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Turbulent kinetic energy over large wind farms observed and simulated by the mesoscale model WRF (3.8.1)
<p>This repository contains the WRF configuration files necessary to reproduce the simulations <br> as described in Siedersleben et al. 2019 (https://doi.org/10.5194/gmd-2019-100)</p> <p>The file windturbines_GMD.txt contains the locations of <br> all windturbines implemented in the simulations. The corresponding attributes of each <br> wind turbine type is described in the wind-turbine-xx.tbl. Be aware that all windturbines use the same power and thrust coefficients only the different hub heights and rotor diameters are taken into account as described in Siedersleben et al. (2019).</p> <p>The namelist.input_nameOfSimulation files necessary to run the simulations are provided in this repository as well. You may notice that <br> there are less namelist files than simulations. The simulations not using a TKE source use the same namelists as the ones with a TKE a source. However, the WRF model needs to be recompiled using the manipolated module_wind_fitch.F (you find this file in this repository). The sensitivity studies investigating the impact of the uncertainties in the power and thrust coefficients use the namelist of the control simulation CNTRb, but with manipulated wind-turbine-x_modMin/Max.tbl wind turbine files.</p> <p>The two python files get_era5*.py can be used to retrieve the ERA5 data, driving the WRF model. <br> Note that the dates and pathes have to be adjusted in the python files. <br> After downloading the surface and model level data some postprocessing <br> is necessary as described nicely here: "http://valcap74.blogspot.com/2017/10/how-to-run-wrf-model-driven-by-era5-on.html". For this<br> purpose the simple script called postProcessERA5 (based on the blog entry mentioned above) can be used.</p>
Intermittency in wind-driven surface alteration on Mars interpreted from wind streaks and measurements by InSight
<p>Shapefiles associated with the GRL publication: Intermittency in wind-driven surface alteration on Mars interpreted from wind streaks and measurements by InSight</p>
Observed and WRF-simulated air temperature and wind speed at the Czech Hydrometeorological Institute weather stations Lučina, Lysá hora and Olomouc
<p>The dataset contains two csv files with observed 2-m air temperature and 10-m wind speed data at Lučina, Lysá hora and Olomouc meteorological stations in the Czech Republic and analogical time series produced by the Weather Research and Forecasting (WRF) model. The dataset covers a period of 27 October 2010, 01:00 UTC to 01 November 2010, 00:00 UTC. WRF output is given for three model configurations:</p> <p>1) QNSE boundary layer scheme</p> <p>2) 3DTKE boundary layer scheme with Revised MM5 surface layer scheme</p> <p>3) 3DTKE boundary layer scheme with MYNN surface layer scheme</p>
Geostrophic wind shear from CFSR v2 data for usage in WAsP
<p>The change of the geostrophic wind speed has an impact on boundary layer mixing that can be important for microscale flow modelling for wind energy purposes. The WAsP software is often used for this purpose. This dataset contains the climatological geostrophic wind shear and direction over the whole global on a 0.5 degree grid that has been used in WAsP 12. It was obtained from the 6-hourly CFSR v2 reanalysis for the period 2011 to 2017 (see https://doi.org/10.5065/D61C1TXF). The omni-directional geostrophic wind shear vector denotes how much the geostrophic wind speed is changing over a certain vertical distance. Because we are interested in geostrophic wind shear changes that contribute to turbulent mixing in the atmospheric boundary layer, it was estimated by using the data on pressure levels from the pressure level closest to the surface up to 500 hPa above that heights.p><p dir="ltr">More details about the implementation of the model in the WAsP software and a validation can be found in the corresponding technical report:<br>Floors, R. R., Troen, I., & Kelly, M. C. (2018). <i>Implementation of large-scale average geostrophic wind shear in WAsP12.1i>. DTU Wind Energy. DTU Wind Energy E No. 0169p><p><br>p><ul><li>meandgdz_2010_2017_CFSRv3.nc: version with coordinate reference system in the coordinates for usage in GIS programs. NaN values at the poles are filled with 0.0, i.e. assuming barotropic atmosphere, which avoids crashes in the pywasp code. A single sector variable has been added, indicating that these values are valid for all wind direction, as opposed to other files that have values for each wind direction sector (for example: https://data.dtu.dk/articles/dataset/ERA5_atmospheric_stability_for_usage_in_WAsP_12_8/19576042). Naming conventions are in accordance with the windkit package (https://docs.wasp.dk/windkit/)</p> <p>Mirror of https://data.dtu.dk/articles/dataset/Geostrophic_wind_shear_from_CFSR_v2_data_for_usage_in_WAsP/21975482</p>
Supplementary material (part 2): "Evaluation of AROME Model Valley Wind Simulations in the Inn Valley, Austria"
<p><em>Part 2</em> of supplementary material for the Master's Thesis: "Evaluation of AROME Model Valley Wind Simulations in the Inn Valley, Austria" (Wibmer 2024, available <a href="https://resolver.obvsg.at/urn:nbn:at:at-ubi:1-151801">here</a>).</p> <p>Due to memory constraints, the supplementary material consists of two parts:</p> <ul> <li><em><strong>Part 1: </strong></em>(available <a href="https://doi.org/10.5281/zenodo.10849397" target="_blank" rel="noopener">here</a>) Includes Python scripts and model setup files, along with the first part of the datasets, including ERA-reanalysis data, observational data, and the preprocessed AROME model output (NetCDF files) of the <em>0.5-km</em> simulation.</li> <li><em><strong>Part 2: </strong></em>Includes the preprