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966 results for “Snow”
Seefeld Cold-Air Pool Experiment (SEECAP): WRF Simulation Output without snow cover January 16 2020 0000 UTC to January 17 2020 1200 UTC
<p>The Seefeld Cold-Air Pool Experiment (SEECAP) focused on the cross-country skiing area Olympiaregion Seefeld and in particular the topographic setting in the Nordic ski arena which favors the formation of cold-air pools and took place between December 2019 and March 2020. The measurement data are described in Rudolph (2022) and Rauchöcker et al. (2024d) and meteorological measurement data associated with SEECAP are published in Rauchöcker et al. (2024c). This upload contains WRF simulation output data for the night between January 16 and January 17 2020 without snow cover and the plotting routines to reproduce the figures in Rauchöcker et al. (2024d). The night between January 16 and January 17 2020 initially featured an ideal cold-air pool formation followed by a interuption by a wind disturbance around midnight. Simulation output for the same night, but with snow cover is also available (Rauchöcker et al., 2024a). The temperature evolution of the measurements agreed much better with the simulation with snow cover and otherwise the same model setting compared to the simulation without snow cover (Rauchöcker et al. 2024d). Also available in a different dataset is output from a simulation with snow cover for the night between January 12 and January 13 2020 (Rauchöcker et al., 2024b), which featured an undisturbed cold-air pool for almost the entire night. This case was considered to feature in Rauchöcker et al. (2024d), but a different case was chosen because some measurement data was not available during this period.</p> <h3><strong>WRF Simulation Output</strong></h3> <p>This Dataset includes data generated with WRFlux v1.4.1 (Göbel et al., 2022), a fork of the Weather Research and Forecasting model WRF (Skamarock et al. 2021). WRFlux allows to calculate the contribution of different processes to the potential temperature tendency at each grid point. The data published here is from the innermost simulation domain with 40m horizontal resolution and 10m vertical resolution close to the surface. The simulations were initialized at 00:00 UTC January 16 2020 and run until 12:00 UTC January 17 2020.</p> <p>Three different simulations were performed: two simulations with modified snow cover as described in Rauchöcker (2022), one each with the MYNN 2.5-order and the SMS-3DTKE PBL parameterizations (a scheme that blends a PBL scheme and a LES subgrid parameteriztion in the greyzone of turbulence), and one without snow cover with the MYNN 2.5-order PBL parameterization. Otherwise the simulations were identical. This dataset includes the simulation without snow cover. A detailed description of the model setup can be found in Rauchöcker et al (2024d) and in the file <em>namelist.input</em> that was used to generate the simulation results.</p> <p>Standard WRF output can be found in <em>wrfout_40m_jan16_nosnow</em>. The mean wind speed components, which were necessary to rotate the tendencies in a coordinate system that is aligned with the valley orientation, are contained in <em>windout_40m_jan16_nosnow</em>. These variables were contained in the unprocessed<em> </em>output files produced by WRFlux; the full files were unfortunately too large to be included here. The postprocessed tendencies are stored in <em>tend_40m_jan16_nosnow.nc</em>.</p>
Snow Water Equivalent Dataset for the South Fork of the San Joaquin River (2018/2021) and Senales (2019/2021)
<p>The dataset is related to: Premier, V., Marin, C., Bertoldi, G., Barella, R., Notarnicola, C., & Bruzzone, L. (2022). Exploring the Use of Multi-source High-Resolution Satellite Data for Snow Water Equivalent Reconstruction over Mountainous Catchments. <em>The Cryosphere Discussions</em>, 1-42.</p> <p>It contains three hydrological seasons - from 1st of October 2018 to 30th of September 2021 - of snow water equivalent (SWE) for the South Fork of the San Joaquin river in California (USA) and two hydrological seasons - from 1st of October 2019 to 30th of September 2021 - for the Schnals/Senales basin in South Tyrol (Italy). The product is daily and with a spatial resolution of 25 m. SWE values are in mm. Snow cover area (SCA) can be derived from the same by thresholding pixel containing SWE greater than 0 mm. Further information about the reference system is contained in the attributes of the netcdf files. Please, contact the authors for further questions. Information about the methodology and the input data for producing these time series is contained in the related article.</p>
Marcell Experimental Forest biweekly snow depth, frost depth, and snow water equivalent, 1962 - ongoing
This data table contains snowpack and frost data measured at the Marcell Experimental Forest from 1962–ongoing. The data came from five peatland/upland forest watersheds instrumented for hydrologic monitoring. Frost thickness and snowpack (snow water content, snowpack depth) are measured at 10 snowcourses that encompass three cover types (conifer, deciduous, open). The Marcell Experimental Forest in Itasca County, Minnesota, is operated and maintained by the USDA Forest Service, Northern Research Station, and was formally established in 1962 to study the ecology and hydrology of peatlands.
