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16 results for “snow density”
Glacier snow density 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 density measurements determined at each stake with snow present on glaciers in Taylor Valley (Canada, Commonwealth, Howard, and Taylor) and Miers Valley (Adams), all of which are located in the McMurdo Dry Valleys of Antarctica. Included here for each stake are the snow thickness and density for each layer and the average density for each pit. Most measurements began during the 93-94 field season. Adams measurements were established during the 14-15 field season. Measurements are ongoing for Adams, Commonwealth, and Howard glaciers. 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.
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>
Glaciological data (point mass balance, SWE, snow depth, bulk snow density, modelled runoff) from Werenskioldbreen (Svabard) 2009-2020
<p>This repository contains supporting data associated to the manuscript to <em>Earth System Science Data: </em></p> <p><strong>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)</strong></p> <p>In 2009-2020, 9 ablation stakes were installed on the Werenskioldbreen.<strong> </strong>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. </p>
Data from: Protection status, human disturbance, snow cover and trapping drive density of a declining wolverine population in the Canadian Rocky Mountains
<p>Protected areas are important in species conservation, but high rates of human-caused mortality outside their borders and increasing popularity for recreation can negatively affect wildlife populations. We quantified wolverine (<em>Gulo gulo</em>) population trends from 2011 to 2020 in >14 000 km2 protected and non-protected habitat in southwestern Canada. We conducted wolverine and multi-species surveys using non-invasive DNA and remote camera-based methods. We developed Bayesian integrated models combining spatial capture-recapture data of marked and unmarked individuals with occupancy data. Wolverine density and occupancy declined by 39 percent, with an annual population growth rate of 0.925. Density within protected areas was 3 times higher than outside and declined between 2011 (3.6 wolverines/1000 km2) and 2020 (2.1 wolverines/1000 km2). Wolverine density and detection probability increased with snow cover and decreased near development. Detection probability also decreased with human recreational activity. The annual harvest rate of 13% was above the maximum sustainable rate. We conclude that humans negatively affected the population through direct mortality, sub-lethal effects and habitat impacts. Our study exemplifies the need to monitor population trends for species at risk – within and between protected areas - as steep declines can occur unnoticed if key conservation concerns are not identified and addressed.</p>
Snow depth and density measurements with different snow core samplers in HARMOSNOW Field Campaigns
<p>The data correspond to snow bulk density and snow depth measured with different snow core sampler in three field campaigns carried out in mountains of Turkey, Iceland and Finland in order to assess the uncertainty of using different snow core samplers and different observers. The field campaigns were carried out in the frame of the COST project HARMOSNOW ES1404 <a href="http://harmosnow.eu/">http://harmosnow.eu/</a></p>
Data from: Protection status, human disturbance, snow cover and trapping drive density of a declining wolverine population in the Canadian Rocky Mountains
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Modelling snowpack bulk density using snow depth, cumulative degree-days and climatological predictor variables -- data set
<p>This file constitutes the data set containing the snow course survey, North American Regional Reanalysis (NARR)-derived degree-day indices, and climatological variables data used to conduct the analysis, and generate the figures and tables in the manuscript titled "Modelling snowpack bulk density using snow depth, cumulative degree-days and climatological predictor variables" by Andras J. Szeitz and R. Dan Moore. The manuscript was submitted for publication in the journal 'Hydrological Processes'.</p> <p>Due to the size of the NARR data files used to derive the air temperature time series for each snow course location, we recommend acquiring them from the National Oceanic and Atmospheric Administration's data portal directly (<a href="https://psl.noaa.gov/data/gridded/data.narr.html">https://psl.noaa.gov/data/gridded/data.narr.html</a>).</p> <p>Likewise, the ClimateNA software application used to extract the climatological variables for each snow course location can be obtained from the Centre for Forest Conservation Genetics, Department of Forest and Conservation Sciences, UBC, directly (<a href="https://climatena.ca/">https://climatena.ca/</a>).</p>
Spatial variation in population-density, movement and detectability of snow leopards in a multiple use landscape in Spiti Valley, Trans-Himalaya
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Data from: Temporal variability in snow accumulation and density at Summit Camp, Greenland ice sheet
