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7,438 results for “surface”
Portobello Marine Laboratory sea surface temperature time series
<p>This table contains the daily sea surface temperature observations taken at the Portobello Marine Laboratory wharf (LAT: -45.8160, LON: 170.6500). The first column is time in MATLAB datenum format. The second column is daily sea surface temperature recorded at 9am local time. Measurements are recorded to an accuracy of <span class="math-tex">\(\pm\)</span>0.1°C. Missing observations have been assigned the value -999. Additional station details and sampling information can be found in <a href="https://environment.govt.nz/publications/new-zealand-coastal-sea-surface-temperature/">Chiswell and Grant (2018)</a>.</p> <p>We acknowledge the foresight and dedication of the founders of this <em>in situ</em> dataset in the 1950s. We are grateful for all the people involved in the data collection. Notably these include</p> <ul> <li>Doug Mackie (data acquisition and record maintenance)</li> <li>Elizabeth (Betty) Batham who championed the long term climate sampling</li> <li>All the researchers who have assisted with sampling</li> </ul>
MHD Model of Ganymede's Magnetosphere: Predicted OCFB and magnetic footprint surface locations for Juno's flyby
<p>This dataset contains model results from a magnetohydrodynamic (MHD) model of Ganymede's magnetosphere adapted to Juno's PJ34 flyby in 2021. Here we publish coordinates for the predicted location of the open-closed-field line-boundary (OCFB) on Ganymede's surface. Additionally we provide coordinates of Juno's magnetic footprint, namely the surface locations that connect to Juno's trajectory through magnetic field lines.</p> <p>For the surface locations we use a western longitude planetographic coordinate system where 0° longitude is in direction of the y-axis and 90° in direction of the x-axis of the cartesian GPhiO system. The GPhiO system is defined by the primary direction<br> z parallel to Jupiter’s rotation axis, the secondary direction y is pointing towards Jupiter barycenter<br> and x completes the right-handed system approximately in direction of plasma flow.</p> <p><strong>Duling2022_JunoGanymede_modeled_surface_OCFB.txt</strong></p> <p>Columns:</p> <p>Longitude [°]<br> Northern OCFB latitude [°]<br> Southern OCFB latitude [°]</p> <p><strong>Duling2022_JunoGanymede_modeled_magnetic_footprint.txt</strong></p> <p>Columns:</p> <p>Spacecraft time [UTC]<br> Magnetic footprint longitude [°]<br> Magnetic footprint latitude [°]<br> Length of field line between Juno and surface [radii]<br> Length of field line between Juno and surface [km]<br> r coordinate of Juno [radii]<br> Latitude of Juno [°]<br> Longitude of Juno [°]<br> x of Juno in GPhiO [km]<br> y of Juno in GPhiO [km]<br> z of Juno in GPhiO [km]</p> <p><strong>Duling2022_JunoGanymede_surface_map.png</strong></p> <p>A plot that visualizes the data of this repository.</p>
ICESat-2 Arctic Sea Ice Surface Topography from the University of Maryland-Ridge Detection Algorithm: April 2019, 2020, and 2021
<p>This dataset is derived from the ICESat-2 (IS-2) Global Geolocated Photon Height Product (ATL03) using the University of Maryland-Ridge Detection Algorithm (UMD-RDA). The UMD-RDA is applied to ATL03 on a per-shot basis, nominally resulting in elevation measurements at IS-2's maximum along-track resolution of ~0.7 m. From these elevation measurements, the UMD-RDA can measure various sea ice parameters including, but not limited to, individual ridge crests and their respective sail heights, the distance between ridges, and sea ice surface roughness.</p> <p><strong>********Changes in Version 2********</strong></p> <p><em>Version 2 includes a column for time (seconds since 2018-01-01) in all parameter files in addition to longitude, latitude, and parameter value.</em></p> <p><em>The full resolution UMD-RDA derived elevation data was too large to host here, but is available upon request. If you need a particular track or segment for your research please contact me with your request by email: kd</em><em>uncan at umd dot edu</em></p>
Dataset and codes for 'Climatic control on seasonal variations of glacier surface velocity'
