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61 results for “Snow modeling”
Data from: Temporal dynamics of snowmelt nutrient release from snow–plant residue mixtures: an experimental analysis and mathematical model development
Open the record for dataset details and reuse information.
SnowClim v1.0: High-resolution snow model and data for the western United States
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Supporting model data for paper: Measuring the impact of a new snow model using surface energy budget process relationships
<p>Supporting model data for paper: Measuring the impact of a new snow model using surface energy budget process relationships which has been submitted to the Journal of Advances in Modelling Earth Systems: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020MS002144</p> <p>The experiment id h3hh corresponds to simulations with the ECMWF IFS with a single layer snow model. h3eg corresponds to the experimental 5-layer snow model.</p> <p>The timeseries are made by concatenating hourly data from day2 of forecasts initialised at 00UTC each day between Dec 1st 2013 and 1 June 2014.</p>
Dataset for Snow Transport Modeling in the Northern Wind River Range
<p>This dataset includes materials related to research on snow transport distances in alpine terrain. The project used a simple process-based snow model (modified version of DHSVM snow sub-model) and a differentiable network to constrain the minimum transport distances and source areas required to explain deep snow accumulation zones assuming a relatively smoother snowfall pattern.</p> <p>This dataset includes:</p> <ol> <li>Scripts for processing workflow and figure generation</li> <li>Spatial inputs and outputs from neural network for each of 4 tested snowfall patterns plus 3 sublimation tests</li> <li>Differentiable modeling scripts</li> </ol> <p>Additional data included in prior version of archive:</p> <ol> <li>DHSVM source code, inputs, config files, etc.</li> <li>WindNinja simulation data</li> <li>Additional spatial data (watershed boundaries, streams, etc.)</li> </ol>
Data for "Improvements in wintertime surface temperature variability in the Community Earth System Model version 2 (CESM2) related to the representation of snow density"
<p>This dataset contains all the postprocessed data required to reproduce the figures in the publication Simpson et al (2022) "Improvements in wintertime surface temperature variability in the Community Earth System Model version 2 (CESM2) related to the representation of snow density", in the Journal of Advances in Modelling the Earth System.</p>
Improved snow darkening coefficient for large-scale albedo modelling with Crocus
<p><strong>Description</strong></p> <p>Light-absorbing particles (LAPs) deposited at the snow surface significantly reduce its albedo and strongly affect the snow melt dynamics. The explicit simulation of these effects with advanced snow radiative transfer models can be associated with a large computational cost. Consequently, many albedo schemes used in snowpack models still rely on empirical parameterizations that do not account for the spatial variability of LAP deposition. In Gaillard et al. (2024), a new strategy of intermediate complexity that includes the effects of spatially variable LAP deposition on snow albedo was tested with the snowpack model Crocus. It relies on an optimization of the snow darkening coefficient that controls the evolution of snow albedo in the visible range. A global dataset of LAP-informed and spatially variable values of the snow darkening coefficient was constructed. The revised snow darkening coefficient improved snow albedo simulations at the ten sites considered in the study by 10%, with the largest improvements found in the Arctic (more than 25%). The uncertainties in the values of the snow darkening coefficient resulting from the inter-annual variability of LAP deposition on snow were also computed.</p> <p>The data are distributed in a NetCDF file (gamma<em>_tot_publish_v2.nc</em>). More details about the dataset and the file format are given in the file <em>readme_gamma_v2.pdf</em>. </p>
Figures and tables with datasets in "A wind-induced snow redistribution study considering contact based on a bidirectional coupled model of wind and discrete snow particles"
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IT-SNOW: a snow reanalysis for Italy blending modeling, in-situ data, and satellite observations
<p>IT-SNOW is a serially complete and multi-year snow reanalysis for Italy. The dataset includes daily maps of Snow Water Equivalent (SWE), snow depth (HS), bulk-snow density (RhoS), and liquid water content (Theta_W). </p> <p>Data are organized in monthly netCDF files, each providing time and lat/lon information for georeference. Units are as follows: HS is in cm, SWE is in mm w.e., RhoS is in kg/m3, and Theta_W is in %. Note that maps are instantaneous snapshots at 11AM UTC, here assumed as representative values for the day. </p> <p>As the output of an operational chain employed in real-world civil-protection applications (S3M Italy), IT-SNOW ingests input data from thousands of automatic weather stations, snow-covered-area maps from Sentinel 2, MODIS, and H-SAF products, and maps of snow depth from the spazialization of 1000+ on-the-ground snow-depth sensors. Additional information are available in the following paper submitted to Earth System Science Data: </p> <p>"IT-SNOW: a snow reanalysis for Italy blending modeling, in-situ data, and satellite observations (2009-2021)", Francesco Avanzi et al., 2022. </p> <p>The initial time span of data is September 1, 2010 to August 31, 2021, with future updates envisaged on an annual basis (see updates below).</p> <p><strong>UPDATES</strong></p> <ul> <li>September 29, 2025: released v5 with the complete 2025 water year (September 2024 - August 2025).</li> <li>November 12, 2024: released v4 with the complete 2024 water year (September 2023 - August 2024).</li> <li>September 02, 2024: released v3.1 with the complete 2023 water year (September 2022 - August 2023) AND all previous water years (which were inadvertently NOT carried over while creating v3).</li> <li>September 02, 2024: released v3 with the complete 2023 water year (September 2022 - August 2023).</li> <li>December 20, 2023: released v2 with the complete 2022 water year (September 2021 - August 2022).</li> </ul> <p>LICENSE INFORMATION</p> <p>IT-SNOW is distributed under a CC BY-NC 4.0 license. you are free to: </p> <p>1. Share — copy and redistribute the material in any medium or format; <br>2. Adapt — remix, transform, and build upon the material;</p> <p>under the following terms: </p> <p>a. Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.<br>b. NonCommercial — You may not use the material for commercial purposes.</p> <p><br>DATA ARE PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THESE DATA, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.</p> <p>For details about the CC BY-NC 4.0 license, see: https://creativecommons.org/licenses/by-nc/4.0/deed.en</p>
Large-eddy simulation model data for article Study of surface layer characteristics in the presence of suspended snow particles using observational data and large-eddy simulation
<p>Large-eddy simulation model data [8. Mortikov E.V., Glazunov A.V., <br> Lykosov V.N. Numerical study of plane Couette flow: <br> turbulence statistics and the structure of pressure-strain correlations <br> // Russ. J. Numer. Analysis Math. Model. 2019. V. 34. № 2. P. 119–132.]<br> The setup of experiments was based<br> on the GABLS-1. The height, width and length of the domain was 4000 m<br> with spatial resolution of 11.7m. U18, U16 - geostrophic wind 18 and 16 m/s,<br> CR - cooling rate 0K/h, 1K/h, 2K/h.<br> Two series of experiments were performed: “NS“ and “SS“.<br> In the experiment “NS“ the surface layer was described according to the<br> Monin-Obukhov similarity theory. The experiments “SS“<br> utilized the parameterization, which takes into account the effect<br> snow particles.</p>
Snow-Off Digital Terrain Model (DTM) from 2010 LiDAR for the Boulder Creek Critical Zone Observatory (CZO), Colorado
This 1m Digital Terrain Model (DTM) 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 layer, the Digital Surface Model (DSM), is a first-stop elevation layer. Accessory layers consist of index map layers for point cloud tiles and flight lines, each with detailed attribute information such as acquisition date and tile file name. The DTM is available through an unrestricted public license. Other LiDAR DSMs, DTMs, and point cloud data ava
Daily 4 km Gridded SWE and Snow Depth from Assimilated In-Situ and Modeled Data over the Conterminous US, Version 1
This data set provides daily 4 km snow water equivalent (SWE) and snow depth over the conterminous United States. It was developed at the University of Arizona (UA) under the support of the NASA MAP and SMAP Programs. The data were created by assimilating in-situ snow measurements from the National Resources Conservation Service's SNOTEL network and the National Weather Service's COOP network with modeled, gridded temperature and precipitation data from PRISM.
