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10 results for “Snow monitoring”
Long-term snow chemical composition monitoring - Hansbreen glacier (Hornsund) - raw data
<p>During the accumulation season, snow samples were taken on Hansbreen Glacier. Several times per season. Snow samples were collected in polyethylene sterile bags and transported to the Polish Polar Station Hornsund. After melting at room temperature, the pH, conductivity and chemical composition (major ions) were analysed in the chemical laboratory of the Polish Polar Station.<br> Snow chemical composition: major ions, HCO3-, pH, conductivity</p> <p>Presented data from 2015 to 2019</p> <p>The data has not been checked, which means that it is raw data.</p> <p>Principal Investigator (PI) Adam Nawrot</p>
Dataset for: Neutrons on Rails -- trans-regional monitoring of soil moisture and snow water equivalent
<p>Using the railway system for regular environmental monitoring could extend the measurement capability to trans-regional and nationwide scales. Cosmic-ray neutron detectors in trains respond to spatial patterns of water content in their environment. Three distinct real world experiments support a proof of concept for soil and snow water monitoring using trains on short and long-range tracks across Germany:</p> <ul> <li>Supplement S4: Data (raw and processed) for the train journey from Leipzig to Berlin.</li> <li>Supplement S5: Data (raw and processed) for the train journey from Dessau to Zerbst, the subsequent car-borne Rover measurements, and the TDR measurements.</li> <li>Supplement S6: Data (raw and processed) for the train journey from Garmisch-Partenkirchen to Munich to Leipzig.</li> </ul> <p>This is the dataset supplementing the corresponding GRL publication "Neutrons on Rails -- trans-regional monitoring of soil moisture and snow water equivalent", preprint available from: https://doi.org/10.1002/essoar.10507363.1</p>
Snowpack ensemble simulations at the Kühtai snow monitoring station
<p>This dataset presents an ensemble of 230000 point-scale snowpack simulations at the snow monitoring station Kühtai (Tyrol, Austria) over the course of 25 winter seasons. The dataset splits in simulations for various sensitivity analysis designs: i.e. to assess the sensitivity of 1) forcing data error, model structure, and parametrization, 2) different forcing variables while perturbing model structure and parametrization, 3) model structures while perturbing forcing errors and parametrization.</p>
Snowpack ensemble simulation at the Kühtai snow monitoring station
<p>This dataset presents an ensemble of 230000 point-scale snowpack simulations at the snow monitoring station Kühtai (Tyrol, Austria) over the course of 25 winter seasons. We provide time series of simulated snow water equivalent, errors of these simulations and the corresponding sensitivity indices. The dataset splits in simulations for various sensitivity analysis designs: i.e. to assess the sensitivity of a) forcing data error, model structure, and parametrization, b) different forcing variables while perturbing model structure and parametrization, c) model structures while perturbing forcing errors and parametrization.</p>
Supporting information for "Incoming Neutron Flux Corrections for Cosmic-ray Soil and Snow Sensors Using the Global Neutron Monitor Network"
<p>This dataset includes 2 files that represent supporting information for McJannet, D and Desilets, D (Submitted 2023)"Incoming Neutron Flux Corrections for Cosmic-ray Soil and Snow Sensors Using the Global Neutron Monitor Network" Water Resources Research.</p> <p>File 1 - Supporting Information 1 - Example calculation: Excel sheet showing demonstration calaculations using the neutron intensity correction described in the paper</p> <p>File 2 - Supporting information 2 - List of neutron moniotr stations used in the paper and acknowledgment of their contribtuion</p>
Monitoring spatio-temporal snow depth in the Chilean Andes using spaceborne tri-stereo photogrammetry
<p>Data provided for submitted work:</p> <p>Thomas E. Shaw, César Deschamps-Berger, Simon Gascoin, James McPhee</p> <p>Monitoring spatial and temporal differences in Andean snow depth <br> derived from satellite tri-stereo photogrammetry</p> <p>---------</p> <p><strong>Contents:</strong></p> <p> <strong>'SDmap_2017_3m.tif</strong>' = Filtered DEM difference for September 4th 2017.<br> <strong>'SDmap_2019_3m.tif'</strong> = Filtered DEM difference for September 2nd 2019.<br> <strong>'SDmap_gapfilled_2017_3m.tif'</strong> = Filtered DEM difference for September 4th 2017, gap-filled by random forest model. Random forest model initialised by 100 runs, using all available snow depths in 2017 and topographic indices as predictors (Shaw et al., 2020).<br> <strong>'SDmap_gapfilled_2019_3m.tif'</strong> = As above, but for the filtered DEM difference of September 2nd 2019. <br> <strong> 'Slope_3m.tif'</strong> = Slope angle (°) derived from the snow-free DEM (6th January 2018).<br> <strong>'TPI_3m.tif' </strong>= Topographic position index (TPI - Revuelto et al., 2014) derived from the snow-free DEM (6th January 2018) based upon a 60 m search distance.<br> <strong>'Aspect_3m.tif' </strong>= Aspect (°) derived from the snow-free DEM (6th January 2018).