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61 results for “Snow modeling”
Preliminary data of drifting snow mass flux from the lower SPC at MOSAiC (2020-01-26 to 2020-02-04) for the submitted paper "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model"
<p>Preliminary data of lower SPC massflux from MOSAiC, for the time period 2020-01-26 -- 2020-02-04.</p> <p>1-h averaged time series of mass flux (kg/m²/h) to compare with the ALPINE3D simulation results.</p> <p>Will soon be replaced with a DOI / Repositiry at the Arctic Data Centre from BAS.</p>
Data from: Dynamic models for impact-initiated stress waves through snow columns
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Data from: The utility of information flow in formulating discharge forecast models: a case study from an arid snow-dominated catchment
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Snow-Off Digital Terrain Model (DTM) shaded relief from 2010 LiDAR Niwot Ridge LTER Project Area, Colorado
Citation: Anderson, S.P., Qinghua, G., and Parrish, E.G., 2012, Snow-on and snow-off LiDAR point cloud data and digital elevation models for study of topography, snow, ecosystems, and environmental change at Boulder Creek Critical Zone Observatory, Colorado: Boulder Creek CZO, INSTAAR, University of Colorado at Boulder, digital media. This 1m Digital Terrain Model (DTM) shaded relief is a snow-off DTM derived from bare-ground Light Detection and Ranging (LiDAR) point cloud data from August 2010 for the Boulder Creek Critical Zone Observatory (CZO), near Boulder Colorado. This dataset is better suited for derived layers such as slope angle, aspect, and contours. The DTM was created from 1,375 LiDAR point cloud tiles subsampled from 10 points/m2 to 1-meter postings, acquired by the National Center for Airborne Laser Mapping (NCALM) project. This data was collected in collaboration between the Boulder Creek CZO and NCALM, both funded by the National Science Foundation (NSF). The DTM has the functionality of a map layer for use in Geographic Information Systems (GIS) or remote sensing software. Total area imaged is 598.92 km^2. The LiDAR point cloud data was acquired with an Optech Gemini Airborne Laser Terrain Mapper (ALTM) and mounted in a Piper Twin PA-31 Chieftain with Inertial Measurement Unit (IMU) at a flying height of 600 m. Data from four GPS (Global Positioning System) ground stations were used for aircraft trajectory determination. The continuous DTM surface was created by mosaicing and then kriging 0.5 x 1 km LiDAR point cloud LAS-formated tiles using Golden Software's Surfer 8 Kriging algorithm. Horizontal accuracy is at least, but usually better than, 11 cm RMSE at 1 sigma and vertical accuracy is 5-30 cm RMSE at 1 sigma. The layer is available in IMAGINE format approx. 4 GB of data. It has a UTM zone 13 projection, with a NAD83 horizonal datum and a NAVD88 vertical datum, with FGDC-compliant metadata. A shaded relief model was also generated. A similar la
End-Century Daily Snow Water Equivalent (SWE) Projections with LSTM model
<p>CMIP5 climate models: CESM-CAM5 (CESM), CNRM-CM5 (CNRM), EC-EARTH (EC), HadGEM2-ES (HadGEM), MIROC5, and GFDL-ESM2M (GFDL). LOCA downscaled forcings are used as inputs to the LSTM model. 'RCP' stands for RCP8.5 and 'hist' for historical simulations. </p> <p>Corresponding code repository: https://github.com/ShihengDuan/code-SWE</p>
The station-based error information of monthly snow depth, precipitation and air temperature for CMIP6 models in mainland China
<p>This dataset contains the data of monthly snow depth in terms of RMSD (cm), spatial correlation (R<sub>s</sub>), temporal correlation (R<sub>t</sub>), consistency index (CI), and Hotspot score (H-score) of the 1415 weather stations (only 342 stations with longterm observations were available for R<sub>t</sub>, CI and H-score) in China used for evaluating the snow depth simulated or estimated from 31 CMIP6 models, MERRA2 reanalysis and a remote sensing snow depth dataset (Che). It also includes the data of errors and accumulated errors of monthly precipitation (mm) and air temperature (℃) from the 342 stations of all the 31 CMIP6 models, which can be used for constructing the regression models for analyzing error sources of snow depth simulations. The NA values of monthly precipitation and temperature indicate that the effects of accumulated errors were ignored for the corresponding month and station.</p>
Model dataset for the journal publication titled "Improved snow albedo evolution in Noah-MP land surface model coupled with a physical snowpack radiative transfer scheme"
<div> <p>This is the Noah-MP model simulation dataset for the journal publication titled "Improved snow albedo evolution in Noah-MP land surface model coupled with a physical snowpack radiative transfer scheme"</p> <p> </p> </div>
Data for Modeling Snow on Sea Ice using Physics Guided Machine Learning
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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>
Snow particle number, aerosol concentration and 10 meter windspeed data from MOSAiC, N-ICE, Weddel Sea expeditions and chemistry transport model data (p-TOMCAT) .
<p>The folder contains data for the MOSAiC, N-ICE and Weddell sea expedition for Snow particle counter measurements. Coarse aerosol measurements from the MOSAiC expedition are also included. Simulation data from a chemistry transport model (p-TOMCAT) is also available. This version includes both .mat and .nc files</p>
Using a multi-layer snow model for transient paleo studies: surface mass balance evolution during the Last Interglacial
<p>This archive provides source data of figures in the main text of the manuscript "Using a multi-layer snow model for transient paleo studies: surface mass balance evolution during the Last Interglacial".</p>
Physics‐Based Narrowband Optical Parameters for Snow Albedo Simulation in Climate Models
<p>This is a supplementary file for a submitted paper"Physics-based effective broadband optical parameters for snow albedo simulation in climate models".</p> <p>The authors derived a set of snow optical properties that effective in broadband snow radiative transfer simulation. These parameters are physically-based. </p>
Data and GrADS scripts for "Changes in March mean snow water equivalent since the mid-twentieth century and the contributing factors in reanalyses and CMIP6 climate models", submitted to The Cryosphere
<p>Data and GrADS (Grid Analysis and Display System) scripts for reproducing the figures and numerical results included in the manuscript "Changes in March mean snow water equivalent since the mid-twentieth century and the contributing factors in reanalyses and CMIP6 climate models". Revised for The Cryosphere in March 2023.</p> <p>In addition to the README file, there are two zipped archives:</p> <p>swe_trends.zip (2.3 GB) includes both the data (mostly as GrADS binaries), the GrADS data descriptor files and the scripts.</p> <p>swe_trends_no_data.zip (74 kB) includes just the scripts and the data descriptor files.</p> <p>Please see the README file for further details on the content and use of the archives.</p>
Preliminary DOI/Repository of ALPINE3D and SNOWPACK data of the submitted paper "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model"
<p>There are 2 zip folders in this repository.</p> <p>"a3d_jgr.zip" contains a folder structure that must be kept as it is in order to run the simulation in the current configuration.<br> The setup contains both input and output data as well as the model configuration as used in the submitted manuscript <br> "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model".</p> <p>The zip file contains 3 main folders:</p> <ul> <li>base_setup_files</li> <li>a3d_jgr_alpha1</li> <li> a3d_jgr_alpha3</li> </ul> <p>The "base_setup_files" contains all input files that are necessary to run the reference (R) simulation ("a3d_jgr_alpha1" folder) and the comparison "C" scenario ("a3d_jgr_alpha3") folder. In the a3d_jgr_alpha1 and a3d_jgr_alpha3 folders you find the corresponding outputs as used in the paper, as well as the settings used - which only differ by the changed "SCHMIDT_DRIFT_FUDGE" value that is found in each a3d_jgr_alphax/setup/io.ini file. The input data is already linked accordingly in each io.ini file.</p> <p>a3d_jgr_alpha1 also contains the detailed snow profiles for each point along the transects.</p> <p>To reproduce the results, download and compile the source code for the adjusted ALPINE3D model first, which can be obtained from https://gitlabext.wsl.ch/snow-models/alpine3d.git under the "alpine3d_mosaic" branch. After installing, you can run the provided model setup uploaded here.</p> <p>_________________________________________________________________________________________________________<br> <br> "SNOWPACK_JGR.zip" contains both input and output data for SNOWPACK as well as the model configuration as used in the submitted manuscript "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model".