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19 results for “reanalysis models”
Data from 'Local Regions Associated With Interdecadal Global Temperature Variability in the Last Millennium Reanalysis and CMIP5 Models'
<p><strong>Abstract from '<em>Local Regions Associated With Interdecadal Global Temperature Variability in the Last Millennium Reanalysis and CMIP5 Models</em>':</strong></p> <p>Despite the importance of interdecadal climate variability, we have a limited understanding of which geographic regions are associated with global temperature variability at these timescales. The instrumental record tends to be too short to develop sample statistics to study interdecadal climate variability, and Coupled Model Intercomparison Project, Phase 5 (CMIP5) climate models tend to disagree about which locations most strongly influence global mean interdecadal temperature variability. Here we use a new paleoclimate data assimilation product, the Last Millennium Reanalysis (LMR), to examine where local variability is associated with global mean temperature variability at interdecadal timescales. The LMR framework uses an ensemble Kalman filter data assimilation approach to combine the latest paleoclimate data and state-of-the-art model data to generate annually resolved field reconstructions of surface temperature, which allow us to explore the timing and dynamics of preinstrumental climate variability in new ways. The LMR consistently shows that the middle- to high-latitude north Pacific and the high-latitude North Atlantic tend to lead global temperature variability on interdecadal timescales. These findings have important implications for understanding the dynamics of low-frequency climate variability in the preindustrial era.</p>
Variability in Antarctic Surface Climatology Across Regional Climate Models and Reanalysis: Datasets
<p>This dataset includes output for snowfall, near-surface air temperature and melt from the following regional climate models (RCMs): Met Office Unified Model version 11.1 (MetUMv11.1), the Modèle Atmosphérique Régional version 3.10 (MARv3.10) and the Regional Atmospheric Climate Model version 2.3p2 (RACMOv2.3p2). The data is aggregated to monthly timesteps from initial 3/6hourly data. The code for aggregation is available here: https://github.com/Jez-Carter/Antarctica_Climate_Variability . Data goes from ~1971-2018 and includes two simulations from each RCM: 0.11° (12.25 km) and 0.44° (49 km) resolution simulations from the MetUM; ERA-Interim and ERA5 driven simulations from MAR and RACMO. The data used in the results for 'Variability in Antarctic Surface Climatology Across Regional Climate Models and Reanalysis Datasets' J.Carter et al, is included here and can be generated using the code available here: https://github.com/Jez-Carter/Antarctica_Climate_Variability . </p> <p><strong>Data usage notice:</strong><br> If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgements should have language similar to the below.</p> <p>"We thank C. Kittel and the MAR team which make available the model outputs, as well agencies (F.R.S - FNRS, CÉCI, and the Walloon Region) that provided computational resources for MAR simulations."</p> <p>In order to document MAR scientific impact and enable ongoing support of the model, users are encouraged to contact C. Kittel to add their works in the list of MAR-related publications.</p> <p>If you need other variables or output frequencies over Antarctica from: MAR, contact C.Kittel (c2kittel@gmail.com); RACMO, contact J.M. van Wessem; MetUM, contact A.Orr. </p>
A High Resolution (3km) Reanalysis Database for Mediterranean Coastal Winds Downscaled from ERA5, using the WRF Model
<p>A high resolution (3km) reanalysis database of Mediterranean coastal winds was constructed to support a research on potential sailing mobility in Antiquity. The database was created by downscaling the ERA5 reanalysis database using the WRF numerical prediction model.</p> <p>A detailed description of the reanalysis database is provided in the attached PDF file. The database format is GRIB version 2 and the total volume of the data files is 435GB. The GRIB files are hosted at <a href="https://coastalwinds.haifa.ac.il">https://coastalwinds.haifa.ac.il</a> as their total volume exceeds the volume that could be provided by Zenodo. Required files can therefore be downloaded from this location.</p> <p><strong>Link to the GRIB data files and index map:</strong></p> <p><strong><a href="https://coastalwinds.haifa.ac.il">https://coastalwinds.haifa.ac.il</a></strong></p> <p><strong>Acknowledgements:</strong></p> <p>The Data Science Research Center (DSRC) at Haifa University kindly provided funding towards the creation of this data set.</p>
Data and code for training and evaluating machine learning models for thunderstorm prediction from reanalysis data
<p>FIXED Data and Python code for training and evaluating machine learning models for predicting thunderstorms, associated with the paper:</p> <p>"Evaluation of machine learning classifiers for predicting deep convection"</p> <p>by Peter Ukkonen and Antti Mäkelä (to appear in JAMES)</p> <p>The data (preprocessed inputs and outputs) is stored as netCDF files and .mat files which can be loaded with Python. </p>
Environmental (oxygen) conditions on the NW African coast from 3D reanalysis models
