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966 results for “Snow”
Snow dataset for Mount-Lebanon (2011-2016) - Beta
<p>We present a comprehensive snow dataset for Mont-Lebanon. The dataset includes continuous meteorological observations from three high elevation automatic weather stations (AWS), snowpack field measurements collected at 30 different snow courses (elevation range 1300-2900 m a.s.l.), and post-processed MODIS snow products. Meteorological and snow observations are presented for the snow seasons (November-June) between 2011 and 2016 for Mzar (MZA, 2294 m a.s.l.), 2014-2016 for the Cedars (CED, 2834 m a.s.l.), and 2015-2016 for Laqlouq (LAQ, 1830 m a.s.l.). Meteorological and snow data includes snow depth, temperature, relative humidity, incoming and reflected solar radiation, wind speed and direction, and atmospheric pressure measured at 30-min interval. Snow depth, snow density, and snow water equivalent were measured at the 30 different snow courses during snow season 2015 and 2016 with an average revisit time of 11.4 days. Post-processed daily MODIS snow cover area (SCA) and snow cover duration (SCD) products are presented for the three snow dominated basins (Abou Ali, Ibrahim, and El Kelb) and cover the time period from 01 September 2011 to 31 August 2016. The current dataset is at doi:10.5281/zenodo.321405.</p>
Snow dataset for Mount-Lebanon (2011-2016)
<p>We present a comprehensive snow dataset for Mont-Lebanon. The dataset includes continuous meteorological observations from three high elevation automatic weather stations (AWS), snowpack field measurements collected at 30 different snow courses (elevation range 1300-2900 m a.s.l.), and post-processed MODIS snow products. Meteorological and snow observations are presented for the snow seasons (November-June) between 2011 and 2016 for Mzar (MZA, 2294 m a.s.l.), 2014-2016 for the Cedars (CED, 2834 m a.s.l.), and 2015-2016 for Laqlouq (LAQ, 1830 m a.s.l.). Meteorological and snow data includes snow depth, temperature, relative humidity, incoming and reflected solar radiation, wind speed and direction, and atmospheric pressure measured at 30-min interval. Snow depth, snow density, and snow water equivalent were measured at the 30 different snow courses during snow season 2015 and 2016 with an average revisit time of 11.4 days. Post-processed daily MODIS snow cover area (SCA) and snow cover duration (SCD) products are presented for the three snow dominated basins (Abou Ali, Ibrahim, and El Kelb) and cover the time period from 01 September 2011 to 31 August 2016.</p>
The Kühtai dataset: 25 years of lysimetric, snow pillow and meteorological measurements
<p>This dataset presents long-term observations from an experimental snow lysimeter plot in Kühtai (Austrian Alps). The data set includes 15 minutes data of snow water equivalent from a 10 m² snow pillow, snow melt outflow from a 10 m² snow lysimeter placed at the same location as the pillow, meteorological data (precipitation, incoming global radiation, reflected short wave radiation, air temperature, relative air humidity and wind speed), and other data (snow depths, snow temperatures at seven heights) from the period October, 1990 – May, 2015. All data have been quality checked, and gaps in the meteorological data have been filled in.</p>
Data of LAI-L20C in Vegetation masking effect on future warming and snow albedo feedback in a boreal forest region of northern Eurasia according to MIROC-ESM
<p>Data of LAI-L20C experiment in the research paper: Vegetation masking effect on future warming and snow albedo feedback in a boreal forest region of northern Eurasia according to MIROC-ESM.</p> <p>The paper was submitted to JGR-Atmosphere.</p> <p>Variables are limited to those used in the paper.</p> <ul> <li>snow water equivalent (swe)</li> <li>snow cover fraction (snc)</li> <li>clear-sky downward shortwave radiation at surface (rsdscs)</li> <li>clear-sky upward shortwave radiation at surface (rsuscs)</li> <li>surface air temperature (tas)</li> </ul> <p>See the paper for the detail.</p>
LSD4WSD : An Open Dataset for Wet Snow Detection with SAR Data and Physical Labelling
<p><strong>LSD4WSD V2.0</strong></p><p><strong>L</strong>earning <strong>S</strong>AR <strong>D</strong>ataset for <strong>W</strong>et <strong>S</strong>now <strong>D</strong>etection - Full Analysis Version. </p><p>The aim of this dataset is to provide a basis for automatic learning to detect wet snow. It is based on Sentinel-1 SAR GRD satellite images acquired between August 2020 and August 2021 over the French Alps. The new version of this dataset is no longer simply restricted to a classification task, and provides a set of metadata for each sample.</p><p>Modification and improvements of the version 2.0.0 :</p><ul><li><i>Number of massif:</i> add 7 new massif to cover the all Sentinel-1 images (cf `info.pdf`).</li><li><i>Acquisition:</i> add images of the descending pass in addition to those originally used in the ascending pass.</li><li><i>Sample: </i>reduction in the size of the samples considered to 15 by 15 to facilitate evaluation at the central pixel.