<br> <strong>'Exposure_SX_3m.tif'</strong> = Exposure parameter (SX) based upon Winstral et al. (2002) derived from the snow-free DEM (6th January 2018) and dominant ERA5 10 m wind direction for 2017 and 2019 winter average (45°).<br> <strong>'SkyViewFraction_3m.tif' </strong>= Sky view fraction derived from the snow-free DEM (6th January 2018).</p> <p>---------</p> <p><strong>NOTE:</strong></p> <p>Products of the Pléiades DEM processing are provided here, though publication of the raw Pléiades DEMs are restricted by the regulations of the CNES agreement for grant PNTS‐2018‐ 4.</p> <p>---------</p> <p><strong>Cited works:</strong></p> <p><strong>Revuelto, J., López-Moreno, J. I., Azorin-Molina, C., & Vicente-Serrano, S. M. (2014)</strong>. Topographic control of snowpack distribution in a small catchment in the central Spanish Pyrenees: Intra- and inter-annual persistence. The Cryosphere, 8(5), 1989–2006. https://doi.org/10.5194/tc-8-1989-2014<br> <strong>Shaw, T. E., Gascoin, S., Mendoza, P. A., Pellicciotti, F., & McPhee, J. (2020)</strong>. Snow Depth Patterns in a High Mountain Andean Catchment from Satellite Optical Tristereoscopic Remote Sensing Water Resources Research. Water Resources Research, 56, 1–23. https://doi.org/10.1029/2019WR024880<br> <strong>Winstral, A., Elder, K., & Davis, R. E. (2002)</strong>. Spatial Snow Modeling of Wind-Redistributed Snow Using Terrain-Based Parameters. Journal of Hydrometeorology, 3(5), 524–538. https://doi.org/10.1175/1525-7541(2002)003<0524:SSMOWR>2.0.CO;2</p>
Data from: DIY meteorology: use of citizen science to monitor snow dynamics in a data-sparse city
Cities are under pressure to operate their services effectively and project costs of operations across various timeframes. In high-latitude and high-altitude urban centers, snow management is one of the larger unknowns and has both operational and budgetary limitations. Snowfall and snow depth observations within urban environments are important to plan snow clearing and prepare for the effects of spring runoff on cities' drainage systems. In-house research functions are expensive, but one way to overcome that expense and still produce effective data is through citizen science. In this paper, we examine the potential to use citizen science for snowfall data collection in urban environments. A group of volunteers measured daily snowfall and snow depth at an urban site in Saskatoon (Canada) during two winters. Reliability was assessed with a statistical consistency analysis and a comparison with other data sets collected around Saskatoon. We found that citizen-science-derived data were more reliable and relevant for many urban management stakeholders. Feedback from the participants demonstrated reflexivity about social learning and a renewed sense of community built around generating reliable and useful data. We conclude that citizen science holds great potential to improve data provision for effective and sustainable city planning and greater social learning benefits overall.
Long-term snow chemical composition monitoring - Ariebreen glacier (Hornsund) - raw data
<p>Since 2020, snow samples have been taken from the Ariebreen glacier several times a season during the accumulation season. The snow samples are collected in polyethylene sterile bags and transported to the Polish Polar Station Hornsund. After melting at room temperature, they are analysed in the chemical laboratory of the Polish Polar Station for pH, conductivity and chemical composition (major ions).<br> <br> Site Information Ariebreen - 0.5 km long glacier between Skoddefjellet and the northern part of Ariekammen, southernmost in Wedel Jarlsberg Land.</p> <p>Presented data from 2020 to 2022</p> <p>The data has not been checked, which means that it is raw data.</p> <p>Principal investigator (PI) Adam Nawrot</p>
Data from: DIY meteorology: use of citizen science to monitor snow dynamics in a data-sparse city
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GPM Ground Validation Snow Depth Monitoring System OLYMPEX V1
The GPM Ground Validation Snow Depth Monitoring System OLYMPEX dataset consists of snow depth, temperature, and relative humidity measurements which were collected using snow depth poles, time lapse cameras, temperature/relative humidity sensors, and manual snow surveys. This dataset was collected during the GPM Ground Validation Olympic Mountain Experiment (OLYMPEX) held on the Olympic Peninsula in the Pacific Northwest of the United States. The analyzed data files are available in netCDF-3 data format. The dataset includes the individual camera photos of snow poles taken hourly during the field campaign, provided as JPG images. There are up to 3 cameras/poles per study site location. In addition, a Microsoft Excel data file contains results of a manual snow survey taken on the specific days of the Airborne Snow Observatory OLYMPEX overflights. In total, measurements contained in this dataset extend from September 5, 2014 through August 20, 2016, but the primary field campaign data were collected during the fall 2015 to spring 2016 time period.
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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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