<p><strong>This repository contains the codes and processed data used to retrieve 10-day changes in glacier surface velocity over the Western Pamir.</strong></p> <p>The supp_CODES.zip contains all details and codes to use COSI-CORR (<a href="http://www.tectonics.caltech.edu/slip_history/spot_coseis/">http://www.tectonics.caltech.edu/slip_history/spot_coseis/</a>) to process a large batch of satellite images. The images can be downloaded directly via <a href="https://earthexplorer.usgs.gov/">https://earthexplorer.usgs.gov/</a> or <a href="https://scihub.copernicus.eu/">https://scihub.copernicus.eu</a>. Please read the Methods and Data section of the associated manuscript for details.</p> <p> </p> <p>The Matrix_velocities.zip contains, for each of the 48 investigated glaciers, the DEM, X, Y (NANNI_2022_supp_glacier_centreline_DEM_XY_1px_30m_1.txt) as well as a matrix of n*m with m the distance along flow and n the number of time step over which the velocity is calculated (NANNI_2022_supp_glacier_centreline_vel_matrix_1px_30m_1.txt), ans the associated figure that show the multi year velocity changes together with the one year average and the along centreline profiles. An example is shown in the two figures for glacier 48 in the main repository.</p> <p> </p> <p>The NANNI_2022_supp_glacier_characteristics file contains the glacier characteristics (48*8), as shown in the associated figures.</p> <p> </p> <p>The NANNI_2022_supp_pickedpoints_migration_AUTUMN/SPRING contains the automatically picked points for the onset of the acceleration in Spring and Autmun for each glacier. The headers contains the information, and the files contains is shown in the associated figure.</p> <p>the temperature profiles used to calculate the Iso 0C are in NANNI_2022_supp_temp_perday_fedchenko_2400m</p> <p>The position of each 48 glacier is shown in the associated figure.</p> <p> </p> <p>You can also find the processed velocity fields (velocity magnitude) under the different path an row: p151r33.zip and p152r33.zip for Landsat8, T42SYJ.zip and T43SBD.zip for Sentinel 2. In these folder you will a find a .tif file names similar to:</p> <p><em>Working_cosicorr_windows_FCorr_16days_p152r33_159_175_AB_1101110_Filtered_correlations_p152r33_filtered_abs.tif</em></p> <p>The name of the files gives information about the time span used (16days), the path and raw (p152r33), the data of the slave in DOY from 2013 (159) and of the master (175).</p> <p>The Statistics.zip file contains for each path and row the associated DEM, glacier mask (RGI), median magnitude (ABS), median NS displacement (NS), median EW displacemnt (EW), with the associated median absolute deviation (MAD). The files containing 'bflt' corresponds to the values computed before the filtering procedure, and the one without, after the filtering procedure. </p> <p>The .tif files are not georeferenced, but are all projected on the same grid with a 30m square pixel size on a UTM 33 42N projection.</p> <p> </p> <p> </p> <p>Please contact me for any question.</p> <p> </p> <div class="notranslate"> </div>
diFUME Digital Surface Model V0.2
<p>Description:</p> <p>Digital Surface Model (V0.2) of Basel for diFUME project. Terrain model (DTM) is calculated from the height model of the Basel-Stadt official survey (<a href="http://www.gva.bs.ch">http://www.gva.bs.ch</a>). The building digital surface model (DSM) combines the DTM product with the building heights derived from the 3D city model (<a href="http://www.gva.bs.ch">http://www.gva.bs.ch</a>). These products describe the height in meters above sea level. Tree crown heights (m above ground level) are derived by the airborne Lidar data (2018 campaign) made available by the civil engineering office of Basel-Stadt (<a href="https://www.tiefbauamt.bs.ch/">https://www.tiefbauamt.bs.ch/</a>).</p> <p> </p> <p>Data specifications:</p> <p>CRS: EPSG:32632 - WGS 84 / UTM zone 32N - Projected</p> <p>Spatial Extent: 392120.0,5266860.0 : 395160.0,5269840.0</p> <p>Temporal Extent: 2015 - 2019</p> <p>Units: meters</p> <p>Width: 3040</p> <p>Height: 2980</p> <p>Bands: 1</p> <p>Pixel Size: 1,-1</p> <p>Data type: Float32 - Thirty two bit floating point</p> <p>GDAL Driver Description: GTiff</p> <p>GDAL Driver Metadata: GeoTIFF</p>
Upper lithospheric structure of northeastern Venezuela from joint inversion of surface wave dispersion and receiver functions
<p>Dataset from the publication: <strong>Upper lithospheric structure of northeastern Venezuela from joint inversion of surface wave dispersion and receiver functions</strong>. DOI: <a href="https://doi.org/10.5194/egusphere-2022-230">10.5194/egusphere-2022-230</a></p> <p> </p> <p>Includes: <em><strong>EGFs, Dispersion Curves measurements, RFs, Vs3dmodel and Moho depths</strong></em></p> <p> </p>
Dataset of Scanning Tunneling Microscopy (STM) images of model surfaces for elementary steps in catalytic reactions
<p>STM images presented in the dataset were recorded by the STRAS research group using a Omicron Variable Temperature STM (VT-STM) microscope, in the TASC laboratory of the CNR-IOM in Trieste.</p> <p>This work has been done within the NFFA-DI project funded by the European Union – NextGenerationEU - Missione 4, “Istruzione e Ricerca” – Componente 2, “Dalla ricerca all'impresa” – Linea di investimento 3.1,“Fondo per la realizzazione di un sistema integrato di infrastrutture di ricerca e innovazione” – Azione 3.1.1, “Creazione di nuove IR o potenziamento di quelle esistenti che concorrono agli obiettivi di Eccellenza Scientifica di Horizon Europe e costituzione di reti”.</p>