Presence/absence of new snow-fall scored from time-lapse photography collected near Toolik Field Station, Alaska, summers 2012-2016
This data set describes the presence/absence of new snowfall approximated daily using time -lapse photography images near Toolik Field Station during summers from 2012 to 2016 under National Science Foundation (NSF) Office of Polar Programs ARC 0908444 (to Laura Gough), ARC 0908602 (to Natalie Boelman), and ARC 0909133 (to John Wingfield). Additional cameras funded by other grants were also used for scoring including multiple Toolik EDC timelapse images taken at Toolik, Atigun Ridge, and Imnavait. Additional data were scored from time lapse photography taken by the Deegan and Urban labs (Office of Polar Programs #0902153 and #1417664). All data are associated with publication DOI: 10.1111/jav.01712.
Bonanza Creek LTER: Hourly Snow Depth Measurements from 1988 to Present in the Bonanza Creek Experimental Forest near Fairbanks, Alaska
Snow depth is measured hourly at both the LTER1 and LTER2 research stations within Bonanza Creek Experimental Forest using a Campbell Scientific data logger and a SR50 sonic snow depth sensor.
Bonanza Creek LTER: Hourly Snow Pillow Measurements from 1988 to Present in the Bonanza Creek Experimental Forest near Fairbanks, Alaska
The snow pillow records the hourly water content of the snowpack (snow water equivalent) at the LTER1 site within the Bonanza Creek Experimental Forest during the winter months.
Bonanza Creek LTER: Hourly Snow Depth Measurements from 2006 to Present in the Caribou-Poker Creeks Research Watershed near Fairbanks, Alaska
Hourly snow depth measured in meters from the CRREL and CARSNOW climate data stations within CPCRW using a Campbell Scientific data logger and a SR50 sonic snow depth sensor.
Climate Change Across Seasons Experiment (CCASE) at the Hubbard Brook Experimental Forest: Snow and Frost
This dataset contains snow depth and frost depth measurements from the Climate Change Across Seasons Experiment (CCASE) at the Hubbard Brook Experimental Forest. Samples are collected weekly throughout the winter months. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Lake Mendota water temperature secchi depth snow depth ice thickness and meterological conditions 1894 - 2007
Data for water temperature at different depth and different frequencies assembled from various sources by Dale Roberson. A table with additional parameters collected at the same time is also provided for dates when available. These parameters are weather observations, secchi depth, snow and ice depths.
Snow depth sensor measurement data for Upper Sub Alpine site, 2010 - 2015.
Effects of infrared heaters on snow accumulation, snowmelt, and snow–atmosphere energy exchange were examined at Niwot Ridge, Colorado (CO). These .zip data files contains hourly snow depth measurements collected using Judd snow depth sensors for water year 2010-2015 (1 October 2009 – 30 September 2015) at the Upper Sub Alpine site, located just southeast of the Tundra Lab, below treeline in the Niwot Ridge Long-Term Ecological Research (NWTLTER) project area. The file contains both level 0 and level 1 (see details in “Process Description” below) hourly snow depth data measured in centimeters, and an accompanying metadata file.
Spatial distribution of snow depth for the Green Lakes Valley, 1997 - 2019
Climate warming represents an abiotic driver for change in alpine ecosystems, potentially altering the seasonal snowpack and thus water availability into the surrounding landscape. Future changes in snow accumulation and snowmelt distribution may have profound impacts on the flora and fauna of alpine ecosystems. In this regard, recent research has leveraged multi-year estimates of the spatial distribution of snow water equivalent (SWE) toward understanding alpine ecosystem function. The purpose of this project is to investigate the spatial variability of maximum snow depth at Niwot Ridge on an inter-annual basis.
Annual snow survey, Green Lakes Valley, Niwot Ridge, Colorado, 2013 - ongoing.
Yearly snow surveys were conducted in the Green Lakes Valley in the City of Boulder Watershed at the estimated peak of snowpack in late spring. Over a period of several days, surveying teams (1 to several people) traversed valley slopes measuring snow depth with avalanche probes. Locations of each depth measurement were recorded as waypoints in Garmin hand-held GPS units. Snow depths were recorded on standardized field sheets along with dates, recorder names, waypoint numbers, and comments.
