Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
121
datasets available to search
ShareScore release 0.9.0
Dataset results
121 results for “MERRA-2”
BGC-Argo matchups with Ocean Color Satellite Sensors and MERRA-2 updated for 2023
<p>Updated matchup dataset as described in "Begouen Demeaux et al., Algorithms to Retrieve the Spectral Diffuse Attenuation Coefficient of Light in the Ocean from Remote Sensing, Optics Express, 2023".</p> <p>Composed of Satellites matchup from the MODIS, VIIRS and OLCI sensors with BGC-Argo floats, including Kds derived from float measurements (Kd_WV_Xing), Rrs at all wavelengths from each sensor, solar zenith angle and information on the atmospheric composition from Merra-2 matchups. </p> <p>New recomputed Kds using the Lee et al., 2005 algorithm with individual sensor coefficients are also listed (new_kd_WV_Lee_indiv), as well as recomputed Kds for a new global m2 coefficient (new_kd_WV_Lee_global). Recomputed Kds for the new coefficients of the NASA/ESA algorithm are also available (new_Kd_Aus). Lastly, Kds for the new GF algorithm depending on the MERRA inputs and IOPs is listed : (kd_WV_f). </p> <p>For any questions, do not hesitate to be in touch. </p>
Precipitation and Temperature Anomalies from MERRA-2 dataset
<p>Anomalies in Precipitation and Temperature for tiles around the globe.</p> <p>Data derived from the reanalysis MERRA-2 project [1]. The dataset covers the period from 1980 to 2018, between 80ºN and 80ºS. The spatial resolution is 1.0º x 1.0º resulting in 45,792 tiles and a time resolution of 7 days (by averaging the values over each week). Precipitation time-series were re-scaled by applying a logarithmic function to all values.</p> <p>To discount seasonality effects, we averaged temperature and precipitation values for each of the 365 calendar days. We considered the interval from 1 January 1980 through 28 February 2018 as the climatic period for which the long-term averages were computed. The anomalies are then obtained by subtracting for each day the respective average temperature or precipitation from the climatic period (for example, the 1 January 1998 anomaly is computed as the value for that day minus the average of all January 1 values between 1980 and 2018).</p> <p>To process the data, use the code available in:</p> <p><a href="https://github.com/filipinascimento/teleconnectionsgranger/">https://github.com/filipinascimento/teleconnectionsgranger/</a></p> <p><a href="http://arxiv.org/abs/2012.03848">http://arxiv.org/abs/2012.03848</a></p> <p>[1] A. Molod, L. Takacs, M. Suarez, and J. Bacmeister, “Development of the geos-5 atmospheric general circulation model: evolution from merra to merra2,” Geoscientific Model Development 8, 1339–1356 (2015).</p> <p> </p> <p> </p>
DL-FRONT MERRA-2 vectorized weather fronts over North America, 1980-2018 (netCDF format)
<p>DL-FRONT is a Deep Learning Neural Network (DLNN) that was trained to detect weather fronts using spatial grids of near-surface atmospheric variables. The dataset is composed of <a href="http://www.unidata.ucar.edu/software/netcdf/docs/">netCDF-4</a> files. Each file contains one year of hourly geospatial data grids describing the locations of four types of weather fronts—cold front, warm front, stationary front, and occluded front, over the time span 1980-2018.</p> <p>This dataset is the product of processing data from the National Aeronautics and Space Administration (NASA) <a href="https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/">Modern-Era Retrospective analysis for Research and Applications, Version 2</a> (MERRA-2). DL-FRONT processed MERRA-2 hourly data grids of instantaneous measures of air pressure reduced to mean sea level, air temperature at 2 meters, specific humidity at 2 meters, and wind velocity at 10 meters over the time span 1980 - 2018 to produce this dataset. The original MERRA-2 data were resampled at 1 degree resolution over the spatial range 31W - 171W x 10N - 77N using bicubic interpolation.</p> <p>At each hourly time step the network produced a set of spatial grids with the same resolution and spatial range as the input, one for each of the five categories mentioned above. Each cell in a spatial grid for a given category records the network-assigned probability (from 0.0 to 1.0) that the cell is in a weather front boundary region of that category (or, for the "no front" category, the probability that the cell is not in any weather front boundary region).</p> <p>Each weather front probability map was then processed to obtain polyline skeletons of the weather front boundary regions found by DL-FRONT. These vector representations of the fronts were then written to JSON files—one file for each hour. These front polylines were then rasterized into geospatial data grids and stored by year into netCDF-4 files that conform to the <a href="http://cfconventions.org/">Climate and Forecast Metadata Conventions</a>. The front data in each file is stored in a netCDF variable with dimensions (time, front type, y, x), where x and y are geospatial dimensions. There is a 2D geospatial data grid for each time step for each of the 4 front types—cold, warm, stationary, and occluded.</p> <p>There are two large groupings of the netCDF files. One group uses a data grid based on the <a href="https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/north-american-regional-reanalysis-narr">North American Regional Reanalysis</a> (NARR) <a href="https://www.nco.ncep.noaa.gov/pmb/docs/on388/tableb.html#GRID221">grid</a>, which is a Lambert Conformal Conic projection coordinate reference system (CRS) centered over North America. The NARR grid is quite close the the spatial range of data displayed on the WPC workstations used to perform surface analysis and identify front locations. The native NARR grid has grid cells which are 32 km on each side. Our grid covers the same extents with cells that are 96 km on each side.</p> <p>The other group uses a 1° latitude/longitude data grid centered over North America with extents 171W – 31W / 10N – 77 N. The files in this group are identified by the name MERRA2, because they were used with data from the NASA MERRA-2 dataset, which uses a latitude/longitude data grid.</p> <p>There are a number of files within each group. The files all follow the naming convention merra2_[masked]_<grid>_<n>wide_<year>.nc, where [masked] indicates that the presence of the word <em>masked</em> is optional and <grid> is either <em>merra2-1deg</em> or <em>narr-96km</em>. The the sequence <n>wide indicates the width with which the fronts were drawn, and <year> is the year for the data stored in the file.</p> <p>The files marked as masked had a mask applied to the data grids that corresponded to the envelope of the geospatial region where there are, on average, 40 or more front crossing of any type per year, as determined using the <a href="https://dx.doi.org/10.5281/zenodo.2651361">Coded Surface Bulletin </a>dataset.</p> <p>The <n>wide portion of the file names takes two forms—<em>1wide</em> and <em>3wide</em>. The fronts in the <em>1wide</em> files were rasterized by drawing the front polylines with a width of one grid cell. The fronts in the <em>3wide</em> files were rasterized by drawing the front polylines with a width of 3 grid cells.</p> <p>Within each grid group, there are four subsets of files:</p> <ul> <li>merra2_masked_<grid>_1wide_<year>.nc</li> <li>merra2_masked_<grid>_3wide_<year>.nc</li> <li>merra2_<grid>_1wide_<year>.nc</li> <li>merra2_<grid>_3wide_<year>.nc</li> </ul>
DL-FRONT MERRA-2 vectorized weather fronts over North America, 1980-2018 (JSON format)
