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EOOffshore: New European Wind Atlas (NEWA) Data for the Irish Continental Shelf Region
<p><a href="https://eooffshore.github.io/">EOOffshore</a> is a <a href="https://www.seai.ie/">Sustainable Energy Authority of Ireland (SEAI)</a> funded <a href="https://www.seai.ie/data-and-insights/seai-research/research-projects/details/building-upon-copernicus-earth-observation-services-to-augment-wind-measurement-coverage-of-the-oredp-offshore-renewable-energy-assessment-areas">project</a>, which commenced in June 2020 in the <a href="https://www.ucd.ie/physics/">School of Physics</a> in <a href="https://www.ucd.ie/">University College Dublin (UCD)</a>. It presents a case study that demonstrates the utility of the <a href="https://pangeo.io/">Pangeo</a> software ecosystem in the development of offshore wind speed and power density estimates, increasing wind measurement coverage of offshore renewable energy assessment areas in the <a href="https://www.marine.ie/Home/site-area/irelands-marine-resource/real-map-ireland">Irish Continental Shelf (ICS)</a> region. It has involved the creation of a new <a href="https://eooffshore.github.io/datasets.html">wind data catalog</a> for this region, consisting of a collection of analysis-ready, cloud-optimized (ARCO) datasets featuring up to 21 years of available in situ, reanalysis, and satellite observation wind data products.</p> <p>The <a href="https://www.neweuropeanwindatlas.eu/">New European Wind Atlas (NEWA)</a> provides wind statistics covering onshore Europe, 100km offshore over European seas, and the complete North and Baltic Seas, based on <a href="https://map.neweuropeanwindatlas.eu/about">30 years of mesoscale simulations</a>. These catalog data sets contain 2009-2018 products for the ICS region, provided by the <a href="https://map.neweuropeanwindatlas.eu/">NEWA Map Layers and Datasets</a> website, featuring variables at multiple heights (metres above surface level). They were used in the EOOffshore project outputs presented (<a href="https://meetingorganizer.copernicus.org/EGU22/EGU22-2746.html"><em>Scalable Offshore Wind Analysis With Pangeo</em></a>) at the <a href="https://meetingorganizer.copernicus.org/EGU22/session/42046"><em>Meeting Exascale Computing Challenges with Compression and Pangeo</em></a> <a href="https://www.egu22.eu/">2022 EGU General Assembly</a> session.</p> <ul> <li><em>eooffshore_ics_newa_celticsea.zarr.tar.gz</em> <ul> <li>Data set for a North Celtic Sea area of interest.</li> </ul> </li> <li><em>eooffshore_ics_newa_irishsea.zarr.tar.gz</em> <ul> <li>Data set for an Irish Sea area of interest.</li> </ul> </li> <li><em>eooffshore_ics_newa_m3.zarr.tar.gz</em> <ul> <li>Data set for the area surrounding the <a href="http://www.marine.ie/Home/site-area/data-services/real-time-observations/irish-weather-buoy-network-imos">Irish Weather Buoy Network - M3 buoy</a> coordinates.</li> </ul> </li> <li><em>eooffshore_ics_newa_m4.zarr.tar.gz</em> <ul> <li>Data set for the area surrounding the <a href="http://www.marine.ie/Home/site-area/data-services/real-time-observations/irish-weather-buoy-network-imos">Irish Weather Buoy Network - M4 buoy</a> coordinates.</li> </ul> </li> </ul> <p>Description and example usage of the NEWA data sets in EOOffshore:</p> <ul> <li><a href="https://eooffshore.github.io/NEWA_ICS_Wind_Data.html">NEWA Wind Data for Irish Continental Shelf region</a></li> <li><a href="https://eooffshore.github.io/Offshore_Wind_AOI.html">Offshore Wind in Irish Areas Of Interest</a></li> <li><a href="https://eooffshore.github.io/Comparison_Wind_Power.html">Comparison of Offshore Wind Speed Extrapolation and Power Density Estimation</a></li> </ul> <p>As requested by the <a href="https://map.neweuropeanwindatlas.eu/about">NEWA Terms of use</a>, the following attribution is declared:</p> <ul> <li>Data [2009 - 2018] obtained from the New European Wind Atlas (NEWA), a free, web-based application developed, owned and operated by the NEWA Consortium. For additional information see <a href="http://www.neweuropeanwindatlas.eu/">www.neweuropeanwindatlas.eu</a>.</li> </ul>
EOOffshore: ASCAT Wind Data for the Irish Continental Shelf Region
<p><a href="https://eooffshore.github.io">EOOffshore</a> is a <a href="https://www.seai.ie/">Sustainable Energy Authority of Ireland (SEAI)</a> funded <a href="https://www.seai.ie/data-and-insights/seai-research/research-projects/details/building-upon-copernicus-earth-observation-services-to-augment-wind-measurement-coverage-of-the-oredp-offshore-renewable-energy-assessment-areas">project</a>, which commenced in June 2020 in the <a href="https://www.ucd.ie/physics/">School of Physics</a> in <a href="https://www.ucd.ie/">University College Dublin (UCD)</a>. It presents a case study that demonstrates the utility of the <a href="https://pangeo.io/">Pangeo</a> software ecosystem in the development of offshore wind speed and power density estimates, increasing wind measurement coverage of offshore renewable energy assessment areas in the <a href="https://www.marine.ie/Home/site-area/irelands-marine-resource/real-map-ireland">Irish Continental Shelf (ICS)</a> region. It has involved the creation of a new <a href="https://eooffshore.github.io/datasets.html">wind data catalog</a> for this region, consisting of a collection of analysis-ready, cloud-optimized (ARCO) datasets featuring up to 21 years of available in situ, reanalysis, and satellite observation wind data products.</p> <p>The <a href="https://marine.copernicus.eu/">Copernicus Marine Service (CMS), or Copernicus Marine Environment Monitoring Service (CMEMS)</a>, is the marine component of the <a href="https://www.copernicus.eu/en/about-copernicus">European Union Copernicus Earth Observation (EO) programme</a>. It provides free, regular and systematic ocean data products on a global and regional scale. The CMS <a href="https://marine.copernicus.eu/about/producers/wind-tac">Surface Wind Thematic Assembly Center (Wind TAC)</a> is responsible for the collection, processing, qualification and distribution of surface winds data products derived from scatterometer satellite missions, including near-real time (NRT) and delayed mode (REP) processing of global wind observations. These catalog data sets contain CMS wind speed and direction data products generated using the Advanced SCATterometer (ASCAT) instruments deployed on the Metop satellites.</p> <ul> <li><em>eooffshore_ics_cmems_WIND_GLO_WIND_L3_REP_OBSERVATIONS_012_005_MetOp_ASCAT.zarr.tar.gz</em> <ul> <li>2007-2021 data products from the <a href="https://resources.marine.copernicus.eu/product-detail/WIND_GLO_WIND_L3_REP_OBSERVATIONS_012_005/INFORMATION"><em>Global Ocean Daily Gridded Reprocessed (REP) Level-3 Sea Surface Winds from Scatterometer</em></a><em> </em>data set.</li> </ul> </li> <li><em>eooffshore_ics_cmems_WIND_GLO_WIND_L3_NRT_OBSERVATIONS_012_002_MetOp_ASCAT.zarr.tar.gz</em> <ul> <li>2016-2021 data products from the <a href="https://resources.marine.copernicus.eu/product-detail/WIND_GLO_WIND_L3_NRT_OBSERVATIONS_012_002"><em>Global Ocean Daily Gridded Near Real Time (NRT) Level-3 Sea Surface Winds from Scatterometer</em></a><em> </em>data set.</li> </ul> </li> </ul> <p>The products feature 0.125 degree grids, based on 12.5 km scatterometer swath observations, for all combinations of Metop A/B (REP) and Metop A/B/C (NRT) satellites and ASCending, DEScending passes. These ASCAT data sets were used in the EOOffshore project outputs presented (<em><a href="https://meetingorganizer.copernicus.org/EGU22/EGU22-2746.html">Scalable Offshore Wind Analysis With Pangeo</a></em>) at the <em><a href="https://meetingorganizer.copernicus.org/EGU22/session/42046">Meeting Exascale Computing Challenges with Compression and Pangeo</a></em> <a href="https://www.egu22.eu/">2022 EGU General Assembly</a> session.</p> <p>Description and example usage of the ASCAT data sets in EOOffshore:</p> <ul> <li><a href="https://eooffshore.github.io/ASCAT_ICS_Wind_Data.html">ASCAT Wind Data for Irish Continental Shelf region</a></li> <li><a href="https://eooffshore.github.io/Offshore_Wind_AOI.html">Offshore Wind in Irish Areas Of Interest</a></li> <li><a href="https://eooffshore.github.io/Comparison_Wind_Power.html">Comparison of Offshore Wind Speed Extrapolation and Power Density Estimation</a></li> </ul> <p>As requested by the <a href="https://marine.copernicus.eu/user-corner/service-commitments-and-licence">Copernicus Marine Service Service Commitments and Licence</a>, these Zarr stores were:</p> <ul> <li> <p>Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00182">https://doi.org/10.48670/moi-00182</a>; <a href="https://doi.org/10.48670/moi-00183">https://doi.org/10.48670/moi-00183</a>;</p> </li> </ul>
Large-Eddy Simulation of Wind Turbine Flows: A New Evaluation of Actuator Disk Models - Dataset
<p>Main data used in the following paper: Revaz, T.; Porté-Agel, F. Large-Eddy Simulation of Wind Turbine Flows: A New Evaluation of Actuator Disk Models. <em>Energies</em> <strong>2021</strong>, <em>14</em>, 3745. https://doi.org/10.3390/en14133745</p>
The winds of young Solar-type stars in the Hyades - Quiet Sun model
<p>This is the quiet Sun model from my MNRAS paper "The winds of young Solar-type stars in the Hyades"(https://doi.org/10.1093/mnras/stab1696). Please see the paper for a full description.</p>
Data used in 'Local Wind Regime Induced by Giant Linear Dunes: Comparison of ERA5-Land Reanalysis with Surface Measurements.'
<p>This repository contains the data used in:</p> <blockquote> <p>Gadal, C., Delorme, P., Narteau, C. et al. Local Wind Regime Induced by Giant Linear Dunes: Comparison of ERA5-Land Reanalysis with Surface Measurements. Boundary-Layer Meteorol 185, 309–332 (2022). <a href="https://doi.org/10.1007/s10546-022-00733-6">https://doi.org/10.1007/s10546-022-00733-6</a></p> </blockquote> <p>where wind data measured at 4 different places in and across the Namib Sand Sea are compared to the data from the ERA5/ERA5Land climate reanalyses.</p> <p>The use this data, one should first look at the GitHub repository <a href="https://github.com/Cgadal/GiantDunes">https://github.com/Cgadal/GiantDunes</a> and at the corresponding documentation <a href="https://cgadal.github.io/GiantDunes/">https://cgadal.github.io/GiantDunes/</a>. The description sometimes refers to scripts used in <a href="https://github.com/Cgadal/GiantDunes/tree/master/Processing">https://github.com/Cgadal/GiantDunes/tree/master/Processing</a>.</p> <p>The two folders 'raw_data' and 'processed_data' contain the input raw_data, and the output data after processing used to make the paper figures, respectively. In each of them, '.npy' files contain Python dictionaries with different variables in them. They can be loaded using the Python library <code>numpy</code> as <code>data = np.load('file.npy', allow_pickle=True).item()</code>; and the different keys (variables) can be printed with <code>data.keys()</code> or <code>data[station].keys()</code> if <code>data.keys()</code> return the different stations. Unless specified otherwise below, note that all variables are given in the International System of Units (SI), and wind direction is given anticlockwise, with the 0 being a wind blowing from the West to the East.