<p>This dataset contain hydrodynamic and biogeochemical variables extracted from the CMEMS service (<a href="https://marine.copernicus.eu/">https://marine.copernicus.eu/</a>) covering the NW region of the African coast and the period 1993 to 2019. The environmental dataset include subsurface (100 to 200m) oxygen concentration.</p>
Environmental (biogeochemical) conditions on the NW African coast from 3D reanalysis models
<p>This dataset contain hydrodynamic and biogeochemical variables extracted from the CMEMS service (<a href="https://marine.copernicus.eu/">https://marine.copernicus.eu/</a>) covering the NW region of the African coast and the period 1993 to 2019. The environmental dataset include: nitrate concentration, phosphate concentration and chlorophyll-a concentration.</p>
Data for: Deep Learning of Model- and Reanalysis- Based Precipitation and Pressure Mismatches over Europe
<p>This study focuses on using UNet Convolutional Neural Networks to predict the spatiotemporal mismatches (errors) between TSMP-G2A model-based and COSMO-REA6 reanalysis-based precipitation and surface pressure over Europe.</p> <p>The following data are provided in this dataset:</p> <p>1) The remapped and NetCDF-merged TSMP-G2A and COSMO-REA6 precipitation and surface pressure over the study area (EU-11 EUROCORDEX, ~0.11 degrees) for the years 1995-2017. Files: COSMO-REA6_PREPROCESSED.zip and TSMP_PREPROCESSED.zip</p> <p>2) The actual and predicted spatiotemporal mismatch data for training, validation, and testing periods (1995-2017). Files: MISMATCH_ACTUAL.zip and MISMATCH_PREDICTED.zip<br> </p> <p>References for original TSMP-G2A and COSMO-REA6 data:<br> TSMP-G2A: http://doi.org/10.17616/R31NJMGR<br> COSMO-REA6: doi:10.1002/qj.2486, 2015</p>
Effects of Reanalysis Forcing Fields on Ozone Trends and Age of Air from a Chemical Transport Model
<p>This dataset is based on the global off-line 3-D chenmical transport model (TOMCAT/SLIMCAT) forced with ECMWF reanalyses (ERA-Interim and ERA5) to compare the performance of the stratospheric ozone simulations. Each field is separately saved as NETCDF file. Each field is show on geographic coordinates, which can be longitude, latitude, vertical hybrid-pressure level (for zonal mean fields, such as ozone, temperature and age-of-air).</p> <p>The dimensions in each field are:</p> <p>lat --> latitude</p> <p>lon --> longitude</p> <p>lev --> hydrid pressure level</p> <p>time --> months of the simulation</p> <p>The output of the total column ozone from the TOMCAT/SLIMCAT simulations forced with ERA-Interim and ERA5 for Figures 1-4 and Figure S2 in the supplement are in files:</p> <p>toz_A_ERAI.nc</p> <p>toz_B_ERA5.nc</p> <p>The output of the stratospheric column ozone (SCO) in Figure S1 in the supplement are in the file (levels1-3 are SWOOSH, B_ERA5 and A_ERAI SCO data, respectively):</p> <p>sco_SWOOSH_A_ERAI_B_ERA5.nc</p> <p>The output of zonal mean ozone profiles from the TOMCAT/SLIMCAT simulations forced with ERA-Interim and ERA5 for Figures 5-7, 9 and Figures S3-4 are in files:</p> <p>O3_mm_A_ERAI.nc</p> <p>O3_mm_B_ERA5.nc</p> <p>The output of zonal mean temperature from the TOMCAT/SLIMCAT simulations forced with ERA-Interim and ERA5 for Figure 8 are in files:</p> <p>te_mm_A_ERAI.nc</p> <p>te_mm_B_ERA5.nc</p> <p>The output of zonal mean age-of-air from the TOMCAT/SLIMCAT simulations forced with ERA-Interim and ERA5 for Figures 10-12 are in files:</p> <p>Age_mm_A_ERAI.nc</p> <p>Age_mm_B_ERA5.nc</p> <p>The output of the zonal mean ozone, temperature and age-of-air from the ERA5.1 reanalysis corrected simulations during the period from 2000 to 2006 in all Figures above using ERA5 are in files:</p> <p>ERA5_1_O3_2000_18.nc</p> <p>ERA5_1_te_2000_18.nc</p> <p>ERA5_1_Age_2000_18.nc</p> <p> </p>
North American Regional Reanalysis (NARR) data used in "Passive Tracer Modelling at Super-Resolution with WRF-ARW to Assess Mass-Balance Schemes"
<p>North American Regional Reanalysis (NARR) data from National Oceanic and Atmospheric Administration (NOAA) - 20 August 2013, 26 August 2013, 2 September 2013 - used as input information (initial and boundary condition) for WRF simulations described in "Passive Tracer Modelling at Super-Resolution with WRF-ARW to Assess Mass-Balance Schemes" (Fathi et al., 2022 - egusphere-2022-1125). NARR data can be accessed and downloaded at the following web address "https://www.ncei.noaa.gov/products/weather-climate-models/north-american-regional/". </p>
Data for "Revisiting the reanalysis-model discrepancy in Southern Hemisphere winter storm track trends"
<p>The dataset supporting the conclusion of the submitted paper is uploaded here.</p> <p>The data are labeled after each figure. The npz files include data required to reproduce our results in python arrays.</p>
Environmental (hydrodynamic) conditions on the NW African coast from 3D reanalysis models
<p>This dataset contain hydrodynamic and biogeochemical variables extracted from the CMEMS service (<a href="https://marine.copernicus.eu/">https://marine.copernicus.eu/</a>) covering the NW region of the African coast and the period 1993 to 2019. The environmental dataset include: water temperature, water salinity, u-velocity and v-velocity.</p>
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>
Multi-influential interactions controls behaviour and cognition through a limited number of pathways in Down syndrome mouse models (Affymetrix reanalysis)
GEO Series GSE149466. Homo sapiens. 0 samples. Type: Expression profiling by array; Third-party reanalysis.