</li><li><i>Sample: </i>increased density of extracted windows, with a distance of approximately 500 meters between the centers of the windows.</li><li><i>Sample:</i> removal of the pre-processing involving the use of logarithms.</li><li><i>Sample:</i> removal of the pre-processing involving the normalisation.</li><li><i>Labels:</i> new structure for the labels part: dictionary with keys: `topography`, `metadata` and `physics`.</li><li><i>Labels:</i> `physics`: addition of direct information from the CROCUS model for 3 simulations: Liquid Water Content, snow height and minimum snowpack temperature.</li><li><i>Labels:</i> `topography`: information on the slope, altitude and average orientation of the sample.</li><li><i>Labels:</i> `metadata` : information on the date of the sample, the mountain massif and the run (ascending or descending).</li><li><i>Dataset</i>: removal of the train/test split*</li></ul><p>*We leave it up to the user to use the Group Kfold method to validate the models using the alpine massif information.</p><p>Finally, it consists of 2467516 samples of size 15 by 15 by 9. For each sample, the 9 metadata are provided, using in particular the <a href="https://www.umr-cnrm.fr/spip.php?article265&lang=en">Crocus</a> physical model:</p><ul><li>topography:<ul><li>elevation (meters) (average),</li><li>orientation (degrees) (average),</li><li>slope (degrees) (average),</li></ul></li><li>metadata:<ul><li>name of the alpine massif,</li><li>date of acquisition,</li><li>type of acquisition (ascending/descending),</li></ul></li><li>physics<ul><li>Liquid Water Content (km/m2),</li><li>snow height (m),</li><li>minimum snowpack temperature (Celsius degree).</li></ul></li></ul><p>The 9 channels are in the following order:</p><ul><li>Sentinel-1 polarimetric channels: VV, VH and the combination C: VV/VH in linear,</li><li>Topographical features: altitude, orientation, slope</li><li>Polarimetric ratio with a reference summer image: VV/VVref, VH/VHref, C/Cref**</li></ul><p>** The reference image selected is that of August 9th 2020, as a reference image without snow (cf. <a href="https://ieeexplore.ieee.org/document/842004">Nagler&al</a>)</p><p>An overview of the distribution and a summary of the sample statistics can be found in the file info.pdf.</p><p>The data is stored in .hdf5 format with gzip compression. We provide a python script to read and request the data. The script is dataset_load.py. It is based on the h5py, numpy and pandas libraries. It allows to select a part or the whole dataset using requests on the metadata. The script is documented and can be used as described in the README.md file</p><p>The processing chain is available at the following <a href="https://github.com/Matthieu-Gallet/LSD4WSD-dataset"><strong>Github</strong></a> address.</p><p>The authors would like to acknowledge the support from the National Centre for Space Studies (CNES) in providing computing facilities and access to SAR images via the PEPS platform.</p><p>The authors would like to deeply thank Mathieu Fructus for running the Crocus simulations.</p><p><strong>Erratum :</strong></p><p>In the dataloader file, the name of the "aquisition" column must be added twice, see the correction below.:</p><blockquote><p>dtst_ld = Dataset_loader(path_dataset,shuffle=False,descrp=["date","massif","aquisition","aquisition","elevation","slope","orientation","tmin","hsnow","tel",],) </p></blockquote><p>If you have any comments, questions or suggestions, please contact the authors: </p><ul><li>matthieu.gallet@univ-smb.fr</li><li>fatima.karbou@meteo.fr</li><li>abdourrahmane.atto@univ-smb.fr</li><li>emmanuel.trouve@univ-smb.fr</li></ul>
Snow flies self-amputate freezing limbs to sustain behavior at sub-zero temperatures
<p><span>All living things are profoundly affected by temperature. In spite of the thermodynamic constraints on biology, some animals have evolved to live and move in extremely cold environments. Here, we investigate behavioral mechanisms of cold tolerance in the snow fly (<em>Chionea</em> spp.), a flightless crane fly that is active throughout the winter in boreal and alpine environments of the northern hemisphere. Using thermal imaging, we show that adult snow flies maintain the ability to walk down to an average body temperature of -7 °C. At this supercooling limit, ice crystallization occurs within the snow fly's hemolymph and rapidly spreads throughout the body, resulting in death. However, we discovered that snow flies frequently survive freezing by rapidly amputating legs before ice crystallization can spread to their vital organs. Self-amputation of freezing limbs is a last-ditch tactic to prolong survival in frigid conditions that few animals can endure. Understanding the extreme physiology and behavior of snow insects is important at this moment when the alpine ecosystems they inhabit are rapidly changing due to anthropogenic climate change.</span></p>
Simulated Sea Ice and Snow Thickness along the MOSAIC drift trajectory, from AWI-CM-1 and AWI-CM-3 nudged simulations.
<p>Sea ice thickness and snow (on sea ice) thickness from nudged simulations performed using the coupled climate models AWI-CM-1 (zonal wavenumber truncated at 20) and AWI-CM-3 (T20 truncation; Pithan et al., 2023) with a 1h relaxation time. The model data is collocated to the drift trajectory of the Multidisciplinary Drifting Observatory for the Study of the Arctic Climate (MOSAIC) across the Arctic Ocean, from 01 September 2019 until 31 August 2020. The collocation is done daily, by finding all model grid cells within the area covered by the distributed network of snow and sea ice measuring instruments deployed and maintained during MOSAIC. The sea ice and snow thickness is then spatially averaged for each day. </p><p>Data is provided in three .nc files for each model and variable (m_ice for sea ice thickness, m_snow for snow thickness) representing ensemble members 1 to 3.</p>
An integrated glaciological and meteorological dataset for Yulong Snow Mountain
<p><strong><span>Data Introduction</span></strong></p> <p><span>The dataset presented here offers a comprehensive insight into the dynamics of Yulong Snow Mountain (YSM), situated in a low latitude and high altitude region in the Northern Hemisphere (27.106°N, 100.205°E). The glaciers of YSM, characterized as temperate (warm) glaciers, exhibit a notable response to global environmental changes. Baishui River Glacier No. 1 (BGR1) stands as the largest and most renowned glacier on Yulong Snow Mountain. </span></p> <p><span>To reveal the glacier and climate change situation on the YSM, an alpine glacier and meteorological monitoring network has been established between 3,046 m and 4,700 m a.s.l. since 2006. This monitoring system comprises a glacier observation field and four automatic weather stations (AWSs), generating a long-term series of glacier and meteorological datasets that are the closest to the equator in the Northern Hemisphere. The dataset offers fundamental information for research on glaciers, climate, and hydrology, as well as for data collection and processing in regions of low latitude and high altitude. Moreover, it holds significant theoretical importance for the coordinated comparison of glaciers and meteorology in global hotspots.</span></p> <p><strong><span>File Names and Variable Descriptions</span></strong></p> <p><span>YSM: Yulong Snow Mountain</span></p> <p><span>BRG1: Baishui River Glacier No.1</span></p> <p><span>GHZ: Ganhaizi</span></p> <p><span>MNP: Maoniuping</span></p> <p><span>UC: Upper Cableway</span></p> <p><span>UG: Upper Glacier</span></p> <p><span>Ta: air temperature (℃)</span></p> <p><span>RH: relative humidity (%)</span></p> <p><span>Ws: wind speed (m/s)</span></p> <p><span>Ppt: precipitation (mm)</span></p> <p><span>P: air pressure (hPa)</span></p>
TVC Experiment 2018/19: Snow field measurements
<p><span>This dataset contains in situ snow measurements recorded as part of Environment and Climate Change Canada's 2018-2019 Trail Valley Creek Snow Experiment (TVC Experiment 18/19). These measurements were collected to evaluate coincident airborne and satellite radar measurements to better understand snow-radar interactions in a tundra environment. The measurements were recorded 50 km north of the town of Inuvik, Northwest Territories around the Trail Valley Creek research station (https://www.trailvalleycreek.ca/). Three periods of measurement took place in November 2018, January 2019, and March 2019 . Measurements and observations were recorded in handwritten snow pit sheets before being transcribed to electronic sheets. The dataset is organized by surveyed site and includes: 1) manual snowpit measurements of the following parameters: Total snow depth, vertical profiles of snow temperature, snow density, stratigraphy and grain size and notes on site characteristics and environmental conditions. 2) distributed snow depth measurements around the surveyed sites recorded with an automatic snow depth probe (magnaprobe), 3) SnowMicroPenetrometer (SMP) force profiles coincident with the snowpit measurements and distributed along the magnaprobe transects with its metadata file, and 4) snow microstructure profiles measuring specific surface area (SSA) using the IceCube instrument.</span></p>
Data from: Metabarcoding analysis provides insight into the link between prey and plant intake in a large alpine cat carnivore, the snow leopard
<p>Species of the family Felidae (a group represented by cats) are thought to be obligate carnivores, specialized for hunting and consuming other animals. However, the detection of plants in the feces of felids raises questions about the role of plants in their diet. This is particularly true for the snow leopard (Panthera uncia), a big cat native to central and South Asia's high mountains. Our study aimed to comprehensively identify the prey and plants consumed by snow leopards as well as six other sympatric mammals. We applied DNA metabarcoding methods on 126 fecal samples collected from the Sarychat-Ertash Nature Reserve in Kyrgyzstan. We found that among the three most common plant families in snow leopard feces, Tamaricaceae (genus Myricaraia) was consumed often by snow leopards. The genus Myricaria frequently appeared in samples lacking any animal prey DNA, indicating that snow leopards might have consumed this plant especially when their digestive tracts were empty. We also observed a significant difference in plant composition between male and female snow leopards, and potentially between sampling seasons. We provide a comprehensive overview of the prey and plants detected in the feces of snow leopards and sympatric mammals. We believe our findings will help in formulating hypotheses and guiding future research to understand the adaptive significance of plant-eating behavior in felids and animal-plant relationships in the ecosystem.</p>
Sentinel-1 Derived Snow Depths and SnowEx Lidar Netcdfs
<p>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.</p> <p> </p> <p>9 sites - Banner 2020, Banner 2021, Cameron 2021, Dry Creek 2020, Fraser 2020, Fraser 2021, Little Cottonwood Canyon 2021, Mores 2020, Mores 2021</p> <p> </p> <p>Data Variables:</p> <p>s1 - sentinel 1 backscatter data. contains 3 bands - VV, VH, and incidence angle</p> <p>ims - IMS snow coverage data (4 = snow covered, 2 = None) []</p> <p>fcf - Forest coverage fraction [%]</p> <p>deltaCR - change in the S1 cross ratio through time [dB]</p> <p>deltaVV - change in S1 VV backscatter through time [dB]</p> <p>deltaGamma - change in combined gamma variable [dB]</p> <p>snow_index - snow index in dB that is converted to derived snow depth by C parameter [dB]</p> <p>snow_depth - derived snow depth from S1 [m]</p> <p>wet_flag - flagged for snow with -2dB of change in CR. 1 = wet, 0 = dry</p> <p>alt_wet_flag - snow flagged by negative snow_index. 1 = wet 0 = dry</p> <p>freeze_flag - snow flagged as refreezing by increase of 1 dB in CR</p> <p>wet_snow - combined wet flag, alt wet flag, freeze flag, and previous time step's wet snow to get current wet snow flags</p> <p>perma_wet - snow that is flagged as wet more than 50% of last four acquisitions after Feb 1</p> <p>lidar-sd - Lidar derived snow depths [m]</p> <p>lidar-vh - lidar derived vegetation heights [m]</p> <p>lidar-dem - lidar derived snow free dems [m]</p> <p>aspect - aspect in degrees from lidar-dem [°]</p> <p>easting - degrees of easting from aspect[°]</p> <p>north - degrees of northing from aspect[°]</p> <p>confidence - unused metric of confidence</p>
A Global Dataset of Standardized Moisture Anomaly Index Incorporating Snow Dynamics (SZIsnow) from 1948 to 2010
<p>The SZI<sub>snow</sub> dataset was calculated based on systematic physical fields from the Global Land Data Assimilation System Version 2 (GLDAS-2) with the Noah land surface model. This SZI<sub>snow</sub> dataset considers different physical water-energy processes, especially snow processes. The evaluation shows the dataset is capable of investigating different types of droughts across different timescales. The assessment also indicates that the dataset has an adequate performance to capture droughts across different spatial scales. The consideration of snow processes improved the capability of SZI<sub>snow</sub>, and the improvement is evident over snow-covered areas (e.g., Arctic region) and high-altitude areas (e.g., Tibet Plateau). Moreover, the analysis also implies that SZI<sub>snow</sub> dataset is able to well capture large-scale drought events across the world. This drought dataset has high application potential for monitoring, assessing, and supplying information on drought, and also can serve as a valuable resource for drought studies.</p>
Fig. 1 in Studies in Malagasy Eugenia L. (Myrtaceae) - V: Eugenia quadriphylla N. Snow & Callm., an unusual and rare new species from the northeast
Fig. 1. – Distribution map of Eugenia quadriphylla N. Snow & Callm. (black circle) plotted on a map of forest cover in 2000 (grey) following HARPER et al. (2007).