AIRBORNE SPECTROMETER MEASUREMENTS FMOM BOREAL SNOW-COVERED LANDSCAPE
<p>The dataset contains 10 meter resolution reflectance data from boreal snow-covered landscape. The purpose of the airborne measurements was to investigate the effect of forest canopy on optical remote sensing signals for snow-covered surfaces. The hyperspectral airborne data was acquired with an AisaDUAL imaging spectrometer on March 18 and on March 21, 2010 in Sodankylä, Finland. The image swath was 240 meters and flight lines were several kilometers long. The original spatial resolution of the data is 80 cm x 80 cm, but it was resampled to pixel size of 10 m x 10 m. All measurements were carried out in non-cloudy conditions (0/8 to 2/8 cloud cover). On 18 March, the tree canopy was snow-free and snow on the ground was several days old, while on 21 March, the tree canopy was snow-covered and snow on ground was fresh. The data contains mosaics of the flight lines for the bands 555 nm, 645 nm, 858.5 nm and 1640 nm for both days 18 March 2010 and 21 March 2010.</p>
AIRBORNE SPECTROMETER MEASUREMENTS FROM BOREAL AND TUNDRA SITE DURING SPRING SNOW MELT
<p>The dataset contains 10 meter resolution reflectance data from boreal and tundra sites during spring snow melt. The purpose of the airborne measurements was to investigate the effect of forest canopy and snow melting on optical remote sensing signals at the very end of melting period. The hyperspectral airborne data was acquired with an AisaDUAL imaging spectrometer on 5 May 2011 in Sodankylä and in Saariselkä, Finland. Saariselkä is a fell region and partly represents open tundra. The image swath was 240 meters and flight lines were several kilometers long. The original spatial resolution of the data is 80 cm x 80 cm, but it was resampled to pixel size of 10 m x 10 m. Snow depth was between 0 cm and 30 cm at the Sodankylä site and between 0 cm and 60 cm at the Saariselkä site implying that the spring melt was clearly more advanced in Sodankylä. Additionally, more snow-free pixels were found at Sodankylä than Saariselkä. During the measurements the sky was cloudless in Sodankylä (cloud cover 0/8) and cloudy (cloud cover 7/8) in Saariselkä. The data contains mosaics of the flight lines for the bands 555 nm, 645 nm, 858.5 nm and 1640 nm for both study sites.</p>
Snow Index
<p>Snow indexes are indicators of the snow present in the alps mountains. The indexes are calculated with an image processing pipeline.<br> <br> The dataset is a CSV file providing: snow index, snow percentage, latitude, longitude and date.</p> <p> </p> <p>THIS WORK IS SHARED UNDER THE FOLLOWING LICENSE CREATIVE COMMONS ATTRIBUTION-SHAREALIKE 4.0 INTERNATIONAL (CC BY-SA 4.0) <a href="https://creativecommons.org/licenses/by-sa/4.0/">https://creativecommons.org/licenses/by-sa/4.0/</a></p>
Radar measurements for the article "Dynamic differential reflectivity calibration using vertical profiles in rain and snow"
<p><strong>Dataset documentation</strong></p> <p>The archives in hdf5 format provided at this link contain the datasets used in the manuscript <em>Dynamic differential reflectivity calibration using vertical profiles in rain and snow</em>, submitted to <em>Remote Sensing</em> (MDPI) by Alfonso Ferrone and Alexis Berne in 2020.</p> <p> </p> <p><strong>File content</strong></p> <p>Each file is structured as a table, with each column referring to a specific variable and each row containing a different realization (in space or time). The set of available variables is campaign dependent, and the possibilities are:</p> <ul> <li> <p><strong>idx</strong>, and integer index that starts at 1 for the first scan of the dataset and increases by 1 for every successive scan;</p> </li> <li> <p><strong>t</strong>, the timestamp of the scan, in seconds since seconds since Jan 01, 1970;</p> </li> <li> <p><strong>r</strong> or <strong>rg</strong> (depending on the file), the distance from the radar in meters;</p> </li> <li> <p><strong>az</strong>, the azimuth angle in degrees;</p> </li> <li> <p><strong>el</strong>, the elevation angle in degrees;</p> </li> <li> <p><strong>zdr</strong>, the uncalibrated differential reflectivity, in dB;</p> </li> <li> <p><strong>zh</strong>, the horizontal reflectivity, in dBZ;</p> </li> <li> <p><strong>rhovh</strong> or <strong>rho</strong>, the co-polar correlation coefficient, unitless;</p> </li> <li> <p><strong>snr_h</strong> or <strong>snr</strong>, the signal to noise ratio for the horizontal channel in dB;</p> </li> <li> <p><strong>snrv</strong>, the signal to noise ratio for the vertical channel, in dB,</p> </li> <li> <p><strong>ngates</strong>, the number of unique range gates.</p> </li> </ul> <p>For the comparison of the data collected by MXPol and DX50 during the PAYERNE campaign, two auxiliary variables were added to the archives:</p> <ul> <li> <p><strong>x</strong> the horizontal distance from the current radar, computed on a line passing through the location of two radars;</p> </li> <li> <p><strong>z</strong> the vertical distance from the current radar.</p> </li> </ul> <p> </p> <p><strong>Usage</strong></p> <p>The dataset are provided in the the Hierarchical Data Format version 5 (HDF5), an open source file format, supported by several programming language.</p> <p>They archives were created using the <em>vaex</em> library for Python 3:</p> <p>https://github.com/vaexio/vaex</p> <p>The function <em>vaex.open</em> from the same library can be used for accessing the archives and converting them to <em>vaex.DataFrame</em>.</p> <p> </p> <p><strong>Campaign-specific information</strong></p> <p>Some of the parameters associated to the variables included in the archives may change depending on the campaign. The following subsection provide a summary of these information.</p> <p> </p> <p><strong>dataframe_HYMEX_2013_from_20130907-040344_to_20131105-175944.hdf5</strong></p> <p>Contains PPI scans at 90° elevation for the HYMEX campaign.</p> <p>Location information:</p> <ul> <li> <p>Latitude: 44.61° N</p> </li> <li> <p>Longitude: 4.55° E</p> </li> <li> <p>Altitude: 604 m.a.s.l.</p> </li> </ul> <p>Technical information:</p> <ul> <li> <p>Radar Frequency: 9.41 GHz</p> </li> <li> <p>Theoretical 3 dB angular beamwidth of the antenna: 1.45°</p> </li> </ul> <p>Scan setup:</p> <ul> <li> <p>Range resolution: 75 m</p> </li> <li> <p>Range to the first gate: 204.3 m</p> </li> </ul> <p>Processsing parameters:</p> <ul> <li> <p>Clutter filtering: OFF</p> </li> <li> <p>Minimum rhohv: 0.6</p> </li> <li> <p>Attenuation correction: Z-PHI method</p> </li> <li> <p>Note on reflectivity calibration: The original manufacturer calibration constant was 7.56 dBZ. The value used here derives from comparison with disdrometers during the HYMEX campaign.</p> </li> </ul> <p> </p> <p><strong>dataframe_PAYERNE_2014_from_20140321-160016_to_20140519-085808.hdf5</strong></p> <p>Contains PPI scans at 90° elevation performed by MXPol during the PAYERNE campaign.</p> <p>Location information:</p> <ul> <li> <p>Latitude: 46.81° N</p> </li> <li> <p>Longitude: 6.94° E</p> </li> <li> <p>Altitude: 496 m.a.s.l.</p> </li> </ul> <p>Technical information:</p> <ul> <li> <p>Radar Frequency: 9.41 GHz</p> </li> <li> <p>Theoretical 3 dB angular beamwidth of the antenna: 1.45°</p> </li> </ul> <p>Scan setup:</p> <ul> <li> <p>Range resolution: 75 m</p> </li> <li> <p>Range to the first gate: 204.3 m</p> </li> </ul> <p>Processsing parameters:</p> <ul> <li> <p>Clutter filtering: OFF</p> </li> <li> <p>Minimum rhohv: 0.6</p> </li> <li> <p>Attenuation correction: None</p> </li> </ul> <p> </p> <p><strong>dataframe_DAVOS_2014_from_20140704-090224_to_20141231-105720.hdf5</strong></p> <p>Contains PPI scans at 90° elevation for the DAVOS campaign.</p> <p>Location information:</p> <ul> <li> <p>Latitude: 46.82° N</p> </li> <li> <p>Longitude: 9.82° E</p> </li> <li> <p>Altitude: 2220 m.a.s.l.