<p>DL-FRONT is a Deep Learning Neural Network (DLNN) that was trained to detect weather fronts using spatial grids of near-surface atmospheric variables. The dataset is composed of hourly JSON files containing geospatial vector polylines describing the locations of four types of weather fronts—cold front, warm front, stationary front, and occluded front, over the time span 1980-2018.</p> <p>This dataset is the product of processing data from the National Aeronautics and Space Administration (NASA) <a href="https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/">Modern-Era Retrospective analysis for Research and Applications, Version 2</a> (MERRA-2). DL-FRONT processed MERRA-2 hourly data grids of instantaneous measures of air pressure reduced to mean sea level, air temperature at 2 meters, specific humidity at 2 meters, and wind velocity at 10 meters over the time span 1980 - 2018 to produce this dataset. The original MERRA-2 data were resampled at 1 degree resolution over the spatial range 31W - 171W x 10N - 77N using bicubic interpolation.</p> <p>At each hourly time step the network produced a set of spatial grids with the same resolution and spatial range as the input, one for each of the five categories mentioned above. Each cell in a spatial grid for a given category records the network-assigned probability (from 0.0 to 1.0) that the cell is in a weather front boundary region of that category (or, for the "no front" category, the probability that the cell is not in any weather front boundary region).</p> <p>Each probability map was then processed to obtain polyline skeletons of the weather front boundary regions found by DL-FRONT. These vector representations of the fronts were then written to JSON files—one file for each hour. Each JSON file contains one top-level object composed of name/value pairs with the names issuanceDate, validDate, ColdFronts, WarmFronts, OccludedFronts, and StationaryFronts. The name/value pairs for createDate and validDate are always present. The other name/value pairs are only present if there is corresponding data. The values for issuanceDate and validDate are UTC timestamp strings.</p> <p>The ColdFronts, WarmFronts, StationaryFronts, and OccludedFronts names in the top-level object, when present, have values that are arrays. In each case, the array is composed of one or more objects. Each object represents a front of the given type. Each object is composed of five name/value pairs with the names lats, lons, cols, rows, and confidence. The value for the name confidence is a number that is the average of the values of the probability map cells intersected by the front polyline. The values associated with the names lats, lons, cols, and rows are arrays. These arrays represent the vertices of a polyline describing the location of a frontal boundary in both geospatial and grid cell coordinates.</p>
MERRA-2 subset for evaluation of renewables with merra2ools R-package: 1980-2020 hourly, 0.5° lat x 0.625° lon global grid
Open the record for dataset details and reuse information.
Transformed Eulerian mean data from the MERRA-2 reanalysis (daily means)
<p>This dataset provides <strong>daily</strong> and zonal mean variables derived from the MERRA-2 reanalysis, including terms of the transformed Eulerian mean (TEM) momentum budget.</p> <p>All variables (zonal, meridional and vertical wind speed, temperature, zonal wind tendencies from Eliassen-Palm (EP) flux divergence and advection, EP fluxes and the residual streamfunction) are obtained from 6-hourly and native vertical and spatial resolution data of the 'Assimilated' (ASM, M2I3NVASM) collection. Zonal mean wind tendency from gravity wave drag is also provided, obtained from the M2T3NPUDT collection.</p> <p>Data are provided as one .zip file per decade (only partial for the 2020s). <strong>Monthly</strong> means of the same quantities are provided in a companion dataset.</p> <p>The data and related documentation are provided 'as is' and without any warranty of any kind. Users are invited to report any issue or inconsistency they may find. Please cite the reference publication when using this dataset.</p> <p> </p> <p>Known issues:</p> <p>- All TEM terms divided by<em> </em><span>\(cos(\phi)\)</span> diverge at the north and south poles (where <span>\(\phi = \pm \pi/2\)</span>), so they should not be considered. If variables at the poles are needed, values at neighbouring latitudes should be taken.</p>
Global PM2.5 Dataset: Hybrid Calibration of CAMS and MERRA-2 PM2.5 Reanalysis Products during 2017-2019