Dataset- Advancements in surface finish for additive manufacturing of metal parts: A comprehensive review of Plasma Electrolytic Polishing (PEP)
<p>This repository collects all the data (Figures and Tables) presented in the review article "Advancements in surface finish for additive manufacturing of metal parts: A comprehensive review of Plasma Electrolytic Polishing (PEP)"</p>
The tectonic evolution of the Arctic since Pangea breakup: Integrating constraints from surface geology and geophysics with mantle structure
<div>Description of Resources - Shephard et al. (2013)</div> <div> </div> <div>This file provides a detailed description of all of the files that make up the data collection associated with the publication: Shephard, G. E., Müller, R. D., & Seton, M. (2013). The tectonic evolution of the Arctic since Pangea breakup: Integrating constraints from surface geology and geophysics with mantle structure. Earth-Science Reviews, 124(0), 148-183. doi: <a href="https://doi.org/10.1016/j.earscirev.2013.05.012" target="_blank" rel="noopener">10.1016/j.earscirev.2013.05.012</a></div> <div> </div> <div>Note: For information on file formats and what programs to use to interact with various file formats, see "File Formats and Recommended Programs”.</div> <div> </div> <div>Note: This paper is based on a global model (Seton et al., 2012), which should also be referenced if looking globally or regions other than the Arctic or northern Panthalassa.</div> <div> </div> <div>The files that make up the tectonic reconstruction model include:</div> <div>• <strong>Rotations </strong>- This is a global rotation model (based on Seton et al., 2012) that includes the new rotations for the Arctic.</div> <div>* Shephard_etal_ESR2013.rot (373 KB)</div> <div> </div> <div>• <strong>Coastlines </strong>- These are present day coastlines that have been assigned plate reconstruction ids to allow them to be reconstructed using the rotation file.</div> <div>* Shephard_etal_ESR2013_Coastlines.gpml (34.1 MB)</div> <div>* Shephard_etal_ESR2013_Coastlines.txt (3.2 MB)</div> <div>* Shephard_etal_ESR2013_Coastlinesc.kml (6.3 MB; datum - WGS 1984)</div> <div>* Shephard_etal_ESR2013_Coastlines.shp (3.2 MB inc auxiliary files; datum - WGS 1984)</div> <div> </div> <div>• <strong>Static polygons </strong>- These are closed polygons that split present day Earth's surface into regions that can be assigned to a given plate id, and therefore reconstructed back through time using the rotation file. These polygons can be used to cookie-cut and assign plate ids to geometry and raster data (for more information on this feature please visit http://gplates.org or http://earthbyte.org).</div> <div>* Shephard_etal_ESR2013_staticpolygons.gpml (19.4 MB)</div> <div>* Shephard_etal_ESR2013_staticpolygons.txt (2.7 MB)</div> <div>* Shephard_etal_ESR2013_staticpolygons.kml (4.4 MB; datum - WGS 1984)</div> <div>* Shephard_etal_ESR2013_staticpolygons.shp (2.3 MB inc auxiliary files; datum - WGS 1984)</div> <div> </div> <div>• <strong>Plate boundary geometries and resolved topologies</strong> – Resolved topologies comprise ridges, transforms, subduction zones and other plate boundary geometries. These boundaries intersect to form closed plate polygons ('resolved topologies') that are valid at 1 Myr intervals (0-200 Ma). The plate boundary geometries and plate polygons have been assigned plate reconstruction ids to allow them to be reconstructed using the rotation file.</div> <div>* Shephard_etal_ESR2013_platebounds.gpml (27.7 MB) - contains both plate boundaries and resolved topological plate polygons</div> <div>* Resolved topologies:</div> <div>- topology_*.00Ma.txt (20.6 MB)</div> <div>- topology_*.00Ma.shp (12.5 MB inc auxiliary files; datum - WGS 1984)</div> <div> </div> <div> </div> <div>References</div> <div> </div> <div>M. Seton, R.D. Müller, S. Zahirovic, C. Gaina, T.H. Torsvik, G. Shephard, A. Talsma, M. Gurnis, M. Turner, S. Maus, M. Chandler, (2012). Global continental and ocean basin reconstructions since 200 Ma. Earth-Science Reviews, 113(3–4), 212-270. doi:<a href="https://doi.org/10.1016/j.earscirev.2012.03.002" target="_blank" rel="noopener">10.1016/j.earscirev.2012.03.002</a></div>
Burned Area Maps based on MODIS Surface Reflectance
