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45 results for “snowpack”
Observations of snowpack distribution and meteorological variables at the Izas Experimental Catchment (Spanish Pyrenees) from 2011 to 2017
<p>We present a climatic dataset acquired at Izas Experimental Catchment, in the Central Spanish Pyrenees, from 2011 to 2017 snow seasons. The dataset includes information on different meteorological variables acquired with an Automatic Weather Station including precipitation, air temperature, incoming and reflected short and long-wave radiation, relative humidity, wind speed and direction, atmospheric air pressure, surface temperature (snow or soil surface) and soil temperature; all of them at 10 minute intervals. Snow depth distribution was measured during 23 field campaigns using a Terrestrial Laser Scanner (TLS), and there are also available time-lapse photographs from which can be derived daily information of different variables such as the Snow Covered Area. The experimental site is located in the southern side of the Pyrenees between 2000 and 2300 m above sea level with an extension of 55 ha. The site is a good example of sub-alpine ambient of mid-latitude mountain ranges. Thus, the dataset has a great potential for understanding environmental processes from a hydrometerological or ecological perspective in which snow dynamics play a determinant role.</p>
Data for climate-resilient snowpack estimation in the Western United States
<p>Generated and preprocessed files for the resilient snowpack estimation project. All preprocessed data were originally produced by the WUS-D3 project (https://dept.atmos.ucla.edu/alexhall/downscaling-cmip6) or PRISM (https://www.prism.oregonstate.edu/).</p>
Tower-based C-band radar measurements of an alpine snowpack
<p>This repository contains the data presented in the following paper: Brangers, I., Marshall, H.-P., De Lannoy, G., Dunmire, D., Matzler, C., and Lievens, H.: Tower-based C-band radar measurements of an alpine snowpack, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-2927, 2023. </p> <p>The data consist of C-band tower based radar data measured in the Idaho Rocky mountains during the snow seasons of 2021-2022 and 2022-2023. The site lies within a local ski area called 'Bogus Basin'. Information about the data collection and processing is presented in the aforementioned paper.</p> <p>The data contains arrays with the timestamps of the measurements, the sampling frequency, and matrices of the time-domain traces of radar bakcscatter at 4 channels (rows=samples/timedomain bins, increases with distance from radar; columns=traces, 1 for each ~hourly timestep ). An example script on how to read and process the data using Matlab is included.</p> <p>For any further questions please contact Isis Brangers (isis.brangers@kuleuven.be) or Hans Lievens (hans.lievens@ugent.be) </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>
Muon Scattering Radiography (MSR) measurements on blocks of ice in laboratory, and on simulated snowpack
<p>Experimental setup (scenario 5):</p> <p>Muon data used in this work has been collected with our muon detection system. This muon monitoring system is currently in use for both scientific and industrial purposes <a href="https://www.zotero.org/google-docs/?broken=RG5rWA">(Martínez-Ruiz del Árbol et al., 2022)</a>. The particle detectors are composed of four Multi-Wire Proportional Chambers (MWPC) and each chamber has two layers with 224 detection wires, all of them separated by 4 mm. The two layers form a two-dimensional grid of wires which covers an area of 89.6 x 89.6 cm and detects the positions where muons cross it.</p> <p>When a muon event is identified, our system detects four points located in the horizontal two-dimensional grids, two points before the particle goes through the target and another two points after the particle traverses it. With this data, way-in and way-out trajectories can be reconstructed, and muon deviations calculated. Specifically, in the numerical analysis of this work, we utilised the projection of muon deviations in two planes perpendicular to the detection wires.</p> <p>Simulation setup (scenarios 1 to 4):</p> <p>The snowpack was simulated using a one-dimensional snow model forced by surface meteorological data. We have used the SNOWPACK model <a href="https://www.zotero.org/google-docs/?EqYNAT">(Bartelt & Lehning, 2002</a><a href="https://www.zotero.org/google-docs/?LUKxAu">)</a> to realistically simulate the behaviour of the snowpack along two seasons, 2015/2016 (1_Modelling) and 2016/2017 (2_Testing). SNOWPACK was forced by the ERA5-Land surface reanalysis <a href="https://www.zotero.org/google-docs/?QCLBxK">(Muñoz-Sabater et al., 2021)</a>. The simulations were performed in the Pyrenees, using the ERA5-Land cell whose centroid falls closer to the Monte Perdido massif (42.7°N, -0.1°E), at an elevation of 2041m asl.