<p>By integrating two reanalysis PM<sub>2.5</sub> products (CAMSRA, MERRA-2), a <strong>global daily hybrid-calibrated PM<sub>2.5</sub> concentration dataset</strong> is generated through the proposed CM-HC scheme with ERT model. This products include <strong>1095</strong> global PM<sub>2.5</sub> GeoTIFF files, starting from Jan 01, 2017 to Dec 31, 2019 (about <strong>0.95GB</strong> memory after uncompressing this zip file).</p> <p>To prove the superiority of this product, some experiments are implemented as follows: comparing with 1) two original products; 2) results of two separate calibration schemes with ERT; 3) results of CM-HC with other three ML models (RF, GBDT, XGBoost). Above analyses include two aspects, that is, the accuracy results and mapping effects. More details can be viewed at the thesis.</p> <p>One <strong>GeoTIFF</strong> file includes one-day calibrated PM<sub>2.5</sub> data. It is worth noting that the files contain the data of ocean area, however, our thesis only shows the results of land area in order to visually display the mapping effect before and after calibrating. Also, there is no site on the sea to validate in our study.</p>
Global PM2.5 Dataset: Hybrid Calibration of CAMS and MERRA-2 PM2.5 Reanalysis Products during 2017-2019
<p>By integrating two reanalysis PM<sub>2.5</sub> products (CAMSRA, MERRA-2), a <strong>global daily hybrid-calibrated PM<sub>2.5</sub> concentration dataset</strong> is generated through the proposed CM-HC scheme with ERT model. This products include <strong>1095</strong> global PM<sub>2.5</sub> GeoTIFF files, starting from Jan 01, 2017 to Dec 31, 2019 (about <strong>0.95GB</strong> memory after uncompressing this zip file).</p> <p>To prove the superiority of this product, some experiments are implemented as follows: comparing with 1) two original products; 2) results of two separate calibration schemes with ERT; 3) results of CM-HC with other three ML models (RF, GBDT, XGBoost). Above analyses include two aspects, that is, the accuracy results and mapping effects. More details can be viewed at the thesis.</p> <p>One <strong>GeoTIFF</strong> file includes one-day calibrated PM<sub>2.5</sub> data. It is worth noting that the files contain the data of ocean area, however, our thesis only shows the results of land area in order to visually display the mapping effect before and after calibrating. Also, there is no site on the sea to validate in our study.</p>
Atmospheric angular momentum estimates from MERRA-2, 1994 - 2022
<p>Data supplement for manuscript "Revisiting the excitation of free core nutation" by Mostafa Kiani Shahvandi, Michael Schindelegger, Lara Börger, Siddhartha Mishra and Benedikt Soja, submitted to <em>JGR: Solid Earth.</em></p> <p> </p> <ul> <li>Data source: Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2)</li> <li>Temporal resolution: 3 hourly</li> <li>Units for mass and motion terms: kg*m^2*s^(-1)</li> <li>Processing: Stationary signals due to atmospheric tides have been removed</li> <li>Other processing steps: Mass term are corrected for the oceans's IB (inverted barometer) response</li> </ul> <p> </p> <p>For further information see <em>ReadMe.txt</em>.</p> <p> </p> <p>Contact: L. Börger (lboerger@igg.uni-bonn.de)</p>
DL-FRONT MERRA-2 weather front probability maps over North America, 1980-
<p>DL-FRONT is a Deep Learning Neural Network (DLNN) that was trained to detect weather fronts using spatial grids of near-surface atmospheric variables. The dataset is composed of hourly spatial grids containing probability maps for each of five front-type categories—cold front, warm front, stationary front, occluded front, and no front.</p> <p>This dataset is the product of processing data from the National Aeronautics and Space Administration (NASA) <a href="https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/">Modern-Era Retrospective analysis for Research and Applications, Version 2</a> (MERRA-2). DL-FRONT processed MERRA-2 hourly data grids of instantaneous measures of air pressure reduced to mean sea level, air temperature at 2 meters, specific humidity at 2 meters, and wind velocity at 10 meters over the time span 1980 - 2018 to produce this dataset. The original MERRA-2 data were resampled at 1 degree resolution over the spatial range 31W - 171W x 10N - 77N using bicubic interpolation.