Find research datasets worth reusing

opencc-by-4.0Apr 2024View details →

Satellite-derived monthly Arctic winter sea ice thickness, snow depth, freeboards, ice draft, and bulk ice density (2011-2022) and validation datasets

<h1>[Description]</h1> This dataset is curated for a manuscript published in Earth and Space Science by Hoyeon Shi and his colleagues in April 2024.  <blockquote> 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. Earth and Space Science, 11(10), e2024EA003715. https://doi.org/10.1029/2024EA003715 </blockquote> Here, version 2 is uploaded, corresponding to the revised manuscript during the revision. The main changes compared to version 1 are:     1) Update of the CryoSat-2 radar freeboard dataset (from v2p4 to v2p6)     2) Update of the coefficients for the radar freeboard correction equations     3) Extension of the retrieval period for the CS2IS2 method (April is now included)     4) Removal of OIB data points used for the regression from the validation datasets     5) Inclusion of the Fram Strait mooring dataset in the validation dataset It consists of three directories, each described below. <h2>01_retrieval_results</h2> 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. Retrieval methods <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> Radar freeboard correction methods <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> The datasets used for generating this dataset are as follows: <ul> <li>CryoSat-2  - 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 - 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 - 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 - 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>) - 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 - 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>) - 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> 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: <code>import numpy as np</code> <code>data = np.fromfile('alpha_Empirical_202001.bin', dtype=np.float32).reshape(6,448,304)</code> <code>hi = data[1,:,:]</code> 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>). <h2>02_valdiation data </h2> This directory includes reference data used for quality assessment of retrievals. There are three sub-directories: '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>). '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>). 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. <code>import numpy as np</code> <code>hs = np.fromfile('SD_201403.bin', dtype=np.float32).reshape(448,304)</code> <h2>03_CS2_OIB_matchup</h2> 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. <h1>[Abbreviations]</h1> AMSR: Advanced Microwave Scanning Radiometer AVHRR: Advanced Very High Resolution Radiometer AWI: Alfred Wegener Institute CS2: CryoSat-2 JAXA: Japan Aerospace Exploration Agency NASA: National Aeronautics and Space Administration NSIDC: National Snow and Ice Data Center OIB: Operation IceBridge OSI SAF: Ocean and Sea Ice Satellite Application Facility

opencc-by-4.0Sep 2024View details →

Manual in-situ measurements of snow depth and snow water equivalent at the Polish Polar Station Hornsund - winter seasons 2021/2022and 2022/2023

The dataset presents manual measurements of snow depth and snow water equivalent collected at the Polish Polar Station Hornsund in Svalbard during the winter seasons of 2021/2022 and 2022/2023. Snow depth measurements have been conducted at the same location by the Station's overwintering personnel since August 1982. Snow depth is calculated from a mean of three snow stakes to avoid the effects of the drifting snow. Measurements are taken manualy, on a daily basis.  Snow water equivalent measurements have also been carried out at the same points by the Station's overwintering crew since October 1982. These measurements are performed every five days using a VS-43 snow tube. However, measurements are not taken when the snow depth is less than 5 cm.

opencc-by-4.0Aug 2024View details →

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"

This datasset contains data to reproduce the following figures of the paper Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site: <ul> <li> Time series data of Figures 1c and 2 </li> <li> Data (*.asc) used for plotting Figures 1d and 1e (as well as Figure S3 and S4) </li> <li>Pléiades snow depth map (Figure S1)</li> <li> Data used for plotting Figure S2 </li> </ul>

opencc-by-4.0Sep 2024View details →

University of Tromso Arctic Ocean freeboard and snow depth product from CryoSat-2, AltiKa and ICESat-2

Dual-frequency snow depth estimates for the Arctic Ocean in Oct-Apr 2018-2023 derived from gridded 25-km resolution CryoSat-2 and SARAL AltiKa radar freeboards and ICESat-2 laser freeboards. Waveform modelling approach applied to radar altimeters, ICESat-2 laser altimetry freeboards from ATL20 r004. See acompanying publication in The Cryosphere for further details.