Evaluation of a mulit-physics snow model in the Tyrolean Alps
<p>This dataset presents an ensemble of 66240 simulations of the seasonal snow cover in the catchment of the Längentalbach (Tyrol, Austria) over the course of 5 winter seasons. Simulations are evaluated against point scale observations at the snow monitoring station Kühtai and catchment scale observations such as MODIS derived snow cover fraction as well as the seasonal water balance. Simulations are carried out with various parameter sets and forcing data error scenarios. The dataset splits in ModelSkills.csv where the resulting model performance values are listed, and ParameterAndForingError.csv where the corresponding parameter values and forcing errors are presented. The file header_info.txt provides additional information about each column in the files.</p>
Supplementary material to "SnowPappus v1.0, a blowing-snow model for large-scale applications of Crocus snow scheme" : Pleiades snow depth maps analysis
<p>This Folder contains :</p><p> - necessary codes to reprduce Fig. 5, 12 and 13 of the third version of the submitted manuscript SnowPappus v1.0, a blowing-snow model for large-scale applications of Crocus snow scheme"</p><p> - necessary data to run these codes, including extracts of simulation outputs</p><p> - A readme.txt which explains how to run everything</p><p> </p>
MODIS/Terra+Aqua BRDF/Albedo Snow-free Model Parameters Daily L3 Global 0.05Deg CMG V006
The MCD43C2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MCD43C2 Version 6.1](https://doi.org/10.5067/MODIS/MCD43C2.061) data product.The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43C2 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Snow-free Model Parameters dataset is produced daily using 16 days of Terra and Aqua MODIS data in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name. This CMG product covers the entire globe for use in climate simulation models. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the User Guide.MCD43C2 provides the three model weighting parameters (isotropic, volumetric, and geometric) computed from snow-free retrievals. Each model parameter is available as a separate layer for MODIS spectral bands 1 through 7 as well as the visible, near infrared (NIR), and shortwave bands. Along with the 30 parameter layers there are ancillary layers for quality, local solar noon, percent finer resolution inputs, and uncertainty.Known Issues* The incorrect representation of the aerosol quantities (low average high) [in the C6 MYD09 and MOD09 surface reflectance products](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=86) may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products.* [Corrections](https://landweb.modaps.eosdis.nasa.gov/data/userguide/LSRHighAerosolFlagFinal.pdf) were implemented in Collection 6.1 reprocessing.* For complete information about the MCD43C2 known issues refer to the [MODIS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&as=6).Improvements/Changes from Previous Version* Observations are weighted to estimate the BRDF/Albedo on the ninth day of the 16-day period.* MCD43 products use the snow status weighted to the ninth day instead of the majority snow/no-snow observations from the 16-day period.* Better quality at high latitudes from use of all available observations for the acquisition period. Collection 5 used only four observations per day.* The MCD43 products use L2G-lite surface reflectance as input.* When there are insufficient high quality reflectances, a database with archetypal BRDF parameters is used to supplement the observational data and perform a lower quality magnitude inversion. This database is continually updated with the latest full inversion retrieval for each pixel.* CMG Albedo is estimated using all the clear-sky observations within the 1,000 m grid for MCD43C as opposed to aggregating from the 500 m albedo.
MODIS/Terra+Aqua BRDF/Albedo Snow-free Model Parameters Daily L3 Global 0.05Deg CMG V061
The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43C2 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Snow-free Model Parameters dataset is produced daily using 16 days of Terra and Aqua MODIS data in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name. This CMG product covers the entire globe for use in climate simulation models. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43c2-cmg-brdfalbedo-model-snow-free-parameters-product/).MCD43C2 provides the three model weighting parameters (isotropic, volumetric, and geometric) computed from snow-free retrievals. Each model parameter is available as a separate layer for MODIS spectral bands 1 through 7 as well as the visible, near infrared (NIR), and shortwave bands. Along with the 30 parameter layers there are ancillary layers for quality, local solar noon, percent finer resolution inputs, and uncertainty.Known Issues* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).Improvements/Changes from Previous Versions* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).