</p> </li> </ul> <p>Technical information:</p> <ul> <li> <p>Radar Frequency: 9.41 GHz</p> </li> <li> <p>Theoretical 3 dB angular beamwidth of the antenna: 1.45°</p> </li> </ul> <p>Scan setup:</p> <ul> <li> <p>Range resolution: 75 m</p> </li> <li> <p>Range to the first gate: 204.3 m</p> </li> </ul> <p>Processsing parameters:</p> <ul> <li> <p>Clutter filtering: OFF</p> </li> <li> <p>Minimum rhohv: 0.6</p> </li> <li> <p>Attenuation correction: None</p> </li> </ul> <p> </p> <p><strong>dataframe_APRES3_from_20151207-123944_to_20160129-125856.hdf5</strong></p> <p>Contains PPI scans at 90° elevation for the APRES3 campaign.</p> <p>Location information:</p> <ul> <li> <p>Latitude: 66.66 S</p> </li> <li> <p>Longitude: 140.00 E</p> </li> <li> <p>Altitude: 40 m.a.s.l.</p> </li> </ul> <p>Technical information:</p> <ul> <li> <p>Radar Frequency: 9.41 GHz</p> </li> <li> <p>Theoretical 3 dB angular beamwidth of the antenna: 1.45°</p> </li> </ul> <p>Scan setup:</p> <ul> <li> <p>Range resolution: 75 m</p> </li> <li> <p>Range to the first gate: 354.3 m</p> </li> </ul> <p>Processsing parameters:</p> <ul> <li> <p>Clutter filtering: OFF</p> </li> <li> <p>Minimum rhohv: 0.6</p> </li> <li> <p>Attenuation correction: None</p> </li> </ul> <p> </p> <p><strong>dataframe_VALAIS_2016_from_20161104-154312_to_20170306-195912.hdf5</strong></p> <p>Contains PPI scans at 90° elevation for the VALAIS campaign.</p> <p>Location information:</p> <ul> <li> <p>Latitude: 46.12 N</p> </li> <li> <p>Longitude: 7.10 E</p> </li> <li> <p>Altitude: 460 m.a.s.l.</p> </li> </ul> <p>Technical information:</p> <ul> <li> <p>Radar Frequency: 9.41 GHz</p> </li> <li> <p>Theoretical 3 dB angular beamwidth of the antenna: 1.27°</p> </li> </ul> <p>Scan setup:</p> <ul> <li> <p>Range resolution: 30 m</p> </li> <li> <p>Range to the first gate: 226.95 m</p> </li> </ul> <p>Processsing parameters:</p> <ul> <li> <p>Clutter filtering: OFF</p> </li> <li> <p>Minimum rhohv: 0.6</p> </li> <li> <p>Attenuation correction: None</p> </li> </ul> <p> </p> <p><strong>dataframe_DX50_from_20140501-020000_to_20140524-015752.hdf5</strong></p> <p>Contains PPI scans at 90° elevation performed by DX50 during the PAYERNE campaign.</p> <p>Location information:</p> <ul> <li> <p>Latitude: 46.84° N</p> </li> <li> <p>Longitude: 6.92° E</p> </li> <li> <p>Altitude: 450 m.a.s.l.</p> </li> </ul> <p>Technical information:</p> <ul> <li> <p>Radar Frequency: 9.459 GHz</p> </li> <li> <p>Theoretical 3 dB angular beamwidth of the antenna: 1.273°</p> </li> </ul> <p>Scan setup:</p> <ul> <li> <p>Range resolution: 75 m</p> </li> <li> <p>Range to the first gate: 0.0 m</p> </li> </ul> <p>Processsing parameters:</p> <ul> <li> <p>Clutter filtering: OFF</p> </li> <li> <p>Minimum rhohv: 0.0</p> </li> <li> <p>Attenuation correction: None</p> </li> </ul> <p> </p> <p><strong>dataframe_DX50_RHI_201405.hdf5</strong></p> <p>Contains RHI scans from DX50 the PAYERNE campaign.</p> <p>The remaining information equal to the ones listed for <em>dataframe_DX50_from_20140501-020000_to_20140524-015752.hdf5</em>.</p> <p> </p> <p><strong>dataframe_MXPol_RHI_201405.hdf5</strong></p> <p>Contains RHI scans from MXPol the PAYERNE campaign.</p> <p>The remaining information is equal to the ones listed for <em>dataframe_PAYERNE_2014_from_20140321-160016_to_20140519-085808.hdf5</em>.</p>
Snow depth, snow water equivalent, ice thickness in Fuglebekken and Revdalen catchments collected in the SnowPilot campaign in Spring 2022
<p>File SnowPilot_snowdepth_along_the_GPR_profile_2022 contains snow depth measurements taken along the GPR profile performed during the SIOS SnowPilot campaign in Spring 2022. File SnowPilot_snowdepth_swe_2022 contains depth, snow water equivalent and basal ice thickness. Snowpits were dug on GPR profile crossings in the Fuglebekken and Revdalen catchments in the Hornsund fiord, Spitsbergen catchment. Snow density was measured with an IG PAS snow tube, and snow depth and basal ice (ice forming on the ground surface) thickness were measured with an avalanche probe. Point locations measured. with handheld GPR reciever.</p>
Crocus-ERA-Interim daily snow product over the Northern Hemisphere at 0.5° resolution