</p> <ul> <li>raw_data: <ul> <li>DEM: contains the Digital Elevation Models of the two stations from the SRTM30, downloaded from here: https://dwtkns.com/srtm30m/</li> <li>ERA5: hourly data from the ER5 climate reanalysis, on surface (_BLH) and pressure levels (_levels). Downloaded from https://cds.climate.copernicus.eu/</li> <li>ERA5Land: hourly data from the ER5Land climate reanalysis Downloaded from https://cds.climate.copernicus.eu/</li> <li>KML_points: kml points of the measurement station. It can be opened directly in GoogleEarth.</li> <li>measured_wind_data: contains the measured in situ data. The windspeed is measured using Vector Instruments A100-LK cup anemometers, the wind direction using Vector Instruments W200-P wind vane and the time using Campbell Instruments CR10X and CR1000X dataloggers.<br> </li> </ul> </li> <li>processed_data: <ul> <li>'Data_preprocessed.npy': preprocessed_data, output of 1_data_preprocessing_plot.py</li> <li>'Data_DEM.npy': properties of the processed DEM, the output of 2_DEM_analysis_plot.py</li> <li>'Data_calib_roughness.npy': data from the calibration of the hydrodynamic roughnesses, the output of 3_roughness_calibration_plot.py</li> <li>'Data_final.npy': file containing all computed quantities</li> <li>'time_series_hydro_coeffs.npy': file containing the time series of the calculated hydrodynamic coefficients by '5_norun_hydro_coeff_time_series.npy'.</li> </ul> </li> </ul> <p> Depending on the loaded data file, main dictionary keys can be:</p> <ul> <li>'lat': latitude, in degree</li> <li>'lon': longitude, in degree</li> <li>'time': time vector, in datetime objects (https://docs.python.org/3/library/datetime.html)</li> <li>'DEM': elevation data array in [m], with dimensions matching 'lat' and 'lon' vectors</li> <li>'z_mes', 'z_insitu', 'z_ERA5LAND': height of the corresponding velocity</li> <li>'direction': measured wind direction, in [degrees]</li> <li>'velocity': measured wind velocity, in [m/s]</li> <li>'orientaion': dune pattern orientation, [deg]</li> <li>'wavelength': dune pattern wavelength, [km]</li> <li>'z0_insitu': chosen hydrodynamic roughness for the considered station.</li> <li>'U_insitu', 'Orientation_insitu': hourly averaged measured wind velocities and direction</li> <li>'U_era', 'Orientation_era': hourly 10m wind data from the ERA5Land data set</li> <li>'Boundary layer height', 'blh': boundary layer height from the hourly ERA5 dataset</li> <li>'Pressure levels', 'levels': Pressure levels from the pressure levels ERA5 dataset</li> <li>'Temperature', 't': Temperature from the pressure levels ERA5 dataset</li> <li>'Specific humidity', 'q': Specific humidity from the pressure levels ERA5 dataset</li> <li>'Geopotential', 'z': Geopotential from the pressure levels ERA5 dataset</li> <li>'Virtual_potential_temperature': Virtual potential temperature calculated from the pressure levels ERA5 dataset</li> <li>'Potential_temperature': Potential temperature calculated from the pressure levels ERA5 dataset</li> <li>'Density': Density calculated from the pressure levels ERA5 dataset</li> <li>'height': Vertical coordinates calculated from the pressure levels ERA5 dataset</li> <li>'theta_ground': Averaged virtual potential temperature within the ABL.</li> <li>'delta_theta': Virtual potential temperature at the ABL.</li> <li>'gradient_free_atm': Virtual potential temperature gradient in the FA.</li> <li>'Froude': time series of the Froude number U/((delta_theta/theta_ground)*g*BLH)</li> <li>'kH': time series of the number 'kH'</li> <li>'kLB': time series of the internal Froude number kU/N</li> </ul> <p>Other keys are not relevant and are stored for verification purposes. For more details, please contact Cyril Gadal (see authors), and look at the following GitHub repository: <a href="https://github.com/Cgadal/GiantDunes">https://github.com/Cgadal/GiantDunes</a>, where all the codes are present.<br> </p>
Wind data (2007-2017) in florentine and chianti areas to support tree's damages reporting.
<p>Wind data of several weather station to support tree damages investigations.</p> <p><strong>Firenze Peretola</strong> Areoporto LIRQ ENAV LAT 43.809722 LON 11.203 ELEV 44</p> <p><strong>Sesto Polo Scientifico</strong> LAMMA-CNR LAT 43.8189 LON 11.2021 ELEV 40</p> <p><strong>Sesto Case Passerini</strong> Codice CFR TOS01001225 LAT 43.82 LON 11.17 ELEV 33</p> <p><strong>Scandicci San Giusto</strong> CFR TOS01001215 LAT 43.76 LON 11.19 ELEV 42</p> <p><strong>Tavarnelle</strong> CFR TOS11000021 LAT 43.57 LON 11.16 ELEV 374</p> <p><strong>Greve in Chianti</strong> CFR TOS11000073 LAT 43.61 LON 11.30 ELEV 254</p> <p>Data sets gives annual and seasonal windplot roses. Wind data summaries by sectors of wind provenience ( Mean, Max,Median and Quantile95). Futher the 500th maximum records of gust are also extracted. Data are provided to support tree damages reporting.</p>
Reference site conditions for floating wind arrays: dataset of reference sites
<h1>IEA Task 49: Integrated Design of Floating Wind Arrays</h1> <h3>This dataset contains atmospheric and oceanographic data of 11 locations around the globe of future floating offshore wind farms.<br>Each dataset was used to perform a metocean analysis for preliminary design, published in the Work Package 1 Report of IEA Task 49.</h3> <div> <div> <div><a name="_msocom_1"></a></div> </div> </div> <table> <tbody> <tr> <td><strong>4COffshore ID</strong></td> <td><strong>Name</strong></td> <td><strong>Latitude [deg]</strong></td> <td><strong>Longitude [deg]</strong></td> <td><strong>Water Depth [m] (GEBCO)</strong></td> <td><strong>Distance from shore [m]</strong></td> <td><strong>Country</strong></td> <td><strong>Dataset curated by</strong></td> </tr> <tr> <td>IT95 </td> <td>Hannibal </td> <td> <div>37.842</div> </td> <td> <div>12.0722</div> </td> <td> <div> -353</div> </td> <td> <div> 35</div> </td> <td>Italy </td> <td>RSE</td> </tr> <tr> <td>US0W </td> <td> Humboldt</td> <td> <div> 40.928</div> </td> <td> <div>-124.708</div> </td> <td> <div> -707</div> </td> <td> <div> 43.8</div> </td> <td>USA</td> <td>NREL</td> </tr> <tr> <td>KR0R </td> <td>Ulsan </td> <td> <div> 35.449</div> </td> <td> <div> 129.949</div> </td> <td> <div> -188</div> </td> <td> <div> 32</div> </td> <td>South Korea </td> <td>UOU</td> </tr> <tr> <td>IE34 </td> <td>Moneypoint One</td> <td> <div> 52.519</div> </td> <td> <div>-10.276</div> </td> <td> <div> -1<a>02</a> </div> </td> <td> <div> 23.4</div> </td> <td>Ireland </td> <td>GDG</td> </tr> <tr> <td>UK6L </td> <td>Havbredey </td> <td> <div>58.862</div> </td> <td> <div> <div> <p>-5.54</p> </div> </div> </td> <td> <div> <div> <p> -91</p> </div> </div> </td> <td> <div> <div> <p> 41.6</p> </div> </div> </td> <td>Scotland </td> <td>DHI</td> </tr> <tr> <td>JP06 </td> <td>Fukushima</td> <td> <div>37.311</div> </td> <td> <div>141.251</div> </td> <td> <div> -90</div> </td> <td> <div> 19.4</div> </td> <td>Japan </td> <td>AIT</td> </tr> <tr> <td>NO44 </td> <td>Utsira nord<strong>*</strong></td> <td> <div>59.276</div> </td> <td> <div>4.541</div> </td> <td> <div> -273</div> </td> <td> <div> 42.4</div> </td> <td>Norway </td> <td>4subsea / UiS,UiB*</td> </tr> <tr> <td>USZ3 </td> <td>Gulf of Maine</td> <td> <div>43.25</div> </td> <td> <div>-69.5</div> </td> <td> <div> -148</div> </td> <td> <div> 138</div> </td> <td>USA </td> <td>NREL</td> </tr> <tr> <td>KR88 </td> <td>Geomundo<strong>**</strong></td> <td> <div>34.039</div> </td> <td> <div>126.901</div> </td> <td> <div> -70</div> </td> <td> <div> 47</div> </td> <td>South Korea </td> <td>IAE**</td> </tr> <tr> <td>FR87 </td> <td>Sud de la Bretagne II</td> <td> <div>47.325</div> </td> <td> <div>-3.659</div> </td> <td> <div> -94</div> </td> <td> <div> 30.7</div> </td> <td>France </td> <td>UiB</td> </tr> <tr> <td>NO66</td> <td>Sørlige Nordsjø II<strong>***</strong></td> <td> <div> 56.78 </div> </td> <td> <div> 4.92 </div> </td> <td> <div> -60</div> </td> <td> <div> 180</div> </td> <td>Norway</td> <td>4subsea / UiS,UiB***</td> </tr> </tbody> </table> <p>* Suplementary dataset published at: https://doi.org/10.5281/zenodo.10048048</p> <p>** Dataset is confidential, for details of usage reach out to the contact person.</p> <p>*** Suplementary dataset published at: https://doi.org/10.5281/zenodo.7057407</p>
High-frequency wind (u, w, v, Ts) and gas concentration measurements of CO2 and H2O over an agricultural field in Braunschweig, Germany
<p>This dataset contains high-frequency eddy covariance (EC) measurements over a flat agricultural field at the Thünen Institute in Braunschweig, Germany (52.30° N, 10.45° E).</p> <p>The data collection period spanned <strong>77 days </strong>in the year 2020 split into three files</p> <p> </p> <table> <tbody> <tr> <td>BS2020_06.rds</td> <td>June 11 to July 15</td> </tr> <tr> <td>BS2020_10.rds</td> <td>October 1 to November 10</td> </tr> <tr> <td>BS_2020_07_subset.rds</td> <td>July 11 to July 25</td> </tr> </tbody> </table> <p> </p> <p>Included in the dataset are 3D wind velocity data, recorded using a uSonic-3 Class A sonic anemometer from Metek GmbH. Additionally, the dataset provides gas concentration measurements for carbon dioxide (CO2) and water vapor (H2O), captured using an LI-7500A open-path infra-red gas analyzer from LI-COR Biosciences GmbH, Germany.</p> <p>Variables in the dataset</p> <table> <tbody> <tr> <th>Variable Name</th> <th>Description</th> <th>Units</th> </tr> </tbody> <tbody> <tr> <td>time</td> <td>Unique time stamp (POSIXct format)</td> <td>Seconds since Unix epoch</td> </tr> <tr> <td>CO2</td> <td>Wet molar density of carbon dioxide</td> <td>µmol m⁻³</td> </tr> <tr> <td>H2O</td> <td>Wet molar density of water vapor</td> <td>mmol m⁻³</td> </tr> <tr> <td>Ts</td> <td>Sonic temperature</td> <td>Kelvin</td> </tr> <tr> <td>u, v, w</td> <td>3D wind velocity components</td> <td>m s⁻¹</td> </tr> </tbody> </table> <p> </p> <p> </p> <div> <div> <div> <p>The dataset is in RDS format (version 3), compatible with R version 3.5.0 or higher.</p> <p>RDS is a binary file format native to the R programming environment</p> </div> </div> </div>
2005_2018_Wind_Speed_Direction
<p><strong>Abstract:</strong></p> <p>European Wind characteristics at 10m in height derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses (ERA) data. The data defines characteristics such as windspeed direction from and direction. Monthly mean values for the years 2005-2018 at 0.125 of a degree Clipped to the E4warning extent. </p> <table> <tbody> <tr> <td><strong>PROJECTION:</strong></td> <td>Geographic</td> </tr> <tr> <td><strong>DATUM:</strong></td> <td>WGS84</td> </tr> </tbody> </table> <p><strong>File Names: </strong></p> <p>The last 4 digits of the file name present Month and Year of file. </p> <p>dir in file names refer to direction.</p> <p>speed in file names refer to windspeed</p> <p> </p> <p> </p>
Temperature and wind speed time series on a 50 km^2 grid in Europe