Contribution to High Asia Runoff from Ice and Snow (CHARIS) Melt Model Output, 2001 - 2014, Version 1
This data set contains input and output data for temperature index (TI) model runs completed for the Contributions to High Asia Runoff from Ice and Snow (CHARIS) project at NSIDC in 2018 and 2019. The input data are the area of snow on land, snow on ice, and exposed glacier ice as well as surface air temperature. These inputs are used to model the volumes of melt runoff from the snow on land, snow on ice, and exposed glacier ice in certain areas of High Mountain Asia.
VIIRS/NPP BRDF/Albedo Snow-free Model Parameters Daily L3 Global 0.05Deg CMG V001
The VNP43C2 Version 1 data product was decommissioned on July 31, 2025. Users are encouraged to use the [VNP43C2](https://doi.org/10.5067/VIIRS/VNP43C2.002) and [VJ243C1](https://doi.org/10.5067/VIIRS/VJ143C2.002) Version 2 data products.The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Snow-free Model Parameters Daily Global 0.05 Degree Climate Modeling Grid (CMG) Version 1 product (VNP43C2) is derived from the 30 arc second CMG VNP43D Version 1 product suite. VNP43C2 is generated daily from all available snow-free acquisitions over a 16-day moving window emphasizing the ninth day of the retrieval period, which is reflected in the Julian date in the filename. VNP43C2 supplies the weighting parameters associated with the RossThick/Li-Sparse-Reciprocal BRDF model, which is used to produce the [VNP43C3](https://doi.org/10.5067/VIIRS/VNP43C3.001) Albedo and [VNP43C4](https://doi.org/10.5067/VIIRS/VNP43C4.001) Nadir BRDF-Adjusted Reflectance (NBAR) products. The highest quality full inversion values are used for the temporal fitting effort and supplemented with lower quality pixels, spatial fitting, and spatial smoothing as needed. The status of each pixel can be found in the ancillary layers. Users are encouraged to assess the quality information before using the BRDF/Albedo data. This 0.05 degree (5,600 meters at the equator) CMG product covers the entire globe for use in climate simulation models. The VNP43C2 product includes 39 layers containing the three parameters (fiso, fvol, and fgeo) for the VIIRS Day/Night band (DNB), moderate resolution bands M1 through M5, M7, M8, M10, and M11, as well as the shortwave band, visible band, and near-infrared (NIR) broadbands. Along with the parameter data for the 13 bands are four ancillary layers for uncertainty, quality, local solar noon, and percent finer resolution inputs.Known Issues* The abnormally high activation of the high aerosol flag in the Collection 1 (C1) VNP09 product has impacted downstream products. The VNP43 BRDF/Albedo/NBAR product is affected by reducing the number of otherwise acceptable observations used as input to characterize surface anisotropy. This effect [most obvious over arid bright surfaces), results in a reduced number of high quality full BRDF model inversions. Therefore, users should be aware that bright arid surfaces (normally associated with high quality BRDF/Albedo/NBAR retrievals) are likely to be somewhat represented by lower quality results in C1. VNP09 has been corrected for Collection 2 (C2). Therefore, users should avoid substantive use of C1 VNP43 over arid regions (and wait for C2 products). In any event, users are always strongly encouraged to download and use the extensive QA data provided in VNP43[I-M]A2, in addition to the briefer mandatory QA provided as part of the VNP43[I-M]A1, 3 and 4 products.* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).
