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3,592 results for “Grid”
LAI_TS_Val: LAI time-series validation datasets in the 1-km pixel grid at global scale from 2001 to 2011
<p>Leaf area index (LAI), which is defined as one half of the total green leaf area per unit ground surface area, is a critical structural variable for quantifying the exchange processes of energy and matter between the land surface and atmosphere, it is thus identified as a key parameter in most terrestrial ecosystem models. To acquire long-term LAI records at the global scale, several remote sensing LAI products have been generated from various satellite sensors. However, assessing the uncertainties associated with these LAI products through comparisons with independent ground-truth measurements is pivotal for an effective application of products. Many sites from global networks have collected and provided invaluable ground LAI measurements covering a wide range of biome types and spatial variabilities. These site-based LAI measurements have been obtained about 30 years (1990-now). However, the spatial scale mismatch between site and pixel observations restricts the utilization of LAI measurements for product time-series validation. This datasets were generated from site-based LAI measurements of FLUXET and Chinese Ecosystem Research Network (CERN), using the proposed GUGM (Grading and Upscaling of Ground Measurements) method to resolve the scale-mismatch issue between site and sensor observations and maximize the utility of time-series of site-based LAI measurements, which can achieve the goal of product time-series validation. This GUGM approach first ingests both high-resolution images and site-based LAI measurements to capture the spatiotemporal variability in the product pixel grid. Then, a strategy was employed to grade the spatial representativeness of LAI measurements in the product pixel grid. For those LAI measurements which cannot be directly used in the validation of products, a strategy was adopted to calculate the spatial upscaling coefficient based on site-based LAI measurements and aggregated high-resolution reference maps to derive reliable LAI time-series validation datasets. The GUGM method has been applied to the site-based LAI measurements to generate global time-series LAI validation datasets from 2001 to 2011 in the 1 km pixel grid. The datasets include 28 sites which are mainly located in North America and Asia, providing 924 validation data in total. Among these sites, 16 sites with 508 (55.0%) validation data were obtained for forest, while 11 sites with 341 (36.9%) validation data and one site with 75 (8.1%) were obtained for crops and grasses, respectively. This datasets were saved in two formats: *.xls and *.kmz and each format was zipped for 63 KB and 31 KB, respectively.</p>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL soybean simulations
<p>This data set contains output data from simulations with the model LPJmL for soybean as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, plant day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season').</p> <p>Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.</p>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL rice simulations
<p>This data set contains output data from simulations with the model LPJmL for rice as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, plant day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season').</p> <p>Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.</p>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL maize simulations
<p>This data set contains output data from simulations with the model LPJmL for maize as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, plant day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season').</p> <p>Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.</p>
Global monthly catches from tuna fisheries by 1° and 5° grids (1950-2023) (FIRMS level 0)
<p>We compiled a comprehensive dataset of geo-referenced catches from global tuna fisheries available at a spatial resolution of 1° and 5° grid areas. This dataset was created by harmonizing public domain data from the five tuna Regional Fisheries Management Organizations (t-RFMOs) for the period 1950-2023. Under the auspices of the Fisheries and Resources Monitoring System (FIRMS) of the United Nations Food and Agriculture Organization (FAO), we developed a systematic data flow process in collaboration with the t-RFMO Secretariats. This process involved the implementation of a data exchange format adhering to the standards of the FAO Coordinating Working Party on Fishery Statistics (CWP), facilitating the seamless integration of data into the dataset.<br><br>Geo-referenced catch data from tuna fisheries are reported in either the number of fish or live-weight equivalent (metric tonnes), with some strata providing catches in both units. The catches primarily represent the quantities of retained fish either landed or transhipped at sea and in ports. The data are stratified by year, month, fishing fleet, fishing gear, fishing mode, 1° or 5° grid area of longitude and latitude, and taxon.<br><br>The dataset encompasses 49 medium- and large-sized pelagic species found in both neritic and oceanic habitats of the world's oceans. This includes 15 species of tunas, 10 species of billfish, 7 species of Spanish mackerels, 2 species of bonitos, and wahoo. Despite uncertainties and incomplete data due to under-reporting, the dataset also includes reported catches for 14 species of pelagic sharks and rays that may be either targeted or incidentally caught in tuna and tuna-like fisheries.<br><br>The dataset serves as a benchmark for the monitoring and assessment of both artisanal and industrial fisheries from over 115 fishing fleets across 114 countries that have exploited tuna and tuna-like species for subsistence and commercial purposes over more than seven decades.</p>
Global monthly catches from tuna fisheries by 5° grid (1950-2023) (FIRMS level 0)
We compiled a comprehensive dataset of geo-referenced catches from global tuna fisheries available on a spatial resolution of 5° grid areas. This dataset was created by harmonizing public domain data from the five tuna Regional Fisheries Management Organizations (t-RFMOs) for the period 1950-2023. Under the auspices of the Fisheries and Resources Monitoring System (FIRMS) of the United Nations Food and Agriculture Organization (FAO), we developed a systematic data flow process in collaboration with the t-RFMO Secretariats. This process involved the implementation of a data exchange format adhering to the standards of the FAO Coordinating Working Party on Fishery Statistics (CWP), facilitating the seamless integration of data into the dataset.<br><br>Geo-referenced catch data from tuna fisheries are reported in either the number of fish or live-weight equivalent (metric tonnes), with some strata providing catches in both units. The catches primarily represent the quantities of retained fish either landed or transhipped at sea and in ports. The data are stratified by year, month, fishing fleet, fishing gear, fishing mode, 5° grid area of longitude and latitude, and taxon.<br><br>The dataset encompasses 49 medium- and large-sized pelagic species found in both neritic and oceanic habitats of the world's oceans. This includes 15 species of tunas, 10 species of billfish, 7 species of Spanish mackerels, 2 species of bonitos, and wahoo. Despite uncertainties and incomplete data due to under-reporting, the dataset also includes reported catches for 14 species of pelagic sharks and rays that may be either targeted or incidentally caught in tuna and tuna-like fisheries.<br><br>The dataset serves as a benchmark for the monitoring and assessment of both artisanal and industrial fisheries from over 115 fishing fleets across 114 countries that have exploited tuna and tuna-like species for subsistence and commercial purposes over more than seven decades.
A 20-year (1998-2017) global sea surface dimethyl sulfide gridded dataset with daily resolution
<p>This dataset contains (1) the matched and binned data used for constructing an artificial neural network (ANN) ensemble model to simulate the sea surface concentration of dimethyl sulfide (DMS); (2) the simulated global daily sea surface concentrations of DMS ranging from 1998 to 2017 by ANN model and the calculated total transfer velocities (Kt) and sea-to-air fluxes. The input variables of this ANN ensemble model include chlorophyll <em>a</em>, sea surface temperature (SST), mixed layer depth (MLD), nitrate, phosphate, silicate, dissolved oxygen (DO), downward short-wave radiation (DSWF), and sea surface salinity (SSS). The spatial resolution of the simulated dataset is 1°×1°. The units of DMS concentration, Kt, and flux are nmol L–1, m d–1, and μmol S m–2 d–1, respectively.</p> <p><strong>Update Note</strong></p> <ol> <li>In Version 4.0 and earlier versions, the sea ice cover data (from the OISST dataset) used to calculate Kt and DMS flux contained certain time periods with completely missing values, which were incorrectly replaced with zeros. This led to a significant overestimation of Kt and DMS flux in polar regions where sea ice coverage exists. The missing data periods include: November 27–28, 2011; January 7–9, 2016; April 18 to June 30, 2016; and January 7 to February 28, 2017. This issue was resolved beginning with Version 5.0 through the use of updated sea ice data.</li> <li>Compared to Version 5.0, the current version introduces a correction to a bug in the DMS concentration simulation. When input data were missing—primarily in polar regions—the DMS concentration should have been flagged as missing. However, it was previously assigned a value of 1.6031 nM in Version 5.0. In the current version, these values are now replaced with -999 to indicate missing data.</li> </ol>
A global gridded CO2 flux dataset inferred from OCO-2 retrievals using the GONGGA inversion system (v2025)
<p><strong>Data Description</strong></p> <p>Here we provide a global monthly CO2 flux dataset at 1° × 1° spatial resolution for the period 2014.9-2024.12. The dataset is generated using the GONGGA (Global ObservatioN-based system for monitoring Greenhouse GAs) inversion system by assimilating OCO-2 (Observing Carbon Observatory 2) v11.2r column CO2 retrievals that scaled to the WMO X2019 standard. The dataset contains fluxes from biosphere (Net Ecosystem Exchange, NEE) (both prior and posterior), ocean (both prior and posterior), biomass burning emissions and fossil fuel emissions.</p> <p>We also provide the posterior model simulated values corresponding to all measurements contained in the lastest release of NOAA’s ObsPack database (obspack_co2_1_GLOBALVIEWplus_v10.1_2024-11-13 and obspack_co2_1_NRT_v10.1_2025-02-07).</p> <p><strong>Change from v2024</strong></p> <ul> <li>Assimilation of OCO-2 v11.2r retrievals</li> <li>Update of prior fluxes</li> </ul> <p><strong>Data version specification</strong></p> <p>v202x.ori refers to original GONGGA flux data with 3-hourly time resolution and 2° latitude × 2.5° longitude spatial resolution, v202x refers to GONGGA flux data resampled to monthly time resolution and 1° latitude × 1° longitude spatial resolution for facilitating comparisons with other GCP inversion results.</p> <p><strong>Article citation</strong></p> <p>Jin, Z., Wang, T., Zhang, H., Wang, Y., Ding, J., Tian, X., Constraint of satellite CO2 retrieval on the global carbon cycle from a Chinese atmospheric inversion system. Science China Earth Sciences, 2023, 66: 609-618, doi: 10.1007/s11430-022-1036-7.</p> <p>Jin, Z., Tian, X., Wang, Y., Zhang, H., Zhao, M., Wang, T., Ding, J., and Piao, S.: A global surface CO2 flux dataset (2015–2022) inferred from OCO-2 retrievals using the GONGGA inversion system, Earth System Science Data, 2024, 16: 2857-2876, doi: 10.5194/essd-16-2857-2024.</p>
GO-SHIP Easy Ocean: Formatted and gridded ship-based hydrographic section data
<p><a href="https://www.go-ship.org">GO-SHIP</a> (The Global Ocean Ship-based Hydrographic Investigations Program) has developed the protocols and methods to generate a data product that concatenates all occupations of individual sections into a time-series; the GO-SHIP Easy Ocean. Here we provide access to the analysis-ready gridded GO-SHIP Easy Ocean product that enhances the accessibility of this unique data set that spans four decades, comprised of more than 40 cross-ocean transects, many with multiple repeats.</p> <p>This product, of uniformly calibrated CTD (temperature, salinity and oxygen) data, provides easy access to and use of the high-quality hydrographic temperature and salinity data that span more than 40 years. The GO-SHIP Easy Ocean product will underpin the quality control of autonomous platforms, provide a ready assessment of ocean-only and coupled climate model simulations, and be used in specific research projects. The GO-SHIP Easy Oceanis a companion to the GLODAP inorganic and carbon product. The section data are available from Zenodo in two standard arrangements: Uninterpolated (reported) and interpolated (gridded). For both arrangements, five quantities are recorded; in situ temperature in ITS-90 scale, in situ salinity in PSS-78 scale, the dissolved oxygen concentration in μmol/kg, Conservative Temperature in °C, and Absolute Salinity in g/kg. The data are available in various formats.</p> <p>Cite <a href="https://doi.org/10.1038/s41597-022-01212-w">Katsumata et al (2022)</a> when using this product and include the following acknowledgment statement in any publication or derived product:</p> <p><em>Data were collected and made publicly available by the International Global Ship-based Hydrographic Investigations Program GO-SHIP (https://www.go-ship.org/) and the national programs that contribute to it.</em></p>
Gridded EPA U.S. Anthropogenic Methane Greenhouse Gas Inventory (gridded GHGI)
<h2><strong>About</strong></h2><p>The gridded EPA U.S. anthropogenic methane greenhouse gas inventory (gridded methane GHGI) includes spatially and temporally resolved (gridded) maps of annual anthropogenic methane emissions (0.1°×0.1°) for the contiguous United States (CONUS). Total gridded methane emissions for each emission source sector are consistent with national annual U.S. anthropogenic methane emissions reported in the U.S. EPA <a href="https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks"><i>Inventory of U.S. Greenhouse Gas Emissions and Sinks</i></a> (U.S. GHGI). More information is available on the <a href="https://www.epa.gov/ghgemissions/gridded-methane-emissions">U.S. EPA website</a>. </p><p>This repository accompanies the peer-reviewed manuscript <a href="https://pubs.acs.org/doi/10.1021/acs.est.3c05138"><i>Maasakkers, et al., 2023</i></a>. Data in this repository are an update to the gridded GHGI version 1, previously described in <a href="https://pubs.acs.org/doi/10.1021/acs.est.6b02878"><i>Maasakkers, et al., 2016</i></a> and available on the <a href="https://www.epa.gov/ghgemissions/gridded-2012-methane-emissions">U.S. EPA website</a>. </p><h4><strong>This repository contains two data products:</strong></h4><ol><li><strong>Gridded GHGI v2 (main product; 2 file types). </strong>Gridded annual U.S. anthropogenic methane emissions for 2012-2018 for 26 source categories (gridded GHGI). This dataset is developed to be consistent with the national U.S. GHGI published in 2020 (<i>U.S. EPA, Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990 - 2020. U.S. Environmental Protection Agency, 2020, EPA 430-R-22-003, </i><a href="https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks-1990-2018"><i>https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks-1990-2018</i></a>).<br><br>This dataset includes 2 file types: <br>a. Annual methane emission fluxes for 26 inventory source categories. Files contain one year of emissions per source category and include a time dimension variable to make the data suitable (COARDS-compliant) for atmospheric models.<br> (Dimensions: latitude x longitude x time; units: molecules CH4 cm-2 s-1):<br><i> - Gridded_GHGI_Methane_v2_YYYY.nc</i><br><br>b. Monthly emission scaling factors for inventory source categories with strong interannual variability (see 'Data Details' below). To use these factors to calculate absolute monthly methane emission fluxes, multiply the scaling factors for each relevant source category by the corresponding emission fluxes in the annual flux files.<br> (Dimensions: latitude x longitude x month; units: dimensionless): <br> - <i>Gridded_GHGI_Methane_v2_Monthly_Scale_Factors_YYYY.nc</i><br> </li><li><strong>Gridded GHGI v2 Express Extension (1 file type).</strong> The v2 Express Extension includes gridded annual U.S. anthropogenic methane emissions for 2012-2020 for 27 source categories (one additional source category compared to the main v2 dataset above). This dataset is developed to be consistent with total methane emissions from the U.S. GHGI published in 2022 (<i>EPA (2022) Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020. U.S. Environmental Protection Agency, EPA 430-R-22-003. </i><a href="https://www.epa.gov/ghgemissions/draft-inventory-us-greenhouse-gas-emissionsand-sinks-1990-2020"><i>https://www.epa.gov/ghgemissions/draft-inventory-us-greenhouse-gas-emissionsand-sinks-1990-2020</i></a><i>)</i>. <br><br><i>**Note**:</i><strong> </strong>This dataset is <strong>not</strong> a full update to the main gridded GHGI v2 product. To quickly incorporate more recent national methane emission estimates into gridded products, national methane emissions from a more recent U.S. GHGI were spatially allocated (i.e., gridded) using the annual source-specific spatial emission patterns developed for the 2012-2018 main v2 product. Emissions for years 2019 and 2020 were allocated using 2018 spatial patterns.<br><br>This dataset includes 1 file type:<br>a. Annual emission files<br> (Dimensions: latitude x longitude x time; units: molecules CH4 cm-2 s-1):<br> - <i>Express_Extension_Gridded_GHGI_Methane_v2_YYYY.nc</i></li></ol><p><i>--------------------------------------------------</i></p>
Data for: Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) 2: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for O2 and O3
<p>We present all of the data across our SNR and abundance study for the molecules O2 and O3 for an exoEarth twin. The wavelength range is from 0.515-1 micron, with 25 evenly spaced 20% bandpasses in this range. The SNR ranges from 3-20, and the abundance values range in log space in steps of 0.5 and/or 0.25 (all presented in VMR in the associated table). We present the lower and upper wavelength per bandpass, the input O2 and O3 values (abundance case), the retrieved O2 and O3 values (presented as the log10(VMR)), the lower and upper limits of the 68% credible region (presented as the log10(VMR)), and the log-Bayes factor for O2 and O3. For more information about how these were calculated, please see Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) 2: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for O2 and O3, accepted and currently available on arXiv. </p> <p>To open this csv as a Pandas dataframe, use the following command:</p> <p>your_dataframe_name = pd.read_csv(f'zenodo_table.csv', dtype={'Input O2': str, {'Input O3': str}})</p>
Sistan: part 1. Grid Squares
<h3>Deposit of Grid Squares from the Sistan region (Iran, Afghanistan) maintained within the EAMENA database.</h3> <p>These Grid Squares cover the central region of the Sistan region (Iran, Afghanistan) and represent a subset of the EAMENA grid system. Each square measures 0.25 degrees in both longitude and latitude, spanning the Middle East and North Africa (MENA) region from Afghanistan to Mauritania. These Grid Squares are the one surveyed to create the Sistan: part 1. Heritage Places. Their spatial coverage covers 16,875 km2 with these boundaries:</p> <ul> <li>Northern boundary: + 31.5</li> <li>Southern boundary: + 30</li> <li>Eastern boundary: + 62.5</li> <li>Western boundary: - 60.5</li> </ul> <p>Their geographic centre is the Iranian city of Zabol (زابل)</p> <p>---</p> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div>The two repositories (<a href="../records/10375902">Heritage Places</a> and Grid Squares) both pertain to Part 1 of the Sistan survey. This dataset primarily focuses on the central areas of Sistan, but it can be expanded through future surveys to include the eastern parts of Sistan in Afghanistan and additional undocumented areas in Iran.</div> </div> </div> </div> </div> </div> </div> </div> <div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> </div> </div> </div> </div> </div>
Madrid Grid Area Buildings + Reachable Endpoints for Given RF Transmitter Location and Parameters, with and without RISs Installation.
<p>1- The obstacles_save folder contains arrays defining the vertices locations (x,y) of buildings in the considered area in Madrid Grid.</p> <p>A transmitter is placed at the center of a square at location [600, 900]. Possible receiver (or relay trasnceivers) locations are defined as the vertices (i.e., corners) of buildings (from previous list). The goal of the simulation is to find how many hops are needed to reach, if possible, each location from the previously mentioned list of vertices, assuming a maximum allowed path loss value of 90 dB between any two consecutive hops. </p> <p>2- The arrays in no_ris specify the vertices reachable within N sucessive hops, when no RIS is installed in the area.</p> <p>3- Similarly, the arrays in double_ris give the coordinates of vertices reachable with N hops when a two RISs are installed in the middle square(as shown in related paper).</p> <p>The RIS beamforming gain is 20 dB (in Table 1 in the paper the gain should be 20 not 15 dB).</p>
Soil grid data for 3 agricultural fields in Italy (Soil Moisture, soil organic carbon)
<p><span>Soil data collected in an agricultural area with annual crops in Italy (west-central Lombardia Po Valley, province of Pavia). The data refers to soil properties of 320 soil samples for Soil Moisture and 120 for SOC, collected in the topsoil (around 5-10 cm), considering a regular sampling grid, within three agricultural field with different crops (spring-summer cycle) and soils type, Rice-Loamy, Sorghum-Sandy and, Maize-Clay, in a period (before the seeding of the crops), when the soil was bare, in the framework of the EJP Steropes project.</span></p> <p><span>The aim of the collected dataset was to be able to analyse the influence of soil moisture in SOC (WP2 of the STEROPES project) prediction models from remote sensing.</span></p> <p><span>Data in the form of shape file (one shapefile for each agricultural field, for oth Soil Moisture and SOC), and pictures of the soil surface in .jpg format. </span></p>
Data from: Electron Populations and Neutralization Process in the Plume of a Gridded Ion Thruster