</p> <p>We coupled the SNOWPACK simulations with a full MSR simulation setup that uses the Cosmic RaY generator <a href="https://www.zotero.org/google-docs/?oSIPTu">(Hagmann et al., 2012)</a> to reproduce the atmosphere muon flux and GEANT4 <a href="https://www.zotero.org/google-docs/?3ZDRDN">(Agostinelli et al., 2003)</a> to simulate the muon scattering caused by the snowpack. GEANT4 is a state-of-the-art software designed and maintained at CERN to simulate the interactions of particles and matter in high-energy and nuclear physics. Our simulation framework contains a model of our experimental setup including the muon detectors and their response. This framework has been successfully applied to multiple industrial problems, for instance, to steel-made pipe wear <a href="https://www.zotero.org/google-docs/?F8nYbS">(Martínez-Ruiz del Árbol et al., 2018)</a>. Similar simulation frameworks are typically used to research applications of muography <a href="https://www.zotero.org/google-docs/?115QeU">(Mori et al., 2017)</a>.</p> <p>We expanded the one-dimensional snowpack geometry to a 1m² snow column, assuming homogeneous snow layers in the longitude and latitude dimensions. Then, we propagated and measured muons penetrating the whole snow column, virtually reproducing the detection process using GEANT4. We collected muon deviations and their Root-mean-square (RMS) value for different accumulations of snow during the two simulated seasons.</p>
Data to support: Implications of snowpack reactive bromine production for Arctic ice core bromine preservation
<p>Snowpack emissions are recognized as an important source of gas-phase reactive bromine in the Arctic and are necessary to explain ozone depletion events in spring caused by the catalytic destruction of ozone by halogen radicals. Quantifying bromine emissions from snowpack is essential for interpretation of ice-core bromine. We present ice-core bromine records since the pre-industrial (1750 CE) from six Arctic locations and examine potential post-depositional loss of snowpack bromine using a global chemical transport model. Trend analysis of the ice-core records shows that only the high-latitude coastal Akademii Nauk ice core from the Russian Arctic preserves significant trends since pre-industrial times that are consistent with trends in sea ice extent and anthropogenic emissions from source regions. Model simulations suggest that recycling of reactive bromine on the snow skin layer (top 1mm) results in 9–17% loss of deposited bromine across all six ice-core locations. Reactive bromine production from below the snow skin layer and within the snow photic zone is potentially more important, but the magnitude of this source is uncertain. Model simulations suggest that the Akademii Nauk core is most likely to preserve an atmospheric signal compared to five Greenland ice cores due to its high latitude location combined with a relatively high snow accumulation rate. Understanding the sources and amount of photochemically reactive snow bromide in the snow photic zone throughout the sunlit period in the high Arctic is essential for interpreting ice-core bromine, and warrants further lab studies and field observations at inland locations.</p>
Data to support: Implications of snowpack reactive bromine production for Arctic ice core bromine preservation
Open the record for dataset details and reuse information.
Winter inputs buffer streamflow sensitivity to snowpack losses in the Salt River Watershed in the Lower Colorado River Basin
Recent streamflow declines in the Upper Colorado River Basin raise concerns about the sensitivity of water supply for 40 million people to rising temperatures. Yet, other studies in western US river basins present a paradox: streamflow has not consistently declined with warming and snow loss. A potential explanation for this lack of consistency is warming-induced production of winter runoff when potential evaporative losses are low. This mechanism is more likely in basins at lower elevations or latitudes with relatively warm winter temperatures and intermittent snowpacks. We test whether this accounts for streamflow patterns in nine gaged basins of the Salt River and its tributaries, which is a sub-basin in the Lower Colorado River Basin (LCRB). We develop a basin-scale model that separates snow and rainfall inputs and simulates snow accumulation and melt using temperature, precipitation, and relative humidity. Despite significant warming from 1968–2011 and snow loss in many of the basins, annual and seasonal streamflow did not decline. Between 25% and 50% of annual streamflow is generated in winter (NDJF) when runoff ratios are generally higher and potential evapotranspiration losses are one-third of potential losses in spring (MAMJ). Sub-annual streamflow responses to winter inputs were larger and more efficient than spring and summer responses and their frequencies and magnitudes increased in 1968–2011 compared to 1929–1967. In total, 75% of the largest winter events were associated with atmospheric rivers, which can produce large cool-season streamflow peaks. We conclude that temperature-induced snow loss in this LCRB sub-basin was moderated by enhanced winter hydrological inputs and streamflow production.