<p>Burned area (BA) was classified using in-house algorithms, described in detail by Woźniak and Aleksandrowicz (2019). This method utilizes images acquired before and after fire events. All MODIS surface reflectance products MOD09A1 (tiles 24_03 and 25_03) for the period 2002 – 2021 were investigated. Since the study area is obscured by clouds or covered with snow for most of the year, only images from the time window that maximized the number of available frames across most years were selected. Hence, only images acquired between the 145th and 241st day of each year (corresponding to the spring-summer period) were retained for further processing. </p>
StreetSurfaceVis: a dataset of street-level imagery with annotations of road surface type and quality
<h1>StreetSurfaceVis</h1> <p><em>StreetSurfaceVis</em> is an image dataset containing <strong>9,122 street-level images from Germany</strong> with labels on <strong>road surface type and quality.</strong> The CSV file <code>streetSurfaceVis_v1_0.csv</code> contains all image metadata and four folders contain the image files. All images are available in four different sizes, based on the image width, in 256px, 1024px, 2048px and the original size.<br>Folders containing the images are named according to the respective image size. Image files are named based on the <code>mapillary_image_id</code>.</p> <p>You can find the corresponding publication here: <a href="https://www.nature.com/articles/s41597-024-04295-9#citeas">StreetSurfaceVis: a dataset of crowdsourced street-level imagery with semi-automated annotations of road surface type and quality</a></p> <p> </p> <h3>Image metadata</h3> <p>Each CSV record contains information about one street-level image with the following attributes:</p> <ul> <li><code>mapillary_image_id</code>: ID provided by Mapillary (see information below on Mapillary)</li> <li><code>user_id</code>: Mapillary user ID of contributor</li> <li><code>user_name</code>: Mapillary user name of contributor</li> <li><code>captured_at</code>: timestamp, capture time of image</li> <li><code>longitude</code>, <code>latitude</code>: location the image was taken at</li> <li><code>train</code>: Suggestion to split train and test data. `True` for train data and `False` for test data. Test data contains data from 5 cities which are excluded in the training data.</li> <li><code>surface_type</code>: Surface type of the road in the focal area (the center of the lower image half) of the image. Possible values: asphalt, concrete, paving_stones, sett, unpaved</li> <li><code>surface_quality</code>: Surface quality of the road in the focal area of the image. Possible values: (1) excellent, (2) good, (3) intermediate, (4) bad, (5) very bad (see the attached <strong>Labeling Guide document</strong> for details)</li> </ul> <p> </p> <h3>Image source</h3> <p>Images are obtained from <a href="https://www.mapillary.com/">Mapillary</a>, a crowd-sourcing plattform for street-level imagery. More metadata about each image can be obtained via the <a href="https://www.mapillary.com/developer/api-documentation">Mapillary API . </a>User-generated images are shared by Mapillary under the <a href="https://creativecommons.org/licenses/by-sa/4.0/">CC-BY-SA</a> License.</p> <p>For each image, the dataset contains the <code>mapillary_image_id</code> and <code>user_name</code>. <br>You can access user information on the Mapillary website by <code>https://www.mapillary.com/app/user/<USER_NAME> </code><br>and image information by <code>https://www.mapillary.com/app/?focus=photo&pKey=<MAPILLARY_IMAGE_ID></code></p> <p>If you use the provided images, please adhere to the <a href="https://www.mapillary.com/terms">terms of use of Mapillary.</a></p> <p> </p> <h3>Instances per class</h3> <p>Total number of images: 9,122</p> <table> <tbody> <tr> <td> </td> <td><strong>excellent</strong></td> <td><strong>good</strong></td> <td><strong>intermediate</strong></td> <td><strong>bad</strong></td> <td><strong>very bad</strong></td> </tr> <tr> <td><strong>asphalt</strong></td> <td>971</td> <td>1697</td> <td>821</td> <td>246</td> <td>-</td> </tr> <tr> <td><strong>concrete</strong></td> <td>314</td> <td>350</td> <td>250</td> <td>58</td> <td>-</td> </tr> <tr> <td><strong>paving stones</strong></td> <td>385</td> <td>1063</td> <td>519</td> <td>70</td> <td>-</td> </tr> <tr> <td><strong>sett</strong></td> <td>-</td> <td>129</td> <td>694</td> <td>540</td> <td>-</td> </tr> <tr> <td><strong>unpaved</strong></td> <td>-</td> <td>-</td> <td>326</td> <td>387</td> <td>303</td> </tr> </tbody> </table> <p> </p> <p>For modeling, we recommend using a train-test split where the test data includes geospatially distinct areas, thereby ensuring the model's ability to generalize to unseen regions is tested. We propose five cities varying in population size and from different regions in Germany for testing - images are tagged accordingly.