Fig. 1. – Eugenia plurinervia N in Novitates neocaledonicae V: Eugenia plurinervia N. Snow, Munzinger & Callm. (Myrtaceae), a new threatened species with distinct leaves
Fig. 1. – Eugenia plurinervia N. Snow, Munzinger & Callm. A. Main stem with peeling grayish bark; B. General habit; C. Flower; D. Immature fruit; E. Radiating secondary nerves of the densely punctate leaves. [Photos: R.Scopetra]
Fig. 2 in Novitates neocaledonicae V: Eugenia plurinervia N. Snow, Munzinger & Callm. (Myrtaceae), a new threatened species with distinct leaves
Fig. 2. – Distribution map of subpopulations of Eugenia plurinervia N. Snow, Munzinger & Callm. (yellow dots) in an area adjacent to mining concession and RT1 road (red) in north-western New Caledonia. Areas in pink and gray indicate ultramafic substrates on Grande Terre. [© Image and mining cadastre from D.I.T.T.T./S.G.T. Governement of New Caledonia]
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>
Long-term reconstruction of satellite-based precipitation, soil moisture, and snow water equivalent in China
<p>A daily 0.1<sup>°</sup> dataset of precipitation (<em>P</em>), soil moisture (SM), and snow water equivalent (SWE) in 1981-2017 across China.</p>
GABLS4, snow model intercomparison.
<p>Content of the archive:</p> <p><strong>FORCING</strong>: The near-surface variable forcing dataset used to drive the offline simulations. Time step of 30 minutes, start date 1/12/2009, 15 days.</p> <p><strong>SIMULATIONS</strong>: NetCDF output files from participating models.</p> <p><strong>OBSERVATIONS</strong>: Surface temperature observation time series and observations of snow temperature in the snowpack at different depths used for the validation.</p> <p><strong>FIGURES</strong>: The datasets and the python scripts used to prepare the figures.</p>
Determination of areas with release potential of snow avalanche in Sharr Mountains in the Republic of Kosovo
<p>Avalanches represent a very high risk in residential areas, road infrastructure, environment, and economy, and can have fatal consequences if the human factors do not take any action. Advances in geospatial technology and access to spatial data have enabled spatial analysis to assist in decision-making regarding spatial planning in avalanche-prone locations. Determining locations with snow avalanche discharge potential is a crucial step in the avalanche zoning process.</p> <p>This research deals with areas with snow avalanche potential disjunction, based mainly on topographic factors followed by meteorological ones. Topographic factors were mainly determined according to morphometric techniques, which are achieved through geographic information systems (GIS), as well as meteorological ones from statistical data and various processing of spatial and non-spatial data. Spatial analysis are also supported by geostatistical methods Fuzzy Logic and AHP, which in interaction with GIS have enabled the achievement of the purpose of this paper. The results from the spatial analysis have been verified based on comparison methods, such as the ROC method which was used during this final phase, in which the analysis has shown that the methods used in this research have given satisfactory results. As the main result, we obtained maps of areas with snow avalanche potential discharge in the study area relating to two geostatistical methods.</p>
Long-term simulation of snow cover and its potential impacts on seasonal frost dynamics in croplands across southern Canada
<p><em>In northern climes, accurate simulation of thermal and hydrological budgets for farmlands during overwintering conditions is crucial to both an accurate prediction of spring flooding and the successful management of nutrient losses. As snow cover influences soil freezing dynamics, it has been hypothesized that reduced snow cover due to warmer winters might increase the depth and duration of frozen soil conditions. Nonetheless, such impacts remain poorly understood and, given the difficulty in measuring the depth of frozen soil, no long-term field experiment has documented these potential effects. The present study was designed to test this hypothesis. Drawing upon observed snow depth and soil temperature data collected from six research farms across Southern Canada over various time spans from 1989 to 2020, the Root Zone Water Quality Model, integrated with the Simultaneous Heat and Water model, was calibrated and validated. The potential influence of warmer winter on shifts in soil frost dynamics was evaluated by estimating the depth and duration of frozen soil for each farmland site under various RCP temperature scenarios using the RZ-SHAW model. Soil frozen depth in Eastern site increased with the increase of RCP temperature scenarios in some years, but decreased under the highest RCP temperature scenario. The monthly relationship between snow depth and soil frozen depth was determined through partial correlation analysis. Snow was most effective in alleviating soil freezing in the months of January and February, a period when snow cover depth was least affected by warming air temperatures. This paper suggests that Global warming induced-snow cover reduction would be site-specific and is </em>more likely to occur in <em>regions where energy lost through reduced snow cover would outweigh the energy gained through warmer air temperature.</em></p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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