Data from: Combining ground‐penetrating radar with terrestrial LiDAR scanning to estimate the spatial distribution of liquid water content in seasonal snowpacks
Many communities and ecosystems around the world rely on mountain snowpacks to provide valuable water resources. An important consideration for water resources planning is runoff timing, which can be strongly influenced by the physical process of water storage within and release from seasonal snowpacks. The aim of this study is to present a novel method that combines light detection and ranging with ground‐penetrating radar to nondestructively estimate the spatial distribution of bulk liquid water content in a seasonal snowpack during spring snowmelt. We develop these methods in a manner to be applicable within a short time window, making it possible to spatially observe rapid changes that occur to this property at subdaily timescales. We applied these methods at two experimental plots in Colorado, showing the high variability of liquid water content in snow. Volumetric liquid water contents ranged from near zero to 19%vol within the scale of meters. We also show rapid changes in bulk liquid water content of up to 5%vol that occur over subdaily timescales. The presented methods have an average uncertainty in bulk liquid water content of 1.5%vol, making them applicable for future studies to estimate the complex spatio‐temporal dynamics of liquid water in snow.
Let it snow? Spring snowpack and microsite characterize the regeneration niche of high-elevation pines
<p><b>Aim: </b>The persistence potential of forests under rapid climate change will depend on species-specific tolerances to increasing growing season soil moisture stress as snowpack declines. High-elevation tree species may be particularly vulnerable to increasing water stress and associated changes to disturbance regimes because they occur at the environmental margins of tree distributions and are considered snowpack dependent. Here, we evaluate the interacting effects of climate, disturbance, and microsite conditions on tree regeneration in high-elevation, migration-limited pines that have experienced recent disturbance-induced tree mortality.</p> <p><b>Location</b>: Great Basin (California & Nevada), USA</p> <p><b>Taxon: </b>Gymnosperms; Pinaceae</p> <p><b>Methods:</b> We used field observations from 70 sites that varied in climate, disturbance, and local site conditions across semi-arid, high-elevation forests of the Great Basin. We employed structural equation models to evaluate how climate and disturbance interact with microsite conditions to influence regeneration.</p> <p><b>Results: </b>We found a broad range of establishment conditions of high-elevation conifers - whitebark, limber, and bristlecone pines – across climatic and disturbance gradients in the Great Basin, but our research detected clear differences in the regeneration niche for each species that may lead to differential survival as climate and disturbance conditions continue to change. Regeneration of whitebark and bristlecone pines diverged in their responses to spring snowpack conditions, with whitebark pine increasing and bristlecone pine decreasing with greater spring snowpack. Limber pine regenerated across a range of climatic and landscape conditions, and this generalist strategy may be advantageous if future climate and disturbance conditions exceed tolerances of more specialized species.</p> <p><b>Main Conclusions: </b>Our findings highlight the critical role that spring snowpack, and consequently growing season soil moisture, plays in determining the persistence potential of high-elevation conifers. However, this role varies among species and thus may drive compositional changes as earlier snowmelt drives soil moisture declines across mountainous landscapes of the western United States.</p>
Crocus snowpack simulations across southwestern Canada and northwestern US
<p>The simulated snowpack properties (snow water equivalent, snow depth) were obtained from the detailed snowpack model Crocus (Vionnet et al., 2012; Lafaysse et al., 2017), implemented in the SVS land surface scheme version 2 (Soil Vegetation and Snow; Garnaud et al., 2019). Snowpack simulations were carried out from 1 September 2019 to 30 June 2020 over a grid covering southwestern Canada and northwestern US at 2.5-km grid spacing. The simulations were driven by short-meteorological forecast from the High-Resolution Deterministic Prediction System (HRDPS; Milbrandt et al., 2016) combined with precipitation estimates from the Canadian Precipitation Analysis (Fortin et al., 2018). Four precipitation-phase partitioning methods (PPMs) were used to derive the liquid and solid precipitation rates from the total precipitation available in the atmospheric forcing. More details about the simulations are and the PPMS given in the readme file included in the dataset.</p> <p>This dataset has been used in a study submitted to Water Resources Research (Vionnet et al, 2022).</p> <p><strong>References: </strong></p> <p>Fortin, V., Roy, G., Stadnyk, T., Koenig, K., Gasset, N., & Mahidjiba, A. (2018). Ten years of science based on the Canadian precipitation analysis: A CaPA system overview and literature review. <em>Atmosphere-Ocean</em>, <em>56</em>(3), 178-196.</p> <p>Garnaud, C., Bélair, S., Carrera, M. L., Derksen, C., Bilodeau, B., Abrahamowicz, M., ... & Vionnet, V. (2019). Quantifying snow mass mission concept trade-offs using an observing system simulation experiment. <em>Journal of Hydrometeorology</em>, <em>20</em>(1), 155-173.</p> <p>Milbrandt, J. A., Bélair, S., Faucher, M., Vallée, M., Carrera, M. L., & Glazer, A. (2016). The pan-Canadian high resolution (2.5 km) deterministic prediction system. <em>Weather and Forecasting</em>, <em>31</em>(6), 1791-1816.</p> <p>Lafaysse, M., Cluzet, B., Dumont, M., Lejeune, Y., Vionnet, V., & Morin, S. (2017). A multiphysical ensemble system of numerical snow modelling. <em>The Cryosphere</em>, <em>11</em>(3), 1173-1198.</p> <p>Vionnet, V., Brun, E., Morin, S., Boone, A., Faroux, S., Le Moigne, P., ... & Willemet, J. M. (2012). The detailed snowpack scheme Crocus and its implementation in SURFEX v7. 2. <em>Geoscientific Model Development</em>, <em>5</em>(3), 773-791.</p> <p>Vionnet, V., Verville, M., Fortin, V., Brugman, M., Abrahamowicz, M., Lemay, F., Thériault, J.M., Lafaysse M., and Milbrandt, J.A. : Snow level from post-processing of atmospheric model improves snowfall estimates and snowpack predictions in mountains, <em>Water Resources Research</em>, 2022, Accepted with minor revisions</p> <p> </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>
SNOWPACK LRT
<p>These are the model forcings and outputs that accompany the Water Resources Research manuscript 'Toward understanding direct absorption and grain size feedbacks by dust radiative forcing in snow with coupled snow physical and radiative transfer modeling' by S. McKenzie Skiles and Thomas H. Painter. </p> <p>Final dataset description and paper DOI will be updated upon paper publication.</p> <p>Dataset developer and point of contact is: Dr. S. McKenzie Skiles, Geography Department, University of Utah</p>
SNOWPACK simulations for Thwaites Cavity and Channel AMIGOS sites in West Antarctica
<p>Compressed zip file, containing the SNOWPACK model simulations used in the manuscript: Maclennan, M. L., Lenaerts, J. T. M., Shields, C. A., Hoffman, A. O., Wever, N., Thompson-Munson, M., Winters, A. C., Pettit, E. C., Scambos, T. A., and Wille, J. D.: <em>Climatology and Surface Impacts of Atmospheric Rivers on West Antarctica</em>, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2022-101, in review, 2022.</p> <p>MeteoIO and SNOWPACK are software published under the GNU LGPLv3 license by the WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland at <a href="https://gitlabext.wsl.ch/snow-models">https://gitlabext.wsl.ch/snow-models</a>. The repository used to develop the versions of MeteoIO and SNOWPACK used in this study can be accessed at <a href="https://github.com/snowpack-model/snowpack">https://github.com/snowpack-model/snowpack</a> (<a href="https://doi.org/10.5281/zenodo.3891845">doi: 10.5281/zenodo.3891845</a>) with the exact version corresponding to commit 149c586 (doi: <a href="https://doi.org/10.5281/zenodo.7331794">10.5281/zenodo.7331794</a>).