</p> <p>Number of test images (train-test split): 776</p> <h3>Inter-rater-reliablility</h3> <p>Three annotators labeled the dataset, such that each image was annotated by one person. Annotators were encouraged to consult each other for a second opinion when uncertain.<br>1,800 images were annotated by all three annotators, resulting in a <em>Krippendorff's alpha</em> of 0.96 for surface type and 0.74 for surface quality.</p> <h3>Recommended image preprocessing</h3> <p>As the focal road located in the bottom center of the street-level image is labeled, it is recommended to crop images to their lower and middle half prior using for classification tasks.</p> <p>This is an exemplary code for recommended image preprocessing in <strong>Python</strong>:</p> <pre><code>from PIL import Image<br></code><code>img = Image.open(image_path)</code><br><code>width, height = img.size</code><br><code>img_cropped = img.crop((0.25 * width, 0.5 * height, 0.75 * width, height))</code></pre> <h3><br><strong>License</strong></h3> <p><a href="https://creativecommons.org/licenses/by-sa/4.0/">CC-BY-SA</a></p> <p> </p> <h3><strong>Citation</strong></h3> <p>If you use this dataset, please cite as: </p> <p> </p> <p>Kapp, A., Hoffmann, E., Weigmann, E. <em>et al.</em> StreetSurfaceVis: a dataset of crowdsourced street-level imagery annotated by road surface type and quality. <em>Sci Data</em> <strong>12</strong>, 92 (2025). https://doi.org/10.1038/s41597-024-04295-9</p> <p> </p> <p><code>@article{kapp_streetsurfacevis_2025,<br> title = {{StreetSurfaceVis}: a dataset of crowdsourced street-level imagery annotated by road surface type and quality},<br> volume = {12},<br> issn = {2052-4463},<br> url = {https://doi.org/10.1038/s41597-024-04295-9},<br> doi = {10.1038/s41597-024-04295-9},<br> pages = {92},<br> number = {1},<br> journaltitle = {Scientific Data},<br> shortjournal = {Scientific Data},<br> author = {Kapp, Alexandra and Hoffmann, Edith and Weigmann, Esther and Mihaljević, Helena},<br> date = {2025-01-16},<br>}</code></p> <p> </p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>This is part of the SurfaceAI project at the University of Applied Sciences, HTW Berlin.</p> <p><br>- Prof. Dr. Helena Mihajlević<br>- Alexandra Kapp<br>- Edith Hoffmann<br>- Esther Weigmann</p> <p>Contact: surface-ai@htw-berlin.de</p> <p>https://surfaceai.github.io/surfaceai/</p> <p><strong>Funding</strong>: SurfaceAI is a mFund project funded by the Federal Ministry for Digital and Transportation Germany.</p> <p> </p>
SPHERA High Resolution Reanalysis over Italy - Hourly surface air temperature (2-meter height) 2013-2020
<p>SPHERA (High Resolution REAnalysis over Italy) is a convection-permitting regional reanalysis developed by ARPAE-Emilia Romagna and publicly available. The SPHERA domain covers Italy and the surrounding seas with a horizontal resolution of 2.2km. The temporal coverage corresponds to the period 1995-2020 and the dataset is available at hourly frequency. SPHERA reanalysis was developed using the Numerical Weather Prediction model COSMO (www.cosmo-model.org) nested in the global reanalysis ERA5 produced by ECMWF. Moreover, upper-air and surface observations were assimilated at the convection-permitting scale by the COSMO nudging scheme.</p> <p>This record reports the hourly surface air temperature at 2-meter height for the period 2013-2020. The full extension of the dataset over 1995-2020 is available over three different records due to space constraints:</p> <ul> <li>1995-2003: <a href="../records/12567563">https://zenodo.org/records/12567563</a></li> <li>2004-2012: <a href="../records/12582246">https://zenodo.org/records/12582246</a></li> <li>2013-2020: <a href="../records/12582797">https://zenodo.org/records/12582797</a></li> </ul> <p>Update (2024-06-28): inconsistencies were noted in a subset of grib messages contained the first version of the repository (slightly different spatial domain size and missing messages at 00-hour timesteps) which have been corrected in the current version v2.</p> <p>Other fields currently available on Zenodo are the surface relative humidity at 2-meter height (also over three different records due to space constraints):</p> <ul> <li>1995-2003: <a href="../records/12724026">https://zenodo.org/uploads/12724026</a></li> <li>2004-2012: <a href="../records/12724104">https://zenodo.org/uploads/12724104</a></li> <li>2013-2020: <a href="../records/12724140">https://zenodo.org/uploads/12724140</a></li> </ul> <p>and hourly accumulated total precipitation: <a href="https://zenodo.org/records/14617083">https://zenodo.org/records/14617083</a></p> <p>Details on the SPHERA dataset production, as well as data verification against surface observations are reported in peer-reviewed publications. See References.</p>
SPHERA High Resolution Reanalysis over Italy - Hourly surface air temperature (2-meter height) 2004-2012