</p> <p>At each hourly time step the network produced a set of spatial grids with the same resolution and spatial range as the input, one for each of the five categories mentioned above. Each cell in a spatial grid for a given category records the network-assigned probability (from 0.0 to 1.0) that the cell is in a weather front boundary region of that category (or, for the "no front" category, the probability that the cell is not in any weather front boundary region).</p> <p>The DLNN was trained using MERRA-2 data and human-identified fronts from the NOAA National Weather Service (NWS) Weather Prediction Center (WPC) <a href="https://www.wpc.ncep.noaa.gov/html/sfc2.shtml">Coded Surface Bulletin</a> dataset. The training datasets covered the years 2003-2007.</p> <p>The dataset contains two sets of files. The first set contains the original front probability maps. The second set contains "one hot" versions of the front probability maps. In the one hot version the five front-type probabilities for a spatial grid cell for a given time step are replaced by the value 1 for the largest front-type probability, and by 0 for the others.</p> <p>The front probability files have names that follow the form merra2_merra2-1deg_fronts_<year>.nc. The one hot files have names that follow the form merra2_merra2-1deg_onehot_<year>.nc. Each file contains one year of hourly spatial data grids.</p>
SNOWPACK simulations forced by MERRA-2 for Antarctic Ice Shelves
<p>This repository contains the SNOWPACK simulations used in the manuscript:<br> Banwell, A., Wever, N., Dunmire, D. and Picard, G. (2023): <em>Quantifying Antarctic-wide ice-shelf surface melt volume using microwave and firn model data: 1980 to 2021.</em> Geophys. Res. Lett. (accepted).</p> <p>For each MERRA-2 grid point that is on an ice shelf, the zip file of the corresponding simulation is named: <latitude>_<longitude>_ICESHELVES.zip</p> <p>Each zip file then contains the following files:</p> <ul> <li><code>output/-72.000_69.375_ICESHELVES.ini</code>: This file contains the model settings as used for the simulation</li> <li><code>output/-72.000_69.375_ICESHELVES.pro</code>: This file contains the detailed, layered SNOWPACK output, with for each layer, for example, temperature, density, liquid water content, grain size, etc. Can be visualized using <a href="https://niviz.org">niViz</a>.</li> <li><code>output/-72.000_69.375_ICESHELVES.smet</code>: This file contains the SNOWPACK time series output, like surface sublimation, snow surface temperature, latent heat flux, etc. Can be visualized using <a href="https://niviz.org">niViz</a>.</li> <li><code>output/-72.000_69.375_ICESHELVES.sno</code>: This file contains the SNOWPACK model state at the end of the simulation.</li> <li><code>output/-72.000_69.375_ICESHELVES.snobackup</code>: This file contains the SNOWPACK model state from the last backup point during the simulation.</li> <li><code>output/-72.000_69.375_ICESHELVES.haz</code>: This file contains very specific SNOWPACK model output and was ignored in the study associated with this dataset.</li> <li><code>output/-72.000_69.375_ICESHELVES.hazbackup</code>: This file contains very specific SNOWPACK model output from the last backup point during the simulation and was ignored in the study associated with this dataset.</li> </ul> <p> </p> <p>Also available are statistics derived from the SNOWPACK model output using the code published at <a href="https://doi.org/10.5281/zenodo.7957639">doi: 10.5281/zenodo.7957639</a> (<a href="https://github.com/nwever/SNOWPACK_iceshelves_melt">GitHub repository</a>):</p> <p> </p> <p><code>statistics.zip</code><br> ==========</p> <p>Contains statistics calculated from the SNOWPACK output. Statistics per region are stored in the folders <code>stats622</code> (for yearly statistics) and <code>stats_per_day622</code> (for daily statistics). Statistics per ice shelf are stored in the folders <code>stats_iceshelves622</code> (for yearly statistics) and <code>stats_iceshelves_per_day622</code> (for daily statistics). For each ice shelf and region, the following files are available:</p> <ul> <li>melt_R<em>X</em>.txt and melt_S<em>X</em>.txt: sum of melt amount (kg/m2) for region R<em>X</em> or ice shelf S<em>X</em>, respectively.