</p> <p>The workflow consists of two phases. First, using MERRA-2 reanalysis as forcing, a spinup is performed for the MERRA-2 grid point closest to the AMIGOS weather stations Channel (CHA) and Cavity (CAV).</p> <p>To perform the spinup:</p> <ol> <li>navigate to the <code>./simulations_spinup/</code> directory</li> <li>edit the following line in the file <code>spinup.rc</code>, to modify the path where the time_shift_script can be found: <code>time_shift_script="/path/to/snowpack/Source/snowpack/tools/timeshift_sno_files.sh"</code> This script can be found in the SNOWPACK repository.</li> <li>Execute the two SNOWPACK spinup simulations specified below. Change the path to point to the SNOWPACK binary executable. The simulations are performed for the two sites, which use the same meteorological forcing, but have a different end date specified for the simulation (corresponding to the installation date of the AMIGOS weather station):</li> </ol> <pre><code class="language-bash">bash spinup.sh "/path/to/usr/bin/snowpack -c cfgfiles/THWAITES_CAV.ini -e 2020-01-10T01:00 > log/THWAITES_SPINUP.log 2>&1" startover=1 bash spinup.sh "/path/to/usr/bin/snowpack -c cfgfiles/THWAITES_CHA.ini -e 2020-01-16T01:00 > log/THWAITES_SPINUP.log 2>&1" startover=1</code></pre> <p>Brief overview of the files in the <code>simulations_spinup</code> directory:</p> <ul> <li><code>base.ini</code>: SNOWPACK settings shared by all simulations</li> <li><code>spinup.ini</code>: Specific settings used when the spinups are being performed</li> <li><code>final.ini</code>: Specific settings used when the final simulation is being performed, after the spinup has completed</li> <li><code>spinup.rc</code>: Specific spinup settings imported by the <code>spinup.sh</code> script</li> <li><code>spinup.sh</code>: bash script to perform spinups</li> <li><code>cfgfiles/THWAITES_CAV.ini</code>: Specific model settings for the Cavity site. This <code>*.ini</code> file is specified to SNOWPACK, and it directs to include other <code>*ini</code> files.</li> <li><code>cfgfiles/THWAITES_CHA.ini</code>: Specific model settings for the Channel site. This *.ini file is specified to SNOWPACK, and it directs to include other <code>*ini</code> files.</li> <li><code>current_snow/THWAITES.sno</code>: <code>*.sno</code> file used for the restarts within the spinup procedure</li> <li><code>log</code>: directory to store simulation logs. Note that in the commands above, we redirect <code>stdout</code> and <code>stderr</code> to files in this directory</li> <li><code>output</code>: directory to store model outputs. See SNOWPACK documentation for more details. <code>*.smet</code> and <code>*.pro</code> files can be visualized using <a href="https://niviz.org">NiViz</a></li> <li><code>smet</code>: directory to store the meteorological forcing data for the simulations</li> <li><code>snow_init/THWAITES.sno</code>: initial <code>*.sno</code> files used to start the spinup</li> </ul> <p><br> To perform the final simulations, covering the atmospheric river period:</p> <ol> <li>navigate to the <code>./simulations_final/</code> directory</li> <li>execute a snowpack simulation for each <code>*.ini</code> file in the <code>cfgfiles</code> directory:</li> </ol> <pre><code class="language-bash">for f in cfgfiles/*ini do /path/to/usr/bin/snowpack -c ${f} -e NOW done</code></pre> <p>The following simulations will be performed:</p> <table> <tbody> <tr> <td><strong>cfgfiles/*ini file:</strong></td> <td><strong>Label output files</strong></td> <td><strong>Mode</strong></td> <td><strong>AMIGOS AWS</strong></td> <td><strong>Parameters from AMIGOS AWS</strong></td> <td><strong>Reanalysis</strong></td> <td><strong>Parameters from reanalysis</strong></td> </tr> <tr> <td>THWAITES_CAV_ERA5.ini</td> <td>THWAITES_ERA5_HSDRIVEN_CAV_ERA5</td> <td>Snow height driven</td> <td>Cavity</td> <td>TA RH VW HS</td> <td>ERA5</td> <td>ILWR ISWR</td> </tr> <tr> <td>THWAITES_CAVHS_ERA5.ini</td> <td>THWAITES_ERA5_HSDRIVEN_CAVHS_ERA5</td> <td>Snow height driven</td> <td>Cavity</td> <td>HS</td> <td>ERA5</td> <td>TA RH VW ILWR ISWR</td> </tr> <tr> <td>THWAITES_CHA_ERA5.ini</td> <td>THWAITES_ERA5_HSDRIVEN_CHA_ERA5</td> <td>Snow