolution of 30 m, and a swath width of 7.8 km. Sampling is scene based (256 samples, 512 lines).
ABoVE: High Resolution Cloud-Free Snow Cover Extent and Snow Depth, Alaska, 2001-2017
This dataset provides estimates of maximum snow cover extent (SCE) and snow depth for each 8-day composite period from 2001 to 2017 at 1 km resolution across Alaska. The study area covers the majority land area of Alaska except for areas covered by perennial ice/snow or open water. A downscaling scheme was used in which Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) global reanalysis 0.5 degree snow depth data were interpolated to a finer 1 km spatial grid. In the methods used, the downscaling scheme incorporated MODIS SCE (MOD10A2) to better account for the influence of local topography on the 1km snow distribution patterns. For MODIS cloud-contaminated pixels, persistent and patchy cloud cover conditions were improved by applying an elevation-based spatial filtering algorithm to predict snow occurrence. Cloud-free MODIS SCE data were then used to downscale MERRA-2 snow depth data. For each snow-covered 1 km pixel indicated by the MODIS data, the snow depth was estimated based on the snow depth of the neighboring MERRA-2 0.5 grid cell, with weights predicted using a spatial filter.
Global EASE-Grid 8-day Blended SSM/I and MODIS Snow Cover, Version 1
This data set comprises global, 8-day Snow-Covered Area (SCA) and Snow Water Equivalent (SWE) data from 2000 through 2008. Global SWE data are derived from the Special Sensor Microwave Imager (SSM/I) and are enhanced with MODIS/Terra Snow Cover 8-Day Level 3 Global 0.05 degree Climate Modeling Grid (CMG) data. Global data are gridded to the Northern and Southern 25 km Equal-Area Scalable Earth Grids (EASE-Grids). These data are suitable for continental-scale time-series studies of snow cover and snow water equivalent.
Northern Hemisphere EASE-Grid 2.0 Weekly Snow Cover and Sea Ice Extent, Version 4
The main parameters for this data set are snow cover and sea ice extent; both parameters are derived from SMMR and DMSP-F8, -F11, -F13, and -F17 SSM/I and SSMIS brightness temperature data. These data are provided on the Northern Hemisphere EASE-Grid 2.0 projection and at a grid cell size of 25 x 25 km. This product is designed to provide a consistent weekly time series of snow cover from October 1966 through January 2023 and sea ice from October 1978 through January 2023.
CLPX-Satellite: MODIS Radiances, Reflectances, Snow Cover and Related Grids, Version 1
This data set provides Moderate Resolution Imaging Spectroradiometer (MODIS) data as part of the Cold Land Processes Field Experiment (CLPX). Parameters include radiances, surface reflectance, snow cover, land surface temperature/emissivity, and vegetation indices.
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International Brain Laboratory public data
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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.