</li> <li>meltdays_<em>A</em>mm_R<em>X</em>.txt and meltdays_<em>A</em>mm_S<em>X</em>.txt: number of days exceeding <em>A</em> mm of melt for region R<em>X</em> or ice shelf S<em>X</em>, respectively.</li> <li>mswater_<em>A</em>mm_R<em>X</em>.txt and mswater_<em>A</em>mm_S<em>X</em>.txt: number of days with total firn column water content exceeding <em>A</em> kg/m2 for region R<em>X</em> or ice shelf S<em>X</em>, respectively.</li> <li>mswater<em>A</em>_<em>B</em>_<em>C</em>mm_R<em>X</em>.txt and mswater<em>A</em>_<em>B</em>_<em>C</em>mm_S<em>X</em>.txt: number of days with more than <em>C</em> kg/m2 amount of liquid water in the firn between <em>A</em> and <em>B</em> meters below the surface for region R<em>X</em> or ice shelf S<em>X</em>, respectively.</li> </ul> <p>Each of the files report the year, respectively the date, in the first column, followed by the statistics for each grid point in the region/ice shelf, followed by the mean and standard deviation over all grid points in the respective region/ice shelf listed in the preceding columns. The first line is a header.</p> <p>To find out the sequence by which MERRA-2 points appear in the output columns, please refer to the list in the file <code>list_points.txt</code>. This list is constructed as follows. First there is a header indicating that what follows are regions. Then, each line contains a comma-delimited list of the region number, latitude, and longitude, respectively. The first line for a region is reported in the column with header <code>1</code> in the statistics file, the second line in the column with header <code>2</code>, and so on. After all regions are listed, there is again a header line denoting that what follows are the ice shelves. Similar to the section for the regions, each following line contains a comma-delimited list of the ice shelf number, latitude, and longitude, respectively. The numbering of regions and ice shelves is listed in the tables below.</p> <p> </p> <p><code>forcingstatistics.zip</code><br> ===============</p> <p>Contains monthly statistics of the forcing variables TA (air temperature, K), QI (specific humidity, -), ILWR (incoming longwave radiation W/m2), ISWR (incoming shortwave radiation, W/m2), PSUM (precipitation, kg/m2/h), and VW (wind speed, m/s). The statistics for each region and ice shelf are stored in <code>monthlystats</code> and <code>monthlystats_iceshelves</code>, respectively. Each file name contains the forcing variable and the region/ice shelf number. The numbering of regions and ice shelves can be found in the tables below. Each file then contains the month (expressed as YYYYMM), followed by means of the specific variable for each MERRA-2 grid point in the region/ice shelves (in the same sequence as for the statistics files discussed above), followed by the mean and standard deviation over all MERRA-2 grid points listed in the preceding columns.</p> <p> </p> <p> </p> <p> </p> <table> <caption>Regions</caption> <tbody> <tr> <td>Number</td> <td> </td> </tr> <tr> <td>R1</td> <td>Amery</td> </tr> <tr> <td>R2</td> <td>Amundsen</td> </tr> <tr> <td>R3</td> <td>Dronning</td> </tr> <tr> <td>R4</td> <td>Peninsula</td> </tr> <tr> <td>R5</td> <td>Ronne</td> </tr> <tr> <td>R6</td> <td>Ross</td> </tr> <tr> <td>R7</td> <td>Victoria</td> </tr> <tr> <td>R8</td> <td>Wilkes</td> </tr> </tbody> </table> <table> <caption> <p> </p> <p>Ice