opencc-by-4.0Oct 2020View details →

Snow cover in the European Alps: Station observations of snow depth and depth of snowfall

Auxiliary files, code, and data for paper published in The Cryosphere: Observed snow depth trends in the European Alps 1971 to 2019  <a href="https://doi.org/10.5194/tc-15-1343-2021">https://doi.org/10.5194/tc-15-1343-2021</a>   Auxiliary files: <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>   Code (working copy, not cleaned, all written in R statistical software): code.zip <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>   Data: <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 use the data you agree to adhere to the respective data provider's terms 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: 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. [from <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a>] </li> </ul>     Version history: v1.3: added maxHS and SCD (with various 1-100cm thresholds) to monthly data v1.2: uploaded data v1.1: changes to aux-paper.zip and code.zip as consequence from submitting a revised manuscript v1.0: initial upload

opencc-by-4.0Dec 2022View details →

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

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

openOpenMar 2007View details →

Air Temperature, Soil Temperature, Precipitation, Snow Depth at Long Term Tree Growth Sites; 1968-Present : Weekly

Part of the Long Term Tree Growth study. This dataset is an accumulation of various manual measurements made on a weekly to monthly basis. It originally included snow stakes, rain buckets, max/min thermometers and a series of soil temperature sensors. Over the years equipment has changed. The soil temperature sensors exceeded their field life during the 1990's and were dropped from the study. In 2001 logging air temperature and relative humidity sensors were installed and those measurements were discontinued. In 2002 logging rain gauges were installed to replace the manual buckets. Both styles were during that growing season and the manual buckets were removed before the 2003 field season. All that remains active in this dataset are the snow stake measurements.

openCC (other)Oct 2021View details →

Hubbard Brook Experimental Forest: Soil Freezing Study (SFS) In Situ Measurements of Snow and Soil Frost Depth

Climate models for the northeastern United States (U.S.) over the next century predict an increase in air temperature between 2.8 and 4.3 °C and a decrease in the average number of days per year when a snowpack will cover the forest floor (Hayhoe et al. 2007, 2008; Campbell et al. 2010). Studies of forest dynamics in seasonally snow-covered ecosystems have been primarily conducted during the growing season, when most biological activity occurs. However, in recent years considerable progress has been made in our understanding of how winter climate change influences dynamics in these forests. The snowpack insulates soil from below-freezing air temperatures, which facilitates a significant amount of microbial activity. However, a smaller snowpack and increased depth and duration of soil frost amplify losses of dissolved organic C and NO3- in leachate, as well as N2O released into the atmosphere. The increase in nutrient loss following increased soil frost cannot be explained by changes in microbial activity alone. More likely, it is caused by a decrease in plant nutrient uptake following increases in soil frost. We conducted a snow-removal experiment at Hubbard Brook Experimental Forest to determine the effects of a smaller winter snowpack and greater depth and duration of soil frost on trees, soil microbes, and arthropods. A number of publications have been based on these data: Comerford et al. 2013, Reinmann et al. 2019, Templer 2012, and Templer et al. 2012. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. Campbell JL, Ollinger SV, Flerchinger GN, Wicklein H, Hayhoe K, Bailey AS. Past and projected future changes in snowpack and soil frost at the Hubbard Brook Experimental Forest, New Hampshire, USA. Hydrological Processes. 2010; 24:2465–2480. Comerford DP, PG Schaberg, PH Te

openCC (other)Jun 2025View details →

Climate Change Across Seasons Experiment (CCASE) at the Hubbard Brook Experimental Forest: Soil Temperature, Soil Frost, and Snow Depth Data in support of "Declining Winter Snowpack Offsets Carbon Storage Enhancement from Growing Season Warming in Northern Temperate Forest Ecosystems", Conrad-Rooney et al. PNAS 2025