<h2>Data from: Electron Populations and Neutralization Process in the Plume of a Gridded Ion Thruster</h2> <ul> <li>Authors: Matteo Guaita, Alberto Marín-Cebrián, Eduardo Ahedo, Mario Merino, Fabrice Cipriani, Käthe Dannenmayer</li> <li>Contact email: mguaita@pa.uc3m.es</li> <li>Date: 15/11/2024</li> <li>Keywords: Plasma Physics, Plasma Plumes, Gridded Ion Thruster, Cathode, Facility Effects, Particel in Cell</li> <li>Version: 1.0.0</li> <li>Digital Object Identifier (DOI): 10.5281/zenodo.14165272</li> <li>License: This dataset is made available under the <a href="http://opendatacommons.org/licenses/by/1.0/" target="_blank" rel="noopener">Open Data Commons Attribution License</a></li> </ul> <h2>Abstract</h2> <p>This dataset contains the data from the simulations presented in the article submitted for pubblicaiton in the Journal: Plasma Sources Science and Technology (PSST):</p> <p>"Electron Populations and Neutralization Process in the Plume of a Gridded Ion Thruster"</p> <p>The data in this repository is the result of several hybrid PIC simulations as described in the reference. For further information on the setup, numerical parameters and physical meaning of the simulations please refer to the article</p> <h2>Dataset description</h2> <p>The simulations that produced the datasets in this repository were run with the full PIC code Picaso. The majority of the data is at steady-state, and has been averaged over the last 7000 simulation time-steps to reduce numerical noise. This averaging has been performed as a first step directly by the code through time-step accumulation techniques, and at a later stage in post-processing by averaging over the last 20 print-outs of the code. The data inside the "time_dependent" folder is instead time-varying.</p> <h2>Data files</h2> <p>Each HDF5 data-group contains the mesh and time coordinates and plasma properties of a specific simulation. In particular, the naming convention is the following:</p> <ul> <li><strong>Ref_planar.hdf5: </strong>Contains the results of the "reference planar simulation" presented in Sections III and IV of the article.</li> <li><strong>2Te_planar.hdf5: </strong>Contains the results of the simulation with a doubled electron temperature at the cathode presented in Section V of the article.</li> <li><strong>2Ie_planar.hdf5: </strong>Contains the results of the simulation with a doubled electron current at the cathode presented in Section V of the article.</li> <li><strong>No_coll_planar.hdf5: </strong>Contains the results of the simulation without inelastic electron collisions presented in Section V of the article.</li> <li><strong>Ref_axisym.hdf5: </strong>Contains the results of the non-accelerated axis-symmetric simulation presented in Section VI of the article</li> <li><strong>fcol_2.5_axisym.hdf5: </strong>Contains the results of the axis-symmetric simulation,accelerated by a factor 2.5, presented in Section VI of the article</li> <li><strong>fcol_5_axisym.hdf5: </strong>Contains the results of the axis-symmetric simulation,accelerated by a factor 5, presented in Section VI of the article</li> <li><strong>fcol_7.5_axisym.hdf5: </strong>Contains the results of the axis-symmetric simulation,accelerated by a factor 7.5, presented in Section VI of the article</li> <li><strong>fcol_10_axisym.hdf5: </strong>Contains the results of the axis-symmetric simulation,accelerated by a factor 10, presented in Section VI of the article</li> </ul> <p>In each of these files the data is organized in a series of subfolders:</p> <ul> <li><strong>Electrons_prim: </strong>Contains the steady-state properties of primary electrons</li> <li><strong>Electrons_trap: </strong>Contains the steady-state properties of trapped electrons</li> <li><strong>Ions_fast: </strong>Contains the steady-state properties of fast ions (ions injected through the thruster grids)</li> <li><strong>Ions_slow: </strong>Contains the steady-state properties of slow ions (ions produced by collisions in the plume)</li> <li><strong>Time_dependent: </strong>Contains the vector of time-stamps and spatially global data saved at the corresponding time</li> </ul> <p>The data files found in the outer simulation folder are:</p> <ul> <li><strong>xs:</strong> Physical x coordinates [cm]</li> <li><strong>zs:</strong> Physical z coordinates [cm]</li> <li><strong>phi: </strong>electric potential [V]</li> <li><strong>rho_el: </strong>space charge density [C/m³]</li> <li><strong>nn: </strong>Total neutral density [1/m³]</li> </ul> <p>The data files for each particle population are:</p> <ul> <li><strong>n: </strong>Plasma (ion) density [1/m³]</li> <li><strong>f_x: </strong>Particle flux along x [1/(m² s)]</li> <li><strong>f_y: </strong>Particle flux along y [1/(m² s)]</li> <li><strong>f_z: </strong>Particle flux along z [1/(m² s)]</li> <li><strong>p_xx: </strong>xx component of the pressure tensor [J/m³]</li> <li><strong>p_yy: </strong>yy component of the pressure tensor [J/m³]</li> <li><strong>p_zz: </strong>zz component of the pressure tensor [J/m³]</li> </ul> <p>The data files in the time dependent folder are:</p> <ul> <li><strong>t: </strong>Time coordinates [s]</li> <li><strong>phi_W: </strong>Potential of the vacuum chamber walls [V]</li> <li><strong>phi_max:</strong> Maximum value of the potential in the plume [V]</li> <li><strong>nte_frac: </strong>Fraction between the number of trapped electrons and ions in the plume bulk [%]</li> <li><strong>nu_te_ela: </strong>globally averaged trapped electron-neutral elastic collision frequency [Hz]</li> <li><strong>nu_te_ion: </strong>globally averaged trapped electron-neutral ionization collision frequency [Hz]</li> <li><strong>nu_te_exc: </strong>globally averaged trapped electron-neutral excitation collision frequency [Hz]</li> <li><strong>nu_te_cou: </strong>globally averaged trapped electron-neutral Coulomb collision frequency [Hz]</li> <li><strong>nu_pe_ela: </strong>globally averaged primary electron-neutral elastic collision frequency [Hz]</li> <li><strong>nu_pe_ion: </strong>globally averaged primary electron-neutral ionization collision frequency [Hz]</li> <li><strong>nu_pe_exc: </strong>globally averaged primary electron-neutral excitation collision frequency [Hz]</li> <li><strong>nu_pe_cou: </strong>globally averaged primary electron-neutral Coulomb collision frequency [Hz]</li> </ul> <p> </p> <p>Note that all the other quantities shown in the article may be obtained from the ones saved here. We remind here that the gas employed is Xenon and that all ions are considered to be singly charged.</p> <h2>Citation</h2> <p>Any works using this dataset or any part of it in any form shall cite it as follows. The BibTeX entry s provided for convenience:</p> <p>@dataset{sim_data_guai25b,<br> author = {Matteo Guaita and Alberto Marín-Cebrián and Mario Merino and Eduardo Ahedo and Fabrice Cipriani and Käthe Dannenmayer},<br> title = {Data from: Electron populations and neutralization process in the plume of a gridded ion thruster},<br> month = November,<br> year = 2024,<br> publisher = {Zenodo},<br> version = {1.0.1},<br> doi = {10.5281/zenodo.14165272},<br> url = {https://doi.org/10.5281/zenodo.12751281}<br>}</p> <p>The journal article associated with this data-set shall also be cited as follows:</p> <p>@article{guai25b,<br> doi = {10.1088/1361-6595/adc482},<br> year = {2025},<br> month = {mar},<br> publisher = {IOP Publishing},<br> author = {Matteo Guaita and Alberto Marín-Cebrián and Mario Merino and Eduardo Ahedo and Fabrice Cipriani and Käthe Dannenmayer},<br> title = {Electron populations and neutralization process in the plume of a gridded ion thruster},<br> journal = {Plasma Sources Science and Technology },<br>}</p> <p> </p> <p><br><br></p> <h2>Acknowledgments</h2> <p>This work, and the corresponding dataset, has been supported by the ECOMODIS project, funded by the European Space Agency, under contract 4000137869/22/NL/RA</p>
CLaMS mean age of air tracers for 15/01/2011 interpolated on simulated CAIRT retrieval grid
<p>The dataset contains simulation results from the Chemical Lagrangian Model of the Stratosphere (CLaMS) for January 15, 2011. These results are interpolated onto the simulated retrieval grid of the Changing-Atmosphere Infrared Tomography Explorer (CAIRT). The data includes six trace gases (SF₆, N₂O, CFC-11 (F11), CFC-12 (F12), HCFC-22 (F22), and CH₄) and the "exact" model mean age of air (BA). The file is provided in NetCDF format.</p>
Hydrographic gridded data set for the South Brazil Bight and Southern Brazilian Shelf
<p>This dataset includes climatological and seasonal maps, spanning data from 1972 to 2024, across 8 different depth levels: 5, 10, 25, 50, 100, 200, 500, and 1000 dBar, with a spatial resolution of 10 km. The maps were generated using the griddata function with triangulation-based natural neighbor interpolation. The variables included in this dataset are conservative temperature (°C), absolute salinity (g kg⁻¹), neutral density (kg m⁻³), dissolved oxygen (mL L⁻¹), total alkalinity (µmol kg⁻¹), total dissolved inorganic carbon (µmol kg⁻¹), pH (total scale), partial pressure of carbon dioxide (µatm), nitrate (µmol kg⁻¹), and phosphate (µmol kg⁻¹). The name description of each variable is provided in the readme_DatasetATLAS.txt. The dataset can be directly accessed using Ocean Data View (ODV) software. </p> <p> </p>
Century Long Reconstruction of Gridded Phosphorus Surplus Across Europe (1850-2019)