<p>The Crocus-ERA-Interim daily snow product is derived from the complex snow scheme Crocus coupled to the ISBA (Interactions between Soil–Biosphere–Atmosphere) land surface model (<a href="http://dx.doi.org/10.1175/JHM-D-12-012.1">Brun et al., 2013</a>) and embedded into the SURFEX numerical platform (<a href="https://www.umr-cnrm.fr/surfex/">https://www.umr-cnrm.fr/surfex/</a>). The model is driven by a meteorological forcing (temperature, precipitation, humidity, winds, etc) derived from the ERA-Interim global atmospheric reanalysis (<a href="https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-interim">https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-interim</a>). This product only concerns open field snowpack, i.e. only low vegetation is modeled (no forest). It covers the entire Northern Hemisphere at 0.5° resolution over the 1979-07-01 to 2019-06-30 period. Snow depth and snow water equivalent (i.e. the snow mass) are available at a dailly frequency. The simulated snow depth, snow water equivalent, and density over open fields were validated against local observations from over 1000 monitoring sites in Northern Eurasian, available either once a day or three times per month (<a href="http://dx.doi.org/10.1175/JHM-D-12-012.1">Brun et al., 2013</a>). This product was used by the NOAA <a href="https://arctic.noaa.gov/report-card/">Artic Report Card</a> from 2017 to 2020 for the annual survey of the Terrestrial Snow Cover anomalies over the Northern Hemisphere. This product was also used in several research studies (e.g. <a href="https://doi.org/10.1175/JCLI-D-15-0229.1">Mudryk et al., 2015</a>; <a href="https://doi.org/10.5194/tc-14-1579-2020">Mortimer et al., 2020</a>; <a href="https://doi.org/10.5194/tc-17-5007-2023">Kouki et al., 2023</a>). The successor of this product based on ERA5 at 0.25° resolution over 1950 to 2023 can be found in <a href="https://doi.org/10.5281/zenodo.10943718">Decharme (2024b)</a>.</p>
Crocus-ERA5 daily snow product over the Northern Hemisphere at 0.25° resolution
<p>The Crocus-ERA5 daily snow product is derived from the complex snow scheme Crocus coupled to the ISBA (Interactions between Soil–Biosphere–Atmosphere) land surface model (<a href="http://dx.doi.org/10.1175/JHM-D-12-012.1">Brun et al., 2013</a>) and embedded into the SURFEX numerical platform (<a href="https://www.umr-cnrm.fr/surfex/">https://www.umr-cnrm.fr/surfex/</a>). The model is driven by a meteorological forcing (temperature, precipitation, humidity, winds, etc) derived from the ERA5 global atmospheric reanalysis (<a href="https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5">https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5</a>). This product only concerns open field snowpack, i.e. only low vegetation is modeled (no forest). It covers the entire Northern Hemisphere at 0.25° resolution over the 1950-07-01 to 2023-06-30 period. All snow characteristics (see later) are available at a dailly frequency. This product is the successor of the Crocus-ERA-Interim daily snow product (<a href="../records/10911538">Decharme, 2024</a>). It is used by the NOAA <a href="https://arctic.noaa.gov/report-card/">Artic Report Card</a> from 2021 to present for the annual survey of the Terrestrial Snow Cover anomalies over the Northern Hemisphere. An evaluation of the snow water equivalent product can be found in <a href="https://egusphere.copernicus.org/preprints/2024/egusphere-2023-3014/">Mudryk et al. (2024)</a> where it is compared to observations and to about twenty alternative datasets.</p>
Downscaled 8km March Snow Water Equivalent Estimates for the Western US, 1901-2010
<p>Downscaled estimates of March mean snow water equivalent at approximately 8km resolution across the western United States for the years 1901-2010. Data downscaled from the CERA-20c reanalysis using UA-SWE daily observations. Downscaled data using both the CERA-20c ensemble mean as well as each individual ensemble member as predictors are included. Units are in millimeters of snow water equivalent.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.