Data associated with the publication: Conrad-Rooney E, AB Reinmann, PH Templer. Declining Winter Snowpack Offsets Carbon Storage Enhancement from Growing Season Warming in Northern Temperate Forest Ecosystems. Proceedings of the National Academy of Sciences, 2025. This dataset includes soil temperature (winter 2021-2022) and snow depth and frost depth (winter 2022-2023) at the Climate Change Across Seasons Experiment. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.

openCC (other)Mar 2025View details →

Glacier snow depth measurements, McMurdo Dry Valleys, Antarctica (1993-2023, ongoing)

As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor glacial mass balance and meltwater flow. This data package includes snow depth measurements to the surface of six glaciers (Canada, Commonwealth, Hughes, Suess, Howard, and Taylor) in Taylor Valley and one glacier (Adams) in Miers Valley, all of which are located in the McMurdo Dry Valleys of Antarctica. Most measurements began during the 93-94 field season. Adams measurements were established during the 14-15 field season. Measurements are ongoing except at Hughes and Suess Glaciers where monitoring ceased following the 08-09 field season. Monitoring the changes in these measurements over time provides a record of mass balance, and aids in determining the role of glaciers in the polar hydrologic cycle.

openCC0Sep 2022View details →

Lake snow removal experiment snow, ice, and Secchi depth, 2019-2021

Although it is a historically understudied season, winter is now recognized as a time of biological activity and relevant to the annual cycle of north-temperate lakes. Emerging research points to a future of reduced ice cover duration and changing snow conditions that will impact aquatic ecosystems. The aim of the study was to explore how altered snow and ice conditions, and subsequent changes to under-ice light environment, might impact ecosystem dynamics in a north, temperate bog lake in northern Wisconsin, USA. This dataset resulted from a snow removal experiment that spanned the periods of ice cover on South Sparkling Bog during the winters of 2019, 2020, and 2021. During the winters 2020 and 2021, snow was removed from the surface of South Sparkling Bog using an ARGO ATV with a snow plow attached. The 2019 season served as a reference year, and snow was not removed from the lake. This dataset represents the snow depths, black and white ice thickness, and Secchi depths during the period of ice cover each winter.

openCC (other)Oct 2019View details →

Snow depth sensor measurement data for Alpine site, 2010 - 2015

Effects of infrared heaters on snow accumulation, snowmelt, and snow–atmosphere energy exchange were examined at Niwot Ridge, Colorado (CO). These .zip data files contains hourly snow depth measurements collected using Judd snow depth sensors for water year 2010-2015 (1 October 2009 – 30 September 2015) at the Alpine site, located just southwest of the Tundra Lab in the Niwot Ridge Long-Term Ecological Research (NWTLTER) project area. The file contains both level 0 and level 1 (see details in “Process Description” below) hourly snow depth data measured in centimeters, and an accompanying metadata file.

openCC (other)Oct 2019View details →

Snow depth sensor measurement data for Lower Sub Alpine site, 2010 - 2015

Effects of infrared heaters on snow accumulation, snowmelt, and snow–atmosphere energy exchange were examined at Niwot Ridge, Colorado (CO). These .zip data files contains hourly snow depth measurements collected using Judd snow depth sensors for water year 2010-2015 (1 October 2009 – 30 September 2015) at the Lower Sub Alpine site, located southeast of the Tundra Lab, below treeline in the Niwot Ridge Long-Term Ecological Research (NWTLTER) project area. The file contains both level 0 and level 1 (see details in “Process Description” below) hourly snow depth data measured in centimeters, and an accompanying metadata file.

openCC (other)Dec 2018View details →

Snow depth data for saddle grid, 1982 - 1990.