<p>This spatio-temporal dataset contains weather timeseries for locations on a grid with 50km edge length in Europe. The data is resolved in one hour timesteps and comprises the years 2000-2018. It has been generated directly from the MERRA-2 reanalysis dataset.</p> <p>This data serves as an input to the Sector-Coupled Euro-Calliope model and has been generated to match the spatial resolution of the renewable energy generation capacity factors found at https://doi.org/10.5281/zenodo.3891480.</p> <p><em>temperature.nc:</em> air temperature at 2m above ground in degrees C.</p> <p><em>tsoil5.nc</em>: soil temperature at layer 5 in degrees C.</p> <p><em>wind10m.nc</em>: wind speed at 10m above ground in m/s.</p> <p><em>grid.nc:</em> provides the latitude and longitude of each grid (a.k.a. "site") centroid. This data is also given in every other dataset, but is provided here as a lightweight alternative to align other datasets to the same grid spacing.</p> <p> </p> <p><strong>Changelog</strong></p> <p>2024-06-07:</p> <ul> <li>Moved data variables to dataset attributes where the same data was duplicated per gridcell.</li> <li>Added units to file attributes.</li> <li>Updated `tsoil5` variable name from `soil_temperture_5` to `tsoil5`. Now all timeseries data variables have names that match the filename. </li> <li>Updated `tsoil5` variable from Kelvin to degrees C.</li> <li>Updated `tsoil5` variable empty data (when gridcell is not over land) from zero to NaN.</li> <li>Added `grid.nc`.</li> <li>Removed `electricity` variable from `wind10m.nc`, leaving only wind speed as the available timeseries data in the file.</li> </ul>
Historical Reconstruction Dataset of Hourly Expected On-Shore Wind Generation in Japan
<h2>Description</h2> <p>This is a historical reconstruction dataset of hourly expected wind generation based on dynamically downscaled atmospheric reanalysis for assessing the spatio-temporal impact of on-shore wind in Japan.</p> <p>The dataset consists of a set of <a href="https://www.unidata.ucar.edu/software/netcdf/">netCDF</a> files with yearly archives of reconstruction results from 1958 to 2012; hourly expected on-shore wind power potential in Japan with a spatial resolution of approximately 5 km mesh has been reconstructed from the numerical weather model reanalysis results. The expected per-unit output values at each location were calibrated using a nonparametric machine learning model that learns statistical relationships between spatial/meteorological features of target locations and actual wind farm outputs.</p> <p>A convenient way to handle this dataset would be to use a tool for manipulating netCDF files, such as <a href="https://code.mpimet.mpg.de/projects/cdo">CDO: Climate Data Operators</a>.</p> <h2>Associated Publication</h2> <ul> <li>Yu Fujimoto, Masamichi Ohba, Yujiro Tanno, Daisuke Nohara, Yuki Kanno, Akihisa Kaneko, Yasuhiro Hayashi, Yuki Itoda, and Wataru Wayama, "Historical Reconstruction Dataset of Hourly Expected Wind Generation Based on Dynamically Downscaled Atmospheric Reanalysis for Assessing Spatio-Temporal Impact of On-Shore Wind in Japan", <em>Big Earth Data</em>, doi: 10.1080/20964471.2024.2374044 </li> </ul> <h2>Version history</h2> <ul> <li>Ver. 1.0: Released.</li> <li>Ver. 1.1: The preprocessing of the source information used for dataset preparation has changed.</li> <li>Ver. 1.2: The hyperparameter tuning scheme for the post-processing model has changed.</li> </ul>
Data archive for "Flight behaviour of Red Kites within their breeding area in relation to local weather variables: Conclusions with regard to wind turbine collision mitigation"
<p>The archive contains the data files to reproduce the results presented in the article “Flight behaviour of Red Kites within their breeding area in relation to local weather variables: Conclusions with regard to wind turbine collision mitigation” published in the Journal of Applied Ecology.</p>
Experimental investigation of RVGs on a wind turbine airfoil
<p>This project contains the data obtained as a result of the Preludium Grant no 2022/45/N/ST8/01425 of the Polish National Science Centre fundings. Within the "Aeroacoustic investigations of streamwise vortex generators for boundary layer separation control" project, two main research tasks were defined:<br> 1. Post-processing and analysis of acoustic measurements using beamforming techniques to investigate RVGs effect on acoustic sources<br> 2. Post-processing and analysis of Particle Image Velocimetry (PIV) data to investigate a flow structure downstream of RVG</p> <p>The resutls from these tasks are uploaded here. The details of the data are included in the EOP_medata_1.docx document uploaded. <br>Further information regarding the data is published in the paper <a href="https://www.researchgate.net/publication/380825438_Aeroacoustic_effect_of_boundary_layer_separation_control_by_rod_vortex_generators_on_the_DU96-W-180_airfoil">https://www.researchgate.net/publication/380825438_Aeroacoustic_effect_of_boundary_layer_separation_control_by_rod_vortex_generators_on_the_DU96-W-180_airfoil</a></p>
Wind tunnel experiment of a micro wind farm model