shelves</p> </caption> <tbody> <tr> <td>S115</td> <td>Amery</td> </tr> <tr> <td>S102</td> <td>West</td> </tr> <tr> <td>S104</td> <td>Publications</td> </tr> <tr> <td>S53</td> <td>Venable</td> </tr> <tr> <td>S110</td> <td>Abbot</td> </tr> <tr> <td>S54</td> <td>Cosgrove</td> </tr> <tr> <td>S55</td> <td>PIG</td> </tr> <tr> <td>S56</td> <td>Thwaites</td> </tr> <tr> <td>S57</td> <td>Crosson</td> </tr> <tr> <td>S58</td> <td>Dotson</td> </tr> <tr> <td>S60</td> <td>Getz</td> </tr> <tr> <td>S64</td> <td>Nickerson</td> </tr> <tr> <td>S65</td> <td>Sulzberger</td> </tr> <tr> <td>S36</td> <td>Brunt</td> </tr> <tr> <td>S37</td> <td>Riiser-Larsen</td> </tr> <tr> <td>S38</td> <td>Quar</td> </tr> <tr> <td>S39</td> <td>Ekstrom</td> </tr> <tr> <td>S124</td> <td>Atka</td> </tr> <tr> <td>S125</td> <td>Jelbaert</td> </tr> <tr> <td>S121</td> <td>Fimbil</td> </tr> <tr> <td>S123</td> <td>Vigrid</td> </tr> <tr> <td>S122</td> <td>Nivlisen</td> </tr> <tr> <td>S120</td> <td>Lazarez</td> </tr> <tr> <td>S119</td> <td>Borchgrevink</td> </tr> <tr> <td>S118</td> <td>Baudouin</td> </tr> <tr> <td>S158</td> <td>Prince Herald</td> </tr> <tr> <td>S51</td> <td>Scar Inlet</td> </tr> <tr> <td>S8</td> <td>Larsen C</td> </tr> <tr> <td>S6</td> <td>Larsen D</td> </tr> <tr> <td>S7</td> <td>Larsen E</td> </tr> <tr> <td>S45</td> <td>N GVIIS</td> </tr> <tr> <td>S44</td> <td>S GVIIS</td> </tr> <tr> <td>S46</td> <td>Bach</td> </tr> <tr> <td>S52</td> <td>Stange</td> </tr> <tr> <td>S50</td> <td>Wilkins</td> </tr> <tr> <td>S34</td> <td>Ronne</td> </tr> <tr> <td>S35</td> <td>Filchner</td> </tr> <tr> <td>S70</td> <td>Ross West</td> </tr> <tr> <td>S71</td> <td>Ross East</td> </tr> <tr> <td>S91</td> <td>Mertz</td> </tr> <tr> <td>S162</td> <td>Ninnis</td> </tr> <tr> <td>S90</td> <td>Cook</td> </tr> <tr> <td>S86</td> <td>Rennick</td> </tr> <tr> <td>S83</td> <td>Mariner</td> </tr> <tr> <td>S73</td> <td>Drygalski</td> </tr> <tr> <td>S74</td> <td>Nansen</td> </tr> <tr> <td>S114</td> <td>Dibble</td> </tr> <tr> <td>S100</td> <td>Shackleton</td> </tr> <tr> <td>S24</td> <td>Tracy Tremenchus</td> </tr> <tr> <td>S99</td> <td>Conger Glenzer</td> </tr> <tr> <td>S159</td> <td>Vincennes Bay</td> </tr> <tr> <td>S98</td> <td>Totten</td> </tr> <tr> <td>S157</td> <td>Moscow uni</td> </tr> <tr> <td>S156</td> <td>Holmes</td> </tr> </tbody> </table> <p> </p>
MERRA-2 inst1_2d_int_Nx: 2d,1-Hourly,Instantaneous,Single-Level,Assimilation,Vertically Integrated Diagnostics 0.625 x 0.5 degree V5.12.4 (M2I1NXINT) at GES DISC
M2I1NXINT (or inst1_2d_int_Nx) is an instantaneous 2-dimensional hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of vertically integrated diagnostics, such as kinetic energy, virtual potential temperature, and total precipitable water (or ice, liquid, and vapor). The timestamp of a data field is on each hour starting from 00:00 UTC, e.g.: 00:00, 01:00, … , 23:00 UTC. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file.MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.Questions: If you have a question, please read "MERRA-2 File Specification Document", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).
MERRA-2 tavg3_3d_nav_Ne: 3d,3-Hourly,Time-Averaged, Vertical Coordinates 0.625 x 0.5 degree V5.12.4 (M2T3NENAV) at GES DISC
M2T3NENAV (or tavg3_3d_nav_Ne) is a 3-dimensional 3-hourly time-averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of vertical coordinates of the 73 model layer edges. The parameters include edge pressure and edge heights. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, … , 22:30 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=73 is for the bottom (or surface) model layer edge. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file.MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.Questions: If you have a question, please read "MERRA-2 File Specification Document", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).
MERRA-2 tavgM_2d_chm_Nx: 2d,Monthly mean,Time-Averaged,Single-Level,Assimilation,Carbon Monoxide and Ozone Diagnostics 0.625 x 0.5 degree V5.12.4 (M2TMNXCHM) at GES DISC
M2TMNXCHM (or tavgM_2d_chm_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated carbon monoxide and ozone diagnostics, such as properties of carbon monoxide (column burden, emission, chemical production, and surface concentration), and total column ozone. The collection also includes variance of certain parameters. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file.MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.Questions: If you have a question, please read "MERRA-2 File Specification Document", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).