The depth of snow was measured at 88 points on the Saddle grid. Depths were measured form fixed extendable poles on the western or accumulation portion of the 350 x 500 m grid, while depths were measured by probing on the eastern portion. Measurements during winter months varied from every 2 weeks to about monthly depending on weather conditions. Measurements during the summer were approximately weekly until all snow disappeared or snow accumulation began for the next winter. Meltout date can be determined from the last date snow was present at a grid point. The area of the study was 19.7 ha.

opencc-by-4.0Jan 2020View details →

Terrestrial Laser Scanner observations of snow depth distribution at Col du Lautaret and Col du Lac Blanc mountain sites

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.

opencc-by-4.0Sep 2020View details →

Snow depth and land surface cover in Tuolumne basin (California) from Pléiades images

This dataset contains products calculated from Pléiades images. Details about the products are available in https://doi.org/10.5194/tc-2020-15. These products were used in Figure 4. - pleiades_elevation_difference_raw_winter_minus_summer.tif  : raw difference of digital elevation models (DEMs) calculated from Pléiades stereo images. - pleiades_snow_depth_winter.tif : difference of DEMs on snow terrain only (where pleiades_land_surface_cover_winter.tif==1 with morphological erosion) - pleiades_land_surface_cover_winter.tif : land cover surface in the winter images (1= snow, 2=forest, 3= stable terrain, 4=water) - pleiades_land_surface_cover_summer.tif : land cover surface in the summer images (1= snow, 2=forest, 3= stable terrain, 4=water)   - elevation_difference_style.qml :  qgis style used for elevation difference and snow depth. -  land_surface_cover_style.qml :  qgis style used for land cover surface.

opencc-by-4.0Apr 2024View details →

Sentinel-1 Derived Snow Depths and SnowEx Lidar Netcdfs

These are netcdfs of S1 raw data, intermediate products, derived snow depths, ancillary data (IMS snow coverage, tree percentage) and lidar snow depths used in an analysis of the Lievens et al. (2021) algorithm.   9 sites - Banner 2020, Banner 2021, Cameron 2021, Dry Creek 2020, Fraser 2020, Fraser 2021, Little Cottonwood Canyon 2021, Mores 2020, Mores 2021   Data Variables: s1 - sentinel 1 backscatter data. contains 3 bands - VV, VH, and incidence angle ims - IMS snow coverage data (4 = snow covered, 2 = None) [] fcf - Forest coverage fraction [%] deltaCR - change in the S1 cross ratio through time [dB] deltaVV - change in S1 VV backscatter through time [dB] deltaGamma - change in combined gamma variable [dB] snow_index - snow index in dB that is converted to derived snow depth by C parameter [dB] snow_depth - derived snow depth from S1 [m] wet_flag - flagged for snow with -2dB of change in CR. 1 = wet, 0 = dry alt_wet_flag - snow flagged by negative snow_index. 1 = wet 0 = dry freeze_flag - snow flagged as refreezing by increase of 1 dB in CR wet_snow - combined wet flag, alt wet flag, freeze flag, and previous time step's wet snow to get current wet snow flags perma_wet - snow that is flagged as wet more than 50% of last four acquisitions after Feb 1 lidar-sd - Lidar derived snow depths [m] lidar-vh - lidar derived vegetation heights [m] lidar-dem - lidar derived snow free dems [m] aspect - aspect in degrees from lidar-dem [°] easting - degrees of easting from aspect[°] north - degrees of northing from aspect[°] confidence - unused metric of confidence

opencc-by-4.0Dec 2021View details →

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

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

opencc-by-4.0Jul 2022View details →

GPR snow depth survey over Svalbard Glaciers

Dataset contains results of GPR surveys of snowpack performed  on five glaciers in Svalbard (Slakbreen, Longyearbreen, Maritbreen, Philipbreen and  Holtedahlfonna ). Surveys were performed in March - April 2008, with 800 MHz antenna (Mala ProEx system). Fieldwork was funded by the Svalbard Integrated Arctic Earth Observing System Access project "Snow Observations in Svalabr (SOS)". Dataset consists of following unprocessed files: *.RAD - survey system and antenna control file *.COR - trace number, date, time and poistion *.MRK - reference markers *.RD3 - radarogram (clsed MALA ProEx format)