<h4><strong>Publication</strong></h4> <p>Please cite this publication if you use the dataset:</p> <p>Batool, M., Sarrazin, F. J. and Kumar, R. Century Long Reconstruction of Gridded Phosphorus Surplus Across Europe (1850-2019), submitted to Earth System Science Data.</p> <p>Please also refer to the above publication for methodological details.</p> <h4><strong>License</strong></h4> <p>The "Century Long Reconstruction of Gridded Phosphorus Surplus Across Europe (1850-2019)" is freely available under an Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0, https://creativecommons.org/licenses/by-nc-sa/4.0), in compliance with the terms of use of the Food and Agriculture Organization of the United Nations (FAO).</p> <p><strong>Data description (v1):</strong></p> <p>This dataset consists of annual long-term reconstruction of total P surplus (both agricultural and non-agricultural soils) across Europe at a 5 arcmin spatial resolution for the period 1850 to 2019. The dataset consists of 48 P surplus estimates that account for the uncertainties resulting from methodological choices and coefficients in major components of the P surplus. This dataset offers the flexibility of aggregating the P surplus at any spatial scale of relevance to support water and land management strategies. Notably, our P surplus dataset has been developed consistently with our N surplus dataset (Batool et al. 2022), enabling joint analysis of N and P budgets across Europe, thereby facilitating holistic nutrient management studies.<br> </p> <p>1. Gridded P surplus data (NetCDF format): 48 files, each of them containing 170 years (1850-2019) of gridded data P surplus</p> <p>2. Aggregated P surplus at European NUTS level (csv format) : 3 files (NUTS 1, NUTS 2, NUTS 3), each of them containing 170 years (1850-2019) of P surplus (mean and standard deviation of 48 estimates). Additionally, a readme file is provided for the NUTS ID's.</p> <p>3. Aggregated P surplus at European river basins (CSV format): 1 file, each of them containing 170 years (1850-2019) of P surplus (mean and standard deviation of 48 estimates). Additionally, a readme file is provided for the river basin ID's.</p> <p>Unit: kg/ha/yr (ha = physical area of grid cell/NUTS/river basins)</p> <p>Time period: 1850-2019</p> <h4><strong>Data description (v2):</strong></h4> <p>We have updated the dataset (v1) and creared v2, which includes improvements and additional components for a more comprehensive phosphorus surplus analysis across Europe. The updates are as follows:</p> <ul> <li><strong>Refined P input estimates from mineral fertilizers (1850–1960):</strong> We have revised our methodology for historical P inputs from mineral fertilizers. In the revised estimates, instead of relying on nitrogen (N) fertilizer trends as a proxy for changes in P fertilizer, we have now incorporated a global dataset that traces the historical sources of phosphorus fertilizers from phosphate rock (1800–2000). This dataset provides a more reliable temporal trend for P fertilizer use. </li> <li><strong>Exclusion of chemical weathering inputs to urban areas:</strong> This adjustment better reflects phosphorus dynamics in urban regions.</li> <li><strong>Expanded data components:</strong> In addition to P surplus, the dataset now includes detailed estimates of P inputs (e.g., mineral fertilizers, manure) and P outputs , offering a more granular view of phosphorus flows.</li> </ul> <ol> <li>Gridded datasets <ol> <li>Total P surplus data (NetCDF format): 48 files, each of them containing 170 years (1850-2019) of gridded P surplus data</li> <li>Total P inputs data (NetCDF format): 1 file, containing 170 years (1850-2019) of gridded P inputs data</li> <li>Total P output data (NetCDF format): 1 file, containing 170 years (1850-2019) of gridded P outputs data</li> <li>P fertilizer (NetCDF format): 2 files, each of them containing 170 years (1850-2019) of gridded P inputs from mineral fertilizer data</li> <li>P animal manure (NetCDF format): 6 files, each of them containing 170 years (1850-2019) of gridded P inputs from animal manure data</li> </ol> </li> <li> <p>Aggregated P surplus at European NUTS level (csv format) : 3 files (NUTS 1, NUTS 2, NUTS 3), each of them containing 170 years (1850-2019) of P surplus (mean and standard deviation of 48 estimates). Additionally, a readme file is provided for the NUTS ID's.</p> </li> <li> <p>Aggregated P surplus at European river basins (CSV format): 1 file, each of them containing 170 years (1850-2019) of P surplus (mean and standard deviation of 48 estimates). Additionally, a readme file is provided for the river basin ID's.</p> </li> </ol> <p>Unit: kg/ha/yr (ha = physical area of grid cell/NUTS/river basins)</p> <p>Time period: 1850-2019</p> <h4><strong>Acknowledgments and underlying datasets</strong></h4> <p>Partial support for this work was provided by the Global Water Quality Analysis and Service Platform (GlobeWQ) project financed by the German Ministry for Education and Research (grant number 02WGR1527A) and the Development Bank of Saxony, Research Project Funding on Resilient Zero-Pollution Wastewater Systems in Climate Change – Case Study Saxony (Project No. 100669418). We are also thankful to UFZ for providing computing power and technical support to the EVE supercomputing facility. We would like to thank people from various organizations and projects for kindly providing us with the data that were used in this study, which includes among others: FAO, Eurostat, HYDE, and IFA.</p> <h4><strong>Contact</strong></h4> <p>Further queries regarding these datasets can be directed to Masooma Batool (masooma.batool@ufz.de) and Rohini Kumar (rohini.kumar@ufz.de).</p>