<p>SPHERA (High Resolution REAnalysis over Italy) is a convection-permitting regional reanalysis developed by ARPAE-Emilia Romagna and publicly available. The SPHERA domain covers Italy and the surrounding seas with a horizontal resolution of 2.2km. The temporal coverage corresponds to the period 1995-2020 and the dataset is available at hourly frequency. SPHERA reanalysis was developed using the Numerical Weather Prediction model COSMO (www.cosmo-model.org) nested in the global reanalysis ERA5 produced by ECMWF. Moreover, upper-air and surface observations were assimilated at the convection-permitting scale by the COSMO nudging scheme.</p> <p>This record reports the hourly surface air temperature at 2-meter height for the period 2004-2012. The full extension of the dataset over 1995-2020 is available over three different records due to space constraints:</p> <ul> <li>1995-2003: <a href="../records/12567563">https://zenodo.org/records/12567563</a></li> <li>2004-2012: <a href="../records/12582246">https://zenodo.org/records/12582246</a></li> <li>2013-2020: <a href="../records/12582797">https://zenodo.org/records/12582797</a></li> </ul> <p>Update (2024-06-28): inconsistencies were noted in a subset of grib messages contained the first version of the repository (slightly different spatial domain size and missing messages at 00-hour timesteps) which have been corrected in the current version v2.</p> <p>Other fields currently available on Zenodo are the surface relative humidity at 2-meter height (also over three different records due to space constraints):</p> <ul> <li>1995-2003: <a href="../records/12724026">https://zenodo.org/uploads/12724026</a></li> <li>2004-2012: <a href="../records/12724104">https://zenodo.org/uploads/12724104</a></li> <li>2013-2020: <a href="../records/12724140">https://zenodo.org/uploads/12724140</a></li> </ul> <p>and hourly accumulated total precipitation: <a href="https://zenodo.org/records/14617083">https://zenodo.org/records/14617083</a></p> <p>Details on the SPHERA dataset production, as well as data verification against surface observations are reported in peer-reviewed publications. See References.</p>
SPHERA High Resolution Reanalysis over Italy - Hourly surface air temperature (2-meter height) 1995-2003
<p>SPHERA (High Resolution REAnalysis over Italy) is a convection-permitting regional reanalysis developed by ARPAE-Emilia Romagna and publicly available. The SPHERA domain covers Italy and the surrounding seas with a horizontal resolution of 2.2km. The temporal coverage corresponds to the period 1995-2020 and the dataset is available at hourly frequency. SPHERA reanalysis was developed using the Numerical Weather Prediction model COSMO (www.cosmo-model.org) nested in the global reanalysis ERA5 produced by ECMWF. Moreover, upper-air and surface observations were assimilated at the convection-permitting scale by the COSMO nudging scheme.</p> <p>This record reports the hourly surface air temperature at 2-meter height for the period 1995-2003. The full extension of the dataset over 1995-2020 is available over three different records due to space constraints:</p> <ul> <li>1995-2003: <a href="../records/12567563">https://zenodo.org/records/12567563</a></li> <li>2004-2012: <a href="../records/12582246">https://zenodo.org/records/12582246</a></li> <li>2013-2020: <a href="../records/12582797">https://zenodo.org/records/12582797</a></li> </ul> <p>Update (2024-06-28): inconsistencies were noted in a subset of grib messages contained the first version of the repository (slightly different spatial domain size and missing messages at 00-hour timesteps) which have been corrected in the current version v2.</p> <p>Other fields currently available on Zenodo are the surface relative humidity at 2-meter height (also over three different records due to space constraints):</p> <ul> <li>1995-2003: <a href="../records/12724026">https://zenodo.org/uploads/12724026</a></li> <li>2004-2012: <a href="../records/12724104">https://zenodo.org/uploads/12724104</a></li> <li>2013-2020: <a href="../records/12724140">https://zenodo.org/uploads/12724140</a></li> </ul> <p>and hourly accumulated total precipitation: <a href="https://zenodo.org/records/14617083">https://zenodo.org/records/14617083</a></p> <p>Details on the SPHERA dataset production, as well as data verification against surface observations are reported in peer-reviewed publications. See References.</p>
Continental Europe surface lithology based on EGDI / OneGeology map at 1:1M scale