<p>A 3-year record of weekly snow water equivalent (SWE) accumulation at Summit Camp, central Greenland ice sheet, obtained by direct sampling, is presented. While the overall SWE accumulation of 24.2 cm w.e. a<sup>−1</sup> matches long-term ice core estimates, variability increases at shorter timescales. Half of the annual SWE accumulation occurs during a few large events, with the average accumulation rate decreasing 35% between the first and second halves of the record coinciding with exceptional anticyclonic conditions in the spring and summer of 2019. No seasonality in accumulation is detected. Rather, local accumulation rates appear to be significantly impacted by wind redistribution that obscures temporal patterns in snowfall. Surface snow density is consistent, on average, with previously measured values but does not correlate with near surface temperature or wind speed. Two surface mass balance reanalysis models significantly underestimate accumulation rates at Summit Camp. This is concerning because such models are often used to estimate ice-sheet mass loss.</p>
Data from: Temporal variability in snow accumulation and density at Summit Camp, Greenland ice sheet
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Data for "Improvements in wintertime surface temperature variability in the Community Earth System Model version 2 (CESM2) related to the representation of snow density"
<p>This dataset contains all the postprocessed data required to reproduce the figures in the publication Simpson et al (2022) "Improvements in wintertime surface temperature variability in the Community Earth System Model version 2 (CESM2) related to the representation of snow density", in the Journal of Advances in Modelling the Earth System.</p>
In the shadows of snow leopards and the Himalayas: density and habitat selection of blue sheep in Manang, Nepal
<p><span>There is a growing agreement that conservation needs to be proactive and pay increased attention to common species and to the threats they face. The blue sheep (<i>Pseudois nayaur</i>) plays a key ecological role in sensitive high-altitude ecosystems of Central Asia and is among the main prey species for the globally vulnerable snow leopard (<i>Panthera uncia</i>). As the blue sheep has been increasingly exposed to human pressures, it is vital to estimate its population dynamics, protect the key populations, identify important habitats, and secure a balance between conservation and local livelihoods. We conducted a study in Manang, Annapurna Conservation Area (Nepal) to survey blue sheep on 60 transects in spring (127.9 km) and 61 transects in autumn (134.7 km) of 2019, estimate their minimum densities from total counts, compare these densities with previous estimates, and assess blue sheep habitat selection by the application of generalized additive models (GAMs). Total counts yielded minimum density estimates of 6.0-7.7 and 6.9-7.8 individuals/km<sup>2</sup> in spring and autumn, respectively, which are relatively high compared to other areas. Elevation and, to a lesser extent, land cover indicated by the normalized difference vegetation index (NDVI) strongly affected habitat selection by blue sheep, whereas the effects of anthropogenic variables were insignificant. Animals were found mainly in habitats associated with grasslands and shrublands at elevations between 4200 and 4700 m. We show that the blue sheep population size in Manang has been largely maintained over the past three decades, indicating the success of the integrated conservation and development efforts in this area. Considering a strong dependence of snow leopards on blue sheep, these findings give hope for the long-term conservation of this big cat in Manang. We suggest that long-term population monitoring and a better understanding of blue sheep-livestock interactions are crucial to maintain healthy populations of blue sheep and, as a consequence, of snow leopards. </span></p>
Data from: Density‐dependent and phenological mismatch effects on growth and survival in lesser snow and Ross's goslings
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In the shadows of snow leopards and the Himalayas: density and habitat selection of blue sheep in Manang, Nepal
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ABoVE: Dall Sheep Track Sinking Depths, Snow Depth, Hardness, and Density, 2017
This dataset contains Dall sheep (Ovis dalli dalli) hoof sinking depths in snow tracks, and snow depth, hardness, and density measurements in snow pits adjacent to the tracks. Snow measurements were collected between March 19-22, 2017 at sites on Jaeger Mesa in the Wrangell Mountains (WRST), Alaska. Estimated sheep age classes and track site coordinates are also provided.
SnowEx20 Grand Mesa IOP Lidar and GPR-Derived Snow Water Equivalent and Snow Density V001
This data set contains snow water equivalent (SWE) and snow density raster data derived from a lidar-based snow depth data set (<a href="https://doi.org/10.5067/M9TPF6NWL53K">SnowEx20 Grand Mesa IOP QSI Lidar Snow Depth Data, Version 1</a>), a lidar-based digital terrain model (<a href="https://doi.org/10.5067/2EHMWG4IT76O">ASO L4 Lidar Point Cloud Digital Terrain Model 3m UTM Grid, Version 1</a>), and two GPR data sets (<a href="https://doi.org/10.5067/Q2LFK0QSVGS2">SnowEx20 Grand Mesa IOP BSU 1 GHz Multi-polarization GPR, Version 1</a> and <a href="https://doi.org/10.5067/WE9GI1GVMQF6">Mesa IOP UNM 800 and 1600 MHz MALA GPR, Version 1</a>). Data was collected during the NASA SnowEx 2020 field campaign in Grand Mesa, Colorado.
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