Output from the Glacier Energy and Mass Balance (GEMB v1.0) forced with 3-hourly ERA5 fields and gridded to 10km, Greenland and Antarctica 1979-2024
<p>These model output of firn air content (FAC) and surface mass balance (SMB) are from version 1.0 of the open-source Glacier Energy and Mass Balance model. GEMB is a column model of ice sheet and glacier surface-atmospheric energy and mass exchange as well as firn state. GEMB has been integrated into the open-source Ice-Sheet and Sea-level System Model which can be downloaded at https://issm.jpl.nasa.gov/. Here, GEMB is forced with 3-hourly ERA5 output from 1979 through end of 2024. For Greenland and its periphery, the ERA5 surface temperature and downwelling longwave radiation forcing are spatially bias-corrected for each month. All values are adjusted by the difference between the RACMO2.3 and the ERA5 1980-2015 monthly means. The GEMB output is bilinearly interpolated onto a 10km grid, from the native ISSM grid, and the output is given as 5-day output or as monthly.</p>
High-resolution microclimatic grids for the Bohemian Forest Ecosystem
<p>Here, we provide spatially continuous, high-resolution (5 m) microclimate grids covering all 923 km<sup>2</sup> of the Bohemian Forest Ecosystem (BFE), i.e. the complete area of the Šumava (Czech Republic) and Bavarian Forest (Germany) National Parks.</p> <p>To derive these grids, we have established a dense network of 288 microclimatic stations that continuously measured air, near-surface, and soil temperature every 15 minutes from 12th October 2019 to 11th October 2020. We combined the measured microclimate temperature with LiDAR derived land surface topography and forest structure through boosted spatial generalized additive models (GAMs).</p> <p>We validated the resulting microclimatic grids with an independent network of forest weather stations and compared these microclimatic grids with the SoilTemp (soil temperature), ForestTemp (near-ground forest understorey temperature), and downscaled ERA5-Land (air temperature). The developed BFE microclimatic grids were closer to independently measured temperatures than any other alternative and captured high microclimatic variability controlled jointly by land surface topography and forest structure. </p> <p>Our microclimatic grids represent accurate, high-resolution spatial variation of mean annual soil temperature, mean, maximum and minimum air temperature at two heights, and growing degree days at 200 cm. </p> <p><strong>The dataset contains 8 microclimatic grids (GeoTIFF format, coordinate system EPSG 31468, resolution 5 m).</strong></p> <p>Extent: 4587063, 5399139: 4646023, 5451289</p> <p>The name of the file is “name.tif”, where name represents the abbreviation of the microclimatic variable (see names below).</p> <p>The values are in °C (°C d for GDD), the data can be readily imported into standard geographical information system software (e.g., QGIS) or accessed in a statistical software (e.g., R). The datasets do not include colour schemes.</p> <p><strong>Measured variable (depth/height) </strong><br> - Microclimatic variable Abbreviation (Units)</p> <p><strong>Soil temperature (-8 cm) </strong> <br> - Mean temperature = mean temperature T.soil_8_cm.mean (°C)<br> <br><strong>Near-ground air temperature (15 cm) </strong> <br> - Mean temperature = mean temperature T.air_15_cm.mean (°C)<br> - Maximum temperature = 95<sup>th</sup> percentile of daily maximum temperatures T.air_15_cm.max.95p (°C)<br> - Minimum temperature = 5<sup>th</sup> percentile of daily minimum temperatures T.air_15_cm.min.5p (°C)<br> <br><strong>Air temperature (200 cm)</strong> </p> <p> - Mean temperature = mean temperature T.air_200_cm.mean (°C)<br> - Maximum temperature = 95<sup>th</sup> percentile of daily maximum temperatures T.air_200_cm.max.95p (°C)<br> - Minimum temperature = 5<sup>th</sup> percentile of daily minimum temperatures T.air_200_cm.min.5p (°C)<br> - Growing degree days = sum of degree days above base temperature (base 5°C) T.air_200_cm.GDD5 (°C d)</p> <p><strong>BFE_script_and_data.zip</strong> contains</p> <p> - Microclimatic variables, topography and forest structure variables for all stations used for modelling (BFE_data.RData).<br> - Script BFE_microclimate_maps_model_script.R used for statistical modelling and prediction. <br> - microclimate2predict.csv - list of microclimate variables for prediction used in the script.</p> <p>The data used for prediction cannot be made publically available. <br>The LIDAR data can be obtained from Administration of Bavarian Forest National Park and Šumava National Park Administration. The LIDAR-derived topography and forest structure rasters can be obtained upon request from the authors.</p> <p>Detailed description will be available in a manuscript.</p> <p><strong>Version 2</strong> insludes improved rasters, the GeoTIFFs include aplha channel for transparency of pixels with NA and full script used for processing.</p> <p> </p>
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