<p>Simultaneous strain gage measurements of sixty porous disk models, in a scaled wind farm with one hundred models, and for fifty-six different layouts. </p> <p>For detailed information about the experimental setup and wind farm layouts see: </p> <p>Bossuyt, J., Meneveau, C., & Meyers, J. (2018). Effect of layout on asymptotic boundary layer regime in deep wind farms. <em>Physical Review Fluids. See also:</em> https://arxiv.org/abs/1808.09579 .</p> <p>For more information about the experimental design of the porous disk models, see also:</p> <p>Bossuyt, J., Howland, M. F., Meneveau, C., & Meyers, J. (2017). Measurement of unsteady loading and power output variability in a micro wind farm model in a wind tunnel. <em>Experiments in Fluids</em>, <em>58</em>(1), 1. http://doi.org/10.1007/s00348-016-2278-6</p> <p> Bossuyt, J., Meneveau, C., & Meyers, J. (2017). Wind farm power fluctuations and spatial sampling of turbulent boundary layers. <em>Journal of Fluid Mechanics</em>, <em>823</em>, 329-344. http://doi.org/10.1017/jfm.2017.328</p> <p> </p> <p>The data contains matrices 'WF_U', 'x', and 'y', and variable 'fs' for each layout. <br> The matrix 'WF_U' contains the reconstructed velocity signal in m/s measured by each porous disk, and has size ( 20 , 3 , number of time samples), with 20 the number of porous disk rows, and 3 the number of streamwise aligned porous disk columns in the wind farm. Matrices 'x', and 'y' have size (20,3) and contain the locations of each instrumented porous disk in units of disk diameter D = 0.03m. It is important to note that the wind farm has one extra column of non-instrumented porous disk models on each side, for a total of 20x5=100 porous disk models.The variable 'fs' contains the sampling frequency in Hz, at which all 60 porous disks are simultaneously sampled.</p> <p>--------------------------------------------------------<br> Example code to load data in Matlab :<br> --------------------------------------------------------<br> filename = 'U_C1_1.h5';<br> fileID = H5F.open(filename,'H5F_ACC_RDONLY','H5P_DEFAULT');</p> <p>datasetID = H5D.open(fileID,'WF_U');<br> WF_U = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br> H5D.close(datasetID);</p> <p>datasetID = H5D.open(fileID,'fs');<br> fs = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br> H5D.close(datasetID);</p> <p>datasetID = H5D.open(fileID,'x');<br> x = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br> H5D.close(datasetID);</p> <p>datasetID = H5D.open(fileID,'y');<br> y = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br> H5D.close(datasetID);</p> <p>H5F.close(fileID);</p> <p>--------------------------------------------------------<br> Example code to load data in Python:<br> --------------------------------------------------------<br> import h5py<br> filename = 'U_C1_1.h5'<br> f = h5py.File(filename, 'r')</p> <p>U = f['WF_U'][()]<br> x = f['x'][()]<br> y = f['y'][()]<br> fs = f['fs'][0][0]<br> f.close()</p> <p>--------------------------------------------------------<br> Example code to generate figures 15 and 16 of Bossuyt et al. (2018). Effect of layout on asymptotic boundary layer regime in deep wind farms. Physical Review Fluids, in Matlab<br> --------------------------------------------------------<br> WF_cases_selected = 1:7;</p> <p>folder = '/';% folder with files</p> <p>WF_cases_l = {'U_C1';'U_C2';'NU1_C1';'NU1_C2';'NU2_C1';'NU2_C2';'NU2_C3'};% name of layout variations<br> WF_cases_n = [6, 7, 11, 8, 11, 7, 6]; % 'number of layout variations for each case</p> <p>WF_data.x = cell( length(WF_cases_selected) , 1);% x - coordinates of porous disk locations<br> WF_data.y = cell( length(WF_cases_selected) , 1);% y - coordinates of porous disk locations<br> WF_data.shift = cell( length(WF_cases_selected) , 1);% spanwise shift of layout series<br> WF_data.fs = cell( length(WF_cases_selected) , 1);<br> WF_data.WF_Pm = cell( length(WF_cases_selected) , 1);<br> WF_data.WF_Um = cell( length(WF_cases_selected) , 1);<br> WF_data.WF_U_rms = cell( length(WF_cases_selected) , 1);</p> <p><br> for i = 1 : length(WF_cases_selected)<br> <br> WF_data_case = struct;<br> WF_data_case.x = cell( WF_cases_n(i) , 1);<br> WF_data_case.y = cell( WF_cases_n(i) , 1);<br> WF_data_case.shift = cell( WF_cases_n(i) , 1);<br> WF_data_case.fs = cell( WF_cases_n(i) , 1);<br> WF_data_case.WF_Pm = cell( WF_cases_n(i) , 1);<br> WF_data_case.WF_Um = cell( WF_cases_n(i) , 1);<br> WF_data_case.WF_U_rms = cell( WF_cases_n(i) , 1);<br> <br> for j = 1:WF_cases_n(i)<br> clc<br> i<br> j<br> <br> WF_data_var = struct;<br> <br> %read the file<br> filename = [folder WF_cases_l{i} '_' num2str(j) '.h5'];<br> fileID = H5F.open(filename,'H5F_ACC_RDONLY','H5P_DEFAULT');<br> <br> datasetID = H5D.open(fileID,'WF_U');<br> WF_data_var.WF_U = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br> H5D.close(datasetID);<br> <br> datasetID = H5D.open(fileID,'fs');<br> WF_data_case.fs{j} = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br> H5D.close(datasetID);<br> <br> datasetID = H5D.open(fileID,'x');<br> WF_data_case.x{j} = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br> H5D.close(datasetID);<br> <br> datasetID = H5D.open(fileID,'y');<br> WF_data_case.y{j} = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br> H5D.close(datasetID);<br> <br> H5F.close(fileID);<br> <br> WF_data_var.WF_P = WF_data_var.WF_U.^3;</p> <p> % Time averaged power<br> WF_data_case.WF_Pm{j} = mean(WF_data_var.WF_P,3);<br> <br> % normalize by power in first row: Pi/P1<br> WF_data_case.WF_Pm{j} = WF_data_case.WF_Pm{j}./mean(WF_data_case.WF_Pm{j}(1,:));<br> <br> % Time averaged velocity<br> WF_data_case.WF_Um{j} = mean(WF_data_var.WF_U,3);<br> <br> % u_rms --> TI<br> WF_data_case.WF_U_rms{j} = std(WF_data_var.WF_U,[],3);<br> end<br> <br> WF_data.x{i} = WF_data_case.x;<br> WF_data.y{i} = WF_data_case.y;<br> WF_data.fs{i} = WF_data_case.fs;<br> WF_data.WF_Pm{i} = WF_data_case.WF_Pm;<br> WF_data.WF_Um{i} = WF_data_case.WF_Um;<br> WF_data.WF_U_rms{i} = WF_data_case.WF_U_rms;<br> <br> %determine spanwise shift for plot legends<br> tmp1 = WF_data.y{i}{j-1};<br> tmp2 = WF_data.y{i}{j};<br> dy = diff( [tmp1(:,1) tmp2(:,1)] ,1,2);<br> dy = max(dy(abs(dy)>0));<br> WF_data.shift{i} = 0:dy:(WF_cases_n(i)-1)*dy;<br> <br> end</p> <p>%%<br> line_tick = {'o-','*-','+-','d-','s-','^-','v-','<-','>-','p-','h-'};<br> line_color = [51,160,44; 141,211,199; 31,120,180; 106,61,154; 227,26,28; 177,89,40; 255,127,0; 166,206,227]./255;</p> <p>legend_items = cell(size(WF_cases_selected));<br> for i = 1:length(legend_items)<br> legend_items{i} = strrep(WF_cases_l{i},'_','-');<br> end</p> <p>%% average power entire farm<br> row_start = 1;<br> row_end = 19;<br> f1 = figure;<br> set(gcf,'paperposition',[0,0,8.4,4.9])<br> hold on</p> <p>for i = 1 : length(WF_cases_selected)<br> tmp_P = zeros(size(WF_data.shift{i}));<br> for j = 1:WF_cases_n(i)<br> tmp_P(j) = mean(mean( WF_data.WF_Pm{i}{j}(row_start:row_end,:)));<br> end<br> plot( WF_data.shift{i} , tmp_P, line_tick{i} ,'Color', line_color(i,:) ,'MarkerFaceColor', line_color(i,:) )<br> end</p> <p>% manualy plot errorbars<br> for i = 1:length(WF_cases_selected)<br> tmp_P = zeros(size(WF_data.shift{i}));<br> for j = 1:WF_cases_n(i)<br> tmp_P(j) = mean(mean( WF_data.WF_Pm{i}{j}(row_start:row_end,:)));<br> end<br> px = WF_data.shift{i} ;<br> py = tmp_P;<br> pw = 0.05;<br> pe = zeros(size(px))+0.01;%for uncertainty value see Bossuyt et al. (2018) Physical Review Fluids. <br> for j = 1:WF_cases_n(i)<br> plot( [px(j)-pw/2 px(j)+pw/2] , [py(j)+pe(j) py(j)+pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br> plot( [px(j)-pw/2 px(j)+pw/2] , [py(j)-pe(j) py(j)-pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br> plot( [px(j) px(j)],[py(j)-pe(j) py(j)+pe(j)],':', 'Color', line_color(i,:),'LineWidth',0.5)<br> end<br> end<br> xlabel('\Delta_y [D]')<br> ylabel('$\langle P_i /P_1\rangle_{1}^{19}$','Interpreter','Latex')<br> box('on')<br> ylim([0.35 0.66])<br> xlim([-0.1 2.6])<br> legend1 = legend(legend_items');<br> set(legend1,'Location','southeast');<br> print(f1, 'WF_Pm_all','-dpng','-r300')</p> <p>%% average power end of farm<br> row_start = 16;<br> row_end = 19;<br> f2 = figure;<br> set(gcf,'paperposition',[0,0,8.4,4.9])<br> hold on</p> <p>for i = 1 : length(WF_cases_selected)<br> tmp_P = zeros(size(WF_data.shift{i}));<br> for j = 1:WF_cases_n(i)<br> tmp_P(j) = mean(mean( WF_data.WF_Pm{i}{j}(row_start:row_end,:)));<br> end<br> plot( WF_data.shift{i} , tmp_P, line_tick{i} ,'Color', line_color(i,:) ,'MarkerFaceColor', line_color(i,:) )<br> end</p> <p>% manualy plot errorbars<br> for i = 1:length(WF_cases_selected)<br> tmp_P = zeros(size(WF_data.shift{i}));<br> for j = 1:WF_cases_n(i)<br> tmp_P(j) = mean(mean( WF_data.WF_Pm{i}{j}(row_start:row_end,:)));<br> end<br> px = WF_data.shift{i} ;<br> py = tmp_P;<br> pw = 0.05;<br> pe = zeros(size(px))+0.02; %for uncertainty value see Bossuyt et al. (2018) Physical Review Fluids. <br> for j = 1:WF_cases_n(i)<br> plot( [px(j)-pw/2 px(j)+pw/2] , [py(j)+pe(j) py(j)+pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br> plot( [px(j)-pw/2 px(j)+pw/2] , [py(j)-pe(j) py(j)-pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br> plot( [px(j) px(j)],[py(j)-pe(j) py(j)+pe(j)],':', 'Color', line_color(i,:),'LineWidth',0.5)<br> end<br> end<br> xlabel('\Delta_y [D]')<br> ylabel('$\langle P_i /P_1\rangle_{16}^{19}$','Interpreter','Latex')<br> box('on')<br> ylim([0.27 0.52])<br> xlim([-0.1 2.6])<br> legend1 = legend(legend_items');<br> set(legend1,'Location','southeast');<br> print(f2, 'WF_Pm_end', '-dpng','-r300')</p> <p>%% plot average unsteady loading total farm<br> row_start = 1;<br> row_end = 19;<br> f3 = figure;<br> set(gcf,'paperposition',[0,0,8.4,4.9])<br> hold on<br> for i = 1 : length(WF_cases_selected)<br> tmp_TI = zeros(size(WF_data.shift{i}));<br> for j = 1:WF_cases_n(i)<br> tmp_TI(j) = mean(mean(WF_data.WF_U_rms{i}{j}(row_start:row_end,:)./WF_data.WF_Um{i}{j}(row_start:row_end,:)))*100;<br> end<br> plot( WF_data.shift{i} , tmp_TI , line_tick{i} ,'Color', line_color(i,:) ,'MarkerFaceColor', line_color(i,:))<br> end</p> <p>% manualy plot errorbars<br> for i = 1:length(WF_cases_selected)<br> tmp_TI = zeros(size(WF_data.shift{i}));<br> for j = 1:WF_cases_n(i)<br> tmp_TI(j) = mean(mean(WF_data.WF_U_rms{i}{j}(row_start:row_end,:)./WF_data.WF_Um{i}{j}(row_start:row_end,:)))*100;<br> end<br> px = WF_data.shift{i} ;<br> py = tmp_TI;<br> pw = 0.05;<br> pe = zeros(size(px))+ 0.004*tmp_TI;%for uncertainty value see Bossuyt et al. (2018) Physical Review Fluids. <br> for j = 1:WF_cases_n(i)<br> plot( [px(j)-pw/2 px(j)+pw/2] , [py(j)+pe(j) py(j)+pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br> plot( [px(j)-pw/2 px(j)+pw/2] , [py(j)-pe(j) py(j)-pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br> plot( [px(j) px(j)],[py(j)-pe(j) py(j)+pe(j)],':', 'Color', line_color(i,:),'LineWidth',0.5)<br> end<br> end<br> xlabel('\Delta_y [D]')<br> ylabel('$ \langle TI \rangle_{1}^{19} [\%]$','Interpreter','Latex')<br> box('on')<br> xlim([-0.1 2.6])<br> legend1 = legend(legend_items');<br> set(legend1,'Location','northeast');<br> print(f3, 'WF_TI_all','-dpng','-r300')</p> <p>%% plot average unsteady loading end of farm<br> row_start = 16;<br> row_end = 19;<br> f4 = figure;<br> set(gcf,'paperposition',[0,0,8.4,4.9])<br> hold on<br> for i = 1 : length(WF_cases_selected)<br> tmp_TI = zeros(size(WF_data.shift{i}));<br> for j = 1:WF_cases_n(i)<br> tmp_TI(j) = mean(mean(WF_data.WF_U_rms{i}{j}(row_start:row_end,:)./WF_data.WF_Um{i}{j}(row_start:row_end,:)))*100;<br> end<br> plot( WF_data.shift{i} , tmp_TI , line_tick{i} ,'Color', line_color(i,:) ,'MarkerFaceColor', line_color(i,:))<br> end</p> <p>% manualy plot errorbars<br> for i = 1:length(WF_cases_selected)<br> tmp_TI = zeros(size(WF_data.shift{i}));<br> for j = 1:WF_cases_n(i)<br> tmp_TI(j) = mean(mean(WF_data.WF_U_rms{i}{j}(row_start:row_end,:)./WF_data.WF_Um{i}{j}(row_start:row_end,:)))*100;<br> end<br> px = WF_data.shift{i} ;<br> py = tmp_TI;<br> pw = 0.05;<br> pe = zeros(size(px))+ 0.01*tmp_TI;%for uncertainty value see Bossuyt et al. (2018) Physical Review Fluids. <br> for j = 1:WF_cases_n(i)<br> plot( [px(j)-pw/2 px(j)+pw/2] , [py(j)+pe(j) py(j)+pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br> plot( [px(j)-pw/2 px(j)+pw/2] , [py(j)-pe(j) py(j)-pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br> plot( [px(j) px(j)],[py(j)-pe(j) py(j)+pe(j)],':', 'Color', line_color(i,:),'LineWidth',0.5)<br> end<br> end<br> xlabel('\Delta_y [D]')<br> ylabel('$ \langle TI \rangle_{16}^{19} [\%]$','Interpreter','Latex')<br> box('on')<br> xlim([-0.1 2.6])<br> legend1 = legend(legend_items');<br> set(legend1,'Location','northeast');<br> print(f4, 'WF_TI_end','-dpng','-r300')</p> <p> </p>
AWESCO Wind Field Datasets
<p>AWESCO Wind Field Datasets</p> <p>The present datasets contain time-resolved three-dimensional wind field data computed by means of large-eddy simulations. The atmospheric boundary layer is modelled as pressure driven boundary layer (PDBL) and the computations are performed using the software SPWind developed at KU Leuven. Wind field data is provided for three different roughness classes corresponding to offshore<br> and onshore conditions. For each roughness class, 45 minutes of wind data, sampled every second, is available and stored in HDF5 format for time series of 15 minutes. The file size is approximately 10GB. The data can be accessed using processing scripts provided for both Python and MATLAB. For more information, please read the provided documentation.</p>
Dataset and code for "Classification of Solar Wind With Machine Learning"
<p>Matlab software and data from http://www.mlspaceweather.org/ for the paper</p> <p>https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017JA024383</p>
Regional HYCOM output: absolute and relative wind forcing experiments
<p>Hybrid Coordinate Ocean Model (HYCOM) output from 2 forced regional simulations of the Agulhas Current. The first experiment is forced by absolute winds, the second experiment is forced by relative winds (the wind speed relative to the current speed). Data uploaded here are the sea surface height and surface u and v velocities, for both experiments. This is weekly output from January 1993- December 2013 at 1/10°. Also uploaded is a vertical section of the HYCOM output along the ACT transect in the Agulhas Current (~33.4°S at the coast and extending 300km offshore) for both experiments from 2010- 2013.</p> <p>For more information on the data please refer to "L. Braby, Backeberg, B., Krug M. and Reason C. (in prep), Quantifying the impact of wind-current feedback on mesoscale variability in forced simulation experiments of the Agulhas Current using an eddy tracking algorithm."</p>
Wind fields from aggregated retrievals from the WIRA-C Doppler wind radiometer in tropical and arctic lattitudes
<p>These data sets contain the retrieved wind fields from aggregated retrievals from the WIRA-C Doppler wind radiometer from two campaigns.</p> <p>The first campaign took place in the southern hemisphere at the Maïdo observatory on La Réunion Island (France), located in the Indian ocean at 21°S, 55°E. Data from April, May and June 2017 are included.</p> <p>For the second (and still ongoing) campaign, WIRA-C is located at the ALOMAR observatory on Andøya (Norway) at 69°N, 16°E. Data from September, October and November are included.</p>
3D wind speed and CO2/H20 concentration measurements collected during austral summer 2017/2018 over an ice free surface of a shallow lake located in the Schirmacher oasis, East Antarctica.
<p>The data set includes measurements collected by the integrated CO2 and H2O open-path gas analyzer and 3-D sonic anemometer (Irgason by Campbell Scientific with serial number 1243, https://www.campbellsci.com/irgason). The instrument was operated from 01.01.2018 to 07.02.2018. It was deployed on the north-west shore of the Lake Zub/Priyadarshini (S70° 45′ 41.5″, E011° 44′ 16.6″) on the distance of 10 m from the coast. The instrument was placed on the aluminum tripod on the height of 2 m, and directed to south-eastwards (137 SE). Six metal guidelines were linked to anchors, and the boom was fixed on the tripod. Two rechargeable batteries (12V/33Ah) were used in additional to two solar panels to power supply of the instrument (irgason_deployment.jpg). The format of the output files is given in Irgason_output.pdf. The raw data are packed into the *.dat files (one per day) and then compressed (bz2). The calibration of the Irgason was done 21.08.2017 in the lab of the Finnish Meteorological Institute with standard zero-and-span procedure, and then the instrument is adjusted accordingly.</p>
Wave and wind data from the Helsinki archipelago and Gulf of Finland
<p>Data source: Finnish Meteorological Institute</p> <p>This is wave and meteorological data collected in the Helsinki archipelago and GoF durin 2012-2018. Each file contains data and metadata for one location. The Gulf of Finland (GoF) site has a separate file for all integrated data (WaveData_GoF_integrated.nc), while separate files (WaveData_GoF_spectra_2016a.nc etc) exist for the spectra. This is because both a DWR Mk-III and DWR4/ACM wave buoy was used, and they have different sampling frequencies. Coinciding wind data is embedded in each file.</p> <p>The data are described in the publication "The wave spectrum in archipelagos", Ocean Science, 2019, DOI: 10.5194/os-15-1469-2019</p> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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.