height driven</td> <td>Channel</td> <td>TA RH VW HS</td> <td>ERA5</td> <td>ILWR ISWR</td> </tr> <tr> <td>THWAITES_CHAHS_ERA5.ini</td> <td>THWAITES_ERA5_HSDRIVEN_CHAHS_ERA5</td> <td>Snow height driven</td> <td>Channel</td> <td>HS</td> <td>ERA5</td> <td>TA RH VW ILWR ISWR</td> </tr> <tr> <td>THWAITES_REFCAV_ERA5.ini</td> <td>THWAITES_ERA5_PSUM_CAV_ERA5</td> <td>Precipitation driven </td> <td>Cavity</td> <td> </td> <td>ERA5</td> <td>PSUM TA RH VW ILWR ISWR</td> </tr> <tr> <td>THWAITES_REFCHA_ERA5.ini</td> <td>THWAITES_ERA5_PSUM_CHA_ERA5</td> <td>Precipitation driven </td> <td>Channel</td> <td> </td> <td>ERA5</td> <td>PSUM TA RH VW ILWR ISWR</td> </tr> <tr> <td>THWAITES_CAV_MERRA2.ini</td> <td>THWAITES_MERRA2_HSDRIVEN_CAV_MERRA2</td> <td>Snow height driven</td> <td>Cavity</td> <td>TA RH VW HS</td> <td>MERRA-2</td> <td>ILWR ISWR</td> </tr> <tr> <td>THWAITES_CAVHS_MERRA2.ini</td> <td>THWAITES_MERRA2_HSDRIVEN_CAVHS_MERRA2</td> <td>Snow height driven</td> <td>Cavity</td> <td>HS</td> <td>MERRA-2</td> <td>TA RH VW ILWR ISWR</td> </tr> <tr> <td>THWAITES_CHA_MERRA2.ini</td> <td>THWAITES_MERRA2_HSDRIVEN_CHA_MERRA2</td> <td>Snow height driven</td> <td>Channel</td> <td>TA RH VW HS</td> <td>MERRA-2</td> <td>ILWR ISWR</td> </tr> <tr> <td>THWAITES_CHAHS_MERRA2.ini</td> <td>THWAITES_MERRA2_HSDRIVEN_CHAHS_MERRA2</td> <td>Snow height driven</td> <td>Channel</td> <td>HS</td> <td>MERRA-2</td> <td>TA RH VW ILWR ISWR</td> </tr> <tr> <td>THWAITES_REFCHA_MERRA2.ini</td> <td>THWAITES_MERRA2_PSUM_CAV_MERRA2</td> <td>Precipitation driven </td> <td>Cavity</td> <td> </td> <td>MERRA-2</td> <td>PSUM TA RH VW ILWR ISWR</td> </tr> <tr> <td>THWAITES_REFCAV_MERRA2.ini</td> <td>THWAITES_MERRA2_PSUM_CHA_MERRA2</td> <td>Precipitation driven </td> <td>Channel</td> <td> </td> <td>MERRA-2</td> <td>PSUM TA RH VW ILWR ISWR</td> </tr> </tbody> </table> <p> </p> <p>Brief overview of the files in the <code>simulations_final</code> directory:</p> <ul> <li><code>base.ini</code>: SNOWPACK settings shared by all simulations (identical to <code>base.ini</code> in simulations_spinup directory</li> <li><code>cfgfiles</code>: Specific model settings for each simulation as listed in the table above.</li> <li><code>current_snow</code>: The initial *.sno file containing the initial firn conditions</li> <li><code>final.ini</code>: Settings used by all simulations in this directory</li> <li><code>output</code>: directory to store model outputs. See SNOWPACK documentation for more details. <code>*.smet</code> and <code>*.pro</code> files can be visualized using <a href="https://niviz.org">NiViz</a></li> <li><code>prepare_snowpack.sh</code>: bash script setting up the final simulations after the simulations in simulations_spinup completed. Among others, the code to mark the installation reference layers for the snow depth measurements is included in this script.</li> <li><code>smet</code>: ERA5 and MERRA-2 forcing data, as well as snow height for Cavity and Channel (<code>cavity_hs.smet</code> and <code>channel_hs.smet</code>, respectively) as well as the other meteorological parameters (<code>cavity_meteo.smet</code> and <code>channel_meteo.smet</code>, respectively).</li> </ul> <p> </p> <p><br> Note:<br> Please refer to <a href="https://doi.org/10.15784/601552">doi: 10.15784/601552</a> and <a href="https://doi.org/10.15784/601549">doi: 10.15784/601549</a> for the AMIGOS weather station data</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>
Let it snow? Spring snowpack and microsite characterize the regeneration niche of high-elevation pines
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Winter inputs buffer streamflow sensitivity to snowpack losses in the Salt River Watershed in the Lower Colorado River Basin
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Data from: Combining ground‐penetrating radar with terrestrial LiDAR scanning to estimate the spatial distribution of liquid water content in seasonal snowpacks
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Snowpack, precipitation, and temperature measurements at the Central Sierra Snow Laboratory for water years 1971 to 2025
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