<p>Continental Europe surface lithology based on <strong><a href="http://www.europe-geology.eu/onshore-geology/geological-map/onegeologyeurope/">EGDI / OneGeology map</a></strong> at 1:1M scale produced by <a href="https://egdi.geology.cz/record/basic/5729ffdf-2558-48fc-a5d2-645a0a010855">GEOZS, Slovenia</a>. European datasets harvested from national WFS for geologic units or national geological units datasets, based on OneGeology and <strong><a href="https://inspire.ec.europa.eu/codelist/LithologyValue/">INSPIRE Lithology</a></strong> and Geochronologic Era URI codelists. Layers include:</p> <ul> <li>EGDI_GE_GeologicUnit_EN_1M_Surface_LithologyPolygon_v2_250m_epsg.3035.tif = original EGDI surface lithology map;</li> <li>dtm_surface.lithology_egdi.1m_c_250m_s_20000101_20221231_eu_epsg.3035_v20240530.tif = gap filled surface lithology map;</li> </ul> <p>Missing values in the original EGDI lithology map have been imputed by training a random forest classifier model based on parameters derived from DTM and soil regions map from Die Bundesanstalt für Geowissenschaften und Rohstoffe (BGR). By generating 1 million random points, geographically balanced over the whole pan-EU land area, each class in the map was covered properly. Classes whose number of samples is less than 10 were discarded from the model training. The hyperparameter tuning of the model was carried out via a Bayesian approach with a criteria to maximize accuracy of 5k-fold cross validation. The tuned random forest model achieved an accuracy of 47% (Kappa=0.43) for the testing data, 20% of the generated sample points. The lithology of Turkey, on the other hand, was digitised from the available geology map produced by the General Directorate of Mineral Research and Exploration (MTA). The available raster map was post-processed and classified as 20 lithology classes using the k-means algorithm. These classes were harmonized with the classes in the EGDI lithology map.</p> <p>Acknowledgment: GEOZS, Continental Shelf Department at the Ministry for Transport and Infrastructure.</p>
Data Repository: Land surface modelling activities at Weierbach catchment.
<p>The data in this repository comes from the modelling activities with the Community Land Model version 5.0 (CLM5) carried out at the Weierbach catchment, Luxembourg. The repository contains:</p> <ol> <li>A list of matric potentials of <em>Fagus sylvatica </em>at which it experiences a specific loss of conductivity (i.e., 12%, 50%, 88%) obtained from published data [File: additional_PHT_Fagus_sylvatica_Europe.csv].</li> <li>The hourly atmospheric forcing used during the simulations with CLM 5.0 in a NetCDF format [File: atmospheric_forcing.zip].</li> <li>All model results per experiment [model_results.zip].</li> <li>The R scripts for processing the model results for obtaining the information required for each figure [Files: manuscript_figure_#.R].</li> <li>A daily summary of the tree water deficit calculated per PFT, individual tree species, and the whole ecosystem [File: twd.csv].</li> <li>A daily summary of tree transpiration scaled at the catchment level per PFT, individual tree species, and the whole ecosystem [File: et_mm_wei.csv]. This daily summary is based on the hourly data available on: Klaus, J., Fabiani, G., Schoppach, R., Chun, K. P., Iffly, J. F., Penna, D., & Juilleret, J. (2024). Detailed sap flow monitoring data at Weierbach catchment, Luxembourg (Version v01) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.11381618" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11381618</a></li> </ol>
SPHERA High Resolution Reanalysis over Italy - Hourly surface relative humidity (2-meter height) 2013-2020
<p>SPHERA (High Resolution REAnalysis over Italy) is a convection-permitting regional reanalysis developed by ARPAE-Emilia Romagna and publicly available. The SPHERA domain covers Italy and the surrounding seas with a horizontal resolution of 2.2km. The temporal coverage corresponds to the period 1995-2020 and the dataset is available at hourly frequency. SPHERA reanalysis was developed using the Numerical Weather Prediction model COSMO (www.cosmo-model.org) nested in the global reanalysis ERA5 produced by ECMWF. Moreover, upper-air and surface observations were assimilated at the convection-permitting scale by the COSMO nudging scheme.</p> <p>This record reports the hourly surface relative humidity at 2-meter height for the period 2013-2020. The full extension of the dataset over 1995-2020 is available over three different records due to space constraints:</p> <ul> <li>1995-2003: <a href="12724026">https://zenodo.org/uploads/12724026</a></li> <li>2004-2012: <a href="12724104">https://zenodo.org/uploads/12724104</a></li> <li>2013-2020: <a href="12724140">https://zenodo.org/uploads/12724140</a></li> </ul> <p>Other fields currently available on Zenodo are the surface air temperature at 2-meter height (also over three different records due to space constraints):</p> <ul> <li>1995-2003: <a href="../records/12567563">https://zenodo.org/records/12567563</a></li> <li>2004-2012: <a href="../records/12582246">https://zenodo.org/records/12582246</a></li> <li>2013-2020: <a href="../records/12582797">https://zenodo.org/records/12582797</a></li> </ul> <p>and hourly accumulated total precipitation: <a href="https://zenodo.org/records/14617083">https://zenodo.org/records/14617083</a></p> <p>Details on the SPHERA dataset production, as well as data verification against surface observations are reported in peer-reviewed publications. See References.</p>
SPHERA High Resolution Reanalysis over Italy - Hourly surface relative humidity (2-meter height) 2004-2012
<p>SPHERA (High Resolution REAnalysis over Italy) is a convection-permitting regional reanalysis developed by ARPAE-Emilia Romagna and publicly available. The SPHERA domain covers Italy and the surrounding seas with a horizontal resolution of 2.2km. The temporal coverage corresponds to the period 1995-2020 and the dataset is available at hourly frequency. SPHERA reanalysis was developed using the Numerical Weather Prediction model COSMO (www.cosmo-model.org) nested in the global reanalysis ERA5 produced by ECMWF. Moreover, upper-air and surface observations were assimilated at the convection-permitting scale by the COSMO nudging scheme.</p> <p>This record reports the hourly surface relative humidity at 2-meter height for the period 2004-2012. The full extension of the dataset over 1995-2020 is available over three different records due to space constraints:</p> <ul> <li>1995-2003: <a href="12724026">https://zenodo.org/uploads/12724026</a></li> <li>2004-2012: <a href="12724104">https://zenodo.org/uploads/12724104</a></li> <li>2013-2020: <a href="12724140">https://zenodo.org/uploads/12724140</a></li> </ul> <p>Other fields currently available on Zenodo are the surface air temperature at 2-meter height (also over three different records due to space constraints):</p> <ul> <li>1995-2003: <a href="../records/12567563">https://zenodo.org/records/12567563</a></li> <li>2004-2012: <a href="../records/12582246">https://zenodo.org/records/12582246</a></li> <li>2013-2020: <a href="../records/12582797">https://zenodo.org/records/12582797</a></li> </ul> <p>and hourly accumulated total precipitation: <a href="https://zenodo.org/records/14617083">https://zenodo.org/records/14617083</a></p> <p>Details on the SPHERA dataset production, as well as data verification against surface observations are reported in peer-reviewed publications. See References.</p>
SPHERA High Resolution Reanalysis over Italy - Hourly surface relative humidity (2-meter height) 1995-2003
<p>SPHERA (High Resolution REAnalysis over Italy) is a convection-permitting regional reanalysis developed by ARPAE-Emilia Romagna and publicly available. The SPHERA domain covers Italy and the surrounding seas with a horizontal resolution of 2.2km. The temporal coverage corresponds to the period 1995-2020 and the dataset is available at hourly frequency. SPHERA reanalysis was developed using the Numerical Weather Prediction model COSMO (www.cosmo-model.org) nested in the global reanalysis ERA5 produced by ECMWF. Moreover, upper-air and surface observations were assimilated at the convection-permitting scale by the COSMO nudging scheme.</p> <p>This record reports the hourly surface relative humidity at 2-meter height for the period 1995-2003. The full extension of the dataset over 1995-2020 is available over three different records due to space constraints:</p> <ul> <li>1995-2003: <a href="12724026">https://zenodo.org/uploads/12724026</a></li> <li>2004-2012: <a href="12724104">https://zenodo.org/uploads/12724104</a></li> <li>2013-2020: <a href="12724140">https://zenodo.org/uploads/12724140</a></li> </ul> <p>Other fields currently available on Zenodo are the surface air temperature at 2-meter height (also over three different records due to space constraints):</p> <ul> <li>1995-2003: <a href="../records/12567563">https://zenodo.org/records/12567563</a></li> <li>2004-2012: <a href="../records/12582246">https://zenodo.org/records/12582246</a></li> <li>2013-2020: <a href="../records/12582797">https://zenodo.org/records/12582797</a></li> </ul> <p>and hourly accumulated total precipitation: <a href="https://zenodo.org/records/14617083">https://zenodo.org/records/14617083</a></p> <p>Details on the SPHERA dataset production, as well as data verification against surface observations are reported in peer-reviewed publications. See References.</p>
Supplemental Material to "Tenacity of Animal Disease Viruses on Wood Surfaces Relevant to Animal Husbandry"
<p>Data set for individual titre reduction of viruses over a period of time in multiple experiments.</p>
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