MERRA-2 tavgU_3d_odt_Np: 3d,diurnal,Time-Averaged,Pressure-Level,Assimilation,Ozone Tendencies 0.625 x 0.5 degree V5.12.4 (M2TUNPODT) at GES DISC
M2TUNPODT (or tavgU_3d_odt_Np) is a 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of ozone tendencies on the 42 pressure levels, such as total ozone analysis tendency, tendency of odd oxygen mixing ratio due to chemistry, tendency of odd oxygen due to moist processes, and tendency of ozone due to dynamics. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. This data collection is the monthly mean of data fields for each 3-hour and time-stamped at the central time starting from 01:30 UTC, e.g.: 01:30, 04:30, … , 22:30 UTC.MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file.MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.Questions: If you have a question, please read "MERRA-2 File Specification Document", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).
MERRA-2 tavg3_3d_odt_Np: 3d,3-Hourly,Time-Averaged,Pressure-Level,Assimilation,Ozone Tendencies 0.625 x 0.5 degree V5.12.4 (M2T3NPODT) at GES DISC
M2T3NPODT (or tavg3_3d_odt_Np) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of ozone tendencies on the 42 pressure levels, such as total ozone analysis tendency, tendency of odd oxygen mixing ratio due to chemistry, tendency of odd oxygen due to moist processes, and tendency of ozone due to dynamics. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, … , 22:30 UTC. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file.MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.Questions: If you have a question, please read "MERRA-2 File Specification Document", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).
MERRA-2 instM_2d_lfo_Nx: 2d,Monthly mean,Instantaneous,Single-Level,Assimilation,Land Surface Forcings 0.625 x 0.5 degree V5.12.4 (M2IMNXLFO) at GES DISC
M2IMNXLFO (or instM_2d_lfo_Nx) is an instantaneous 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of land surface forcing parameters, such as height, specific humidity, wind, and air temperature of the model surface layer. The collection also includes variance of certain parameters.MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file.MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.Questions: If you have a question, please read "MERRA-2 File Specification Document", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).
MERRA-2 tavgM_2d_glc_Nx: 2d,Monthly mean,Time-Averaged,Single-Level,Assimilation,Land Ice Surface Diagnostics 0.625 x 0.5 degree V5.12.4 (M2TMNXGLC) at GES DISC
M2TMNXGLC (or tavgM_2d_glc_Nx) is a 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated land ice surface diagnostics at the single levels, such as fractional area of glaciated surface snow cover, snow mass over glaciated surface, snow depth over glaciated surface, and total snow mass residual due to densification. The collection also includes variance of certain parameters.MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file.MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.Questions: If you have a question, please read "MERRA-2 File Specification Document", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).
MERRA-2 tavgM_3d_qdt_Np: 3d,Monthly mean,Time-Averaged,Pressure-Level,Assimilation,Moist Tendencies 0.625 x 0.5 degree V5.12.4 (M2TMNPQDT) at GES DISC
M2TMNPQDT (or tavgM_3d_qdt_Np) is a 3-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of moist tendencies on the 42 pressure levels, such as tendency of ice (or liquid) water due to dynamics, total ice (or liquid) water tendency due to moist, total specific humidity analysis tendency, and specific humidity tendency due to moist. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. The collection also includes variance of certain parameters.MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file.MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.Questions: If you have a question, please read "MERRA-2 File Specification Document", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).
MERRA-2 tavgU_3d_tdt_Np: 3d,diurnal,Time-Averaged,Pressure-Level,Assimilation,Temperature Tendencies 0.625 x 0.5 degree V5.12.4 (M2TUNPTDT) at GES DISC
M2TUNPTDT (or tavgU_3d_tdt_Np) is a 3-dimensional monthly diurnal means data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of air temperature tendencies on 42 pressure levels, such as total temperature analysis tendency, and tendency of air temperature due to dynamics (or friction, moisture, radiation, and physics). The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. This data collection is the monthly mean of data fields for each 3-hour and time-stamped at the central time starting from 01:30 UTC, e.g.: 01:30, 04:30, … , 22:30 UTC.MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file.MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.Questions: If you have a question, please read "MERRA-2 File Specification Document", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).
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.