</p> <p>The zip file contains 2 main folders: </p> <ul> <li>SNOWPACK_JGR_ALPHA1</li> <li>SNOWPACK_JGR_ALPHA3</li> </ul> <p>In the SNOWPACK_JGR_ALPHA1 (reference "SP_R" simulation) and SNOWPACK_JGR_ALPHA3 (comparison "SP_C" scenario) folders you find the corresponding inputs, outputs and configuration as used in the paper, as well as the settings used - which only differ by the changed "SCHMIDT_DRIFT_FUDGE" value that is found in each SNOWPACK_JGR_ALPHA/setup/io.ini file. The input data is already linked accordingly in each io.ini file.</p> <p>To reproduce the results, download and compile the source code for the adjusted SNOWPACK model first, which can be obtained from https://gitlabext.wsl.ch/snow-models/snowpack.git under the "snowpack_mosaic" branch. After installing, you can run the provided model setup uploaded here.</p>
Assessing Eolian Snow Redistribution in Din-Gad Catchment, Central Himalaya, using Remote Sensing and Modelling
<p>This directory contains files related to the MSc graduation research project of Luc van Dijk of the Department of Physical Geography, Utrecht University. The project is titled "<em>Assessing Eolian Snow Redistribution in Din-Gad Catchment, Central Himalaya, using Remote Sensing and Modelling</em>" and was completed on April 14, 2023. Below is a description of the files in this directory.</p> <p><strong>Satellite_imagery.zip</strong><br> Folder containing all the pre-processed satellite images, that were exported from Google Earth Engine. Additional to the standard image bands, the bands 'NDSI', 'SC' and 'ASI' are present. These describe the Normalized Difference Snow Index, the Snow Cover and the Avalanche Susceptibility Index, respectively. The Google Earth Engine pre-processing script can be found here: https://code.earthengine.google.com/b7fe3ca48de410c9a3fff842f88a1e7f</p> <p><strong>SPHY_output_SnowStorage.zip</strong><br> Folder containing the SPHY output maps (variable: SnowStorage in mm) of the study domain.</p> <p><strong>SRCM.py</strong><br> The python-based Snow Redistribution Classification Model.</p> <p><strong>SRCM_output.zip</strong><br> Folder containing the SRCM output files. Each file has 8 bands: (1) snowmelt, (2) snow removal by avalanching, (3) eolian snow removal, (4) unexplained snow removal, (5) snowfall, (6) snow deposition by avalanching, (7) eolian snow deposition, (8) unexplained snow deposition.</p> <p><strong>SRCM_output_aggregated_wind_heatmaps.zip</strong><br> Folder containing the results of SRCM_output.zip, but only bands 3 and 7 and aggregated per month.</p> <p><strong>WindNinja_output_resampled.zip</strong><br> Folder containing the wind fields that were downscaled from ERA5-Land data using WindNinja. The wind fields were converted from vector (speed, direction), to 2-band raster layers (speed, direction) and resampled from 100 m to 30 m resolution.</p>
Earth system model simulation results (1980-2014) for snow analysis
<p>This dataset contains monthly output in 1984-2014 from simulations using E3SM. To extract them, you should first collect the files together and run <code>zip -F sd_simulation_output_sliced.zip --out sd_simulation_output.zip, then unzip sd_simulation_output.zip</code>.</p>
Model dataset for the journal publication entitled "New features and enhancements in Community Land Model (CLM5) snow albedo modeling: description, sensitivity, and evaluation"
<p>This is the global 1-deg CLM5-SNICAR model simulation dataset for the journal publication entitled "New features and enhancements in Community Land Model (CLM5) snow albedo modeling: description, sensitivity, and evaluation".</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>
Snow Properties and Its Modeling for Studying Gas Exchange Under the Simulated Avalanche Snow
ClinicalTrials.gov study NCT03413878. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Hypercapnia and Gas Exchange Under the Avalanche Snow Model (HyperAvaSM)
ClinicalTrials.gov study NCT02521272. IPD Sharing: Not stated. Countries: 1. Publications: 3.
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OpenNeuro
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