VIIRS/NPP BRDF/Albedo Snow-free Model Parameters Daily L3 Global 0.05Deg CMG V002
The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Snow-free Model Parameters Daily Global 0.05 Degree Climate Modeling Grid (CMG) Version 2 product (VNP43C2) is derived from the 30 arc second CMG VNP43D Version 2 product suite. VNP43C2 is generated daily from all available snow-free acquisitions over a 16-day moving window emphasizing the ninth day of the retrieval period, which is reflected in the Julian date in the filename. VNP43C2 supplies the weighting parameters associated with the RossThick/Li-Sparse-Reciprocal BRDF model, which is used to produce the [VNP43C3](https://doi.org/10.5067/VIIRS/VNP43C3.002) Albedo and [VNP43C4](https://doi.org/10.5067/VIIRS/VNP43C4.002) Nadir BRDF-Adjusted Reflectance (NBAR) products. The highest quality full inversion values are used for the temporal fitting effort and supplemented with lower quality pixels, spatial fitting, and spatial smoothing as needed. The status of each pixel can be found in the ancillary layers. Users are encouraged to assess the quality information before using the BRDF/Albedo data. This 0.05 degree (5,600 meters at the equator) CMG product covers the entire globe for use in climate simulation models. The VNP43C2 product includes 39 layers containing the three parameters (fiso, fvol, and fgeo) for the VIIRS Day/Night band (DNB), moderate resolution bands M1 through M5, M7, M8, M10, and M11, as well as the shortwave band, visible band, and near-infrared (NIR) broadbands. Along with the parameter data for the 13 bands are four ancillary layers for uncertainty, quality, local solar noon, and percent finer resolution inputs.Known Issues* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).Improvements/Changes from Previous Versions* Improved calibration algorithm and better coefficients for entire Suomi NPP mission* Improved geolocation accuracy and updates to fix outliers around maneuver periods and other events* Corrections to the aerosol quantity flag (low, average, high) mainly over brighter surfaces in the mid to high latitudes such as desert and tropical vegetation areas. This has an impact on the retrieval of other downstream data products such as VNP13 Vegetation Indices and VNP43 BRDF/Albedo.* Improved cloud mask input product for corrections along coastlines and artifacts from use of coarse resolution climatology data* Replaced the land/water mask input product with MODIS heritage seven class land/water mask* More details can be found in this [VIIRS Land V2 Changes document](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf).
VIIRS/JPSS1 BRDF/Albedo Snow-free Model Parameters Daily L3 Global 0.05Deg CMG V002
The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Snow-free Model Parameters Daily Global 0.05 Degree Climate Modeling Grid (CMG) Version 2 product (VJ143C2) is derived from the 30 arc second CMG VJ143D Version 2 product suite. VJ143C2 is generated daily from all available snow-free acquisitions over a 16-day moving window emphasizing the ninth day of the retrieval period, which is reflected in the Julian date in the filename. VJ143C2 supplies the weighting parameters associated with the RossThick/Li-Sparse-Reciprocal BRDF model, which is used to produce the [VJ143C3](https://doi.org/10.5067/VIIRS/VJ143C3.002) Albedo and [VJ143C4](https://doi.org/10.5067/VIIRS/VJ143C4.002) Nadir BRDF-Adjusted Reflectance (NBAR) products. The highest quality full inversion values are used for the temporal fitting effort and supplemented with lower quality pixels, spatial fitting, and spatial smoothing as needed. The status of each pixel can be found in the ancillary layers. Users are encouraged to assess the quality information before using the BRDF/Albedo data. This 0.05 degree (5,600 meters at the equator) CMG product covers the entire globe for use in climate simulation models. The VJ143C2 product includes 39 layers containing the three parameters (fiso, fvol, and fgeo) for the VIIRS Day/Night band (DNB), moderate resolution bands M1 through M5, M7, M8, M10, and M11, as well as the shortwave band, visible band, and near-infrared (NIR) broadbands. Along with the parameter data for the 13 bands are four ancillary layers for uncertainty, quality, local solar noon, and percent finer resolution inputs.Known Issues* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).Improvements/Changes from Previous Version* The NOAA-20 VIIRS algorithms include the same improvements as the S-NPP VIIRS V002* Improved calibration algorithm and better coefficients for entire NOAA-20 mission* Improved geolocation accuracy and updates to fix outliers around maneuver periods and other events* Corrections to the aerosol quantity flag (low, average, high) mainly over brighter surfaces in the mid to high latitudes such as desert and tropical vegetation areas. This has an impact on the retrieval of other downstream data products such as VJ113 Vegetation Indices and VJ143 BRDF/Albedo.* Improved cloud mask input product for corrections along coastlines and artifacts from use of coarse resolution climatology data* Replaced the land/water mask input product with MODIS heritage seven class land/water mask
interactive and prescribed snow depth driven climate model simulations output
<p>compressed dataset of model simulation output for current and future climates</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.