Multi-influential interactions controls behaviour and cognition through a limited number of pathways in Down syndrome mouse models (RNA-Seq reanalysis I)
GEO Series GSE149467. Homo sapiens. 0 samples. Type: Expression profiling by high throughput sequencing; Third-party reanalysis.
Data from: Mixed-model reanalysis of primate data suggests tissue and species biases
Open the record for dataset details and reuse information.
Multi-influential interactions controls behaviour and cognition through a limited number of pathways in Down syndrome mouse models (RNA-Seq reanalysis II)
GEO Series GSE149469. Homo sapiens. 0 samples. Type: Expression profiling by high throughput sequencing; Third-party reanalysis.
High Mountain Asia Daily 0.05 x 0.05 deg Noah-MP Land Surface Model Reanalysis V001
This data set consists of a water budget reanalysis for the High Mountain Asia (HMA) region spanning the years 2003 through 2020. Estimates are provided for more than 30 parameters, including storages; fluxes; snow depth, extent, and snow water equivalent; temperature (land surface, soil, snow, and ice); surface albedo; soil moisture; evapotranspiration; and streamflow. The data were generated using the Noah Multi-Parameterization (Noah-MP) land surface model (Version 4.0.1), driven by precipitation estimates and hydrological inputs developed specifically for HMA.
High Mountain Asia 12 km Modeled Estimates of Aerosol Transport, Chemistry, and Deposition Reanalysis, 2003-2019 V001
This data set contains a 12 km resolution, simulated reanalysis of aerosol transport, chemistry, and deposition over the High Mountain Asia (HMA) region for 1 January 2003 through 31 August 2019. Two-dimensional surface data are provided at one hour intervals. Three-dimensional atmospheric data are provided at three-hour intervals for 35 sigma levels extending from the surface to 50 hPa. Also known as the Model for Atmospheric Transport and Chemistry in Asia (MATCHA), the data comprise a wide range of variables intended to help assess the impacts of aerosols on the cryosphere in the HMA region, including: concentrations of black/brown carbon and other light absorbing particles (LAPs), broken out by source region; longwave/shortwave heating rates due to LAPs; wet/dry deposition of LAPs; precipitation and hydrological data; and meteorological state variables. The simulation was generated using a fully coupled, regional chemistry-climate model (WRF-Chem-CLM-SNICAR), constrained by aerosol optical depth (AOD) and carbon monoxide (CO) satellite observations acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) and Measurements Of Pollution In The Troposphere (MOPITT) instruments, respectively.
Database for manuscript 'Potential of Satellite and Reanalysis Evaporation Datasets for Hydrological Modelling under Various Model Calibration Strategies'
<p>******************************************************************************************************************************************************<strong>NOTICE: </strong>all datasets and tools provided in this database can and should only be used to reproduce the original experiment for which the database was created. The use of any datasets and tools in this database is subject to third party restrictions. Before copying or using this database for other purposes than reproducing the original experiment for which it was created, please ask for adequate authorisations to the author (Moctar Dembélé, mocdembele@gmail.com), who might additionaly need the authorization of the providers of the data and the tools available in this database. ******************************************************************************************************************************************************</p> <p>This database provides model inputs and outputs for the manuscript 'Potential of Satellite and Reanalysis Evaporation Datasets for Hydrological Modelling under Various Model Calibration Strategies' by Dembélé et al.</p> <p>The content of each folder is as follows:</p> <p>-Input contains the data needed to setup and run the mHM model.</p> <p>-The folders MOD16A2, SSEBop, ALEXI, CMRSET, SEBS, GLEAM v3.2a, GLEAM v3.3a, GLEAM v3.2b, GLEAM v3.3b), ERA5, MERRA-2, and JRA-55 contain the model output files using different evaporation data to calibrate the mHM model. Each of these folders contains four sub-folders corresponding to four distint calibration strategies.</p> <p>-refQ contains the model output files when the model is calibrated using only streamflow data.</p> <p>-inputAnalysis contains the results and the files of the analysis of the model input datasets using the MATLAB software..</p> <p>-combiEvapAnalysis contains the results and the files of the analysis of the model outputs using the MATLAB software.</p> <p>For further information, please contact Moctar Dembélé, mocdembele@gmail.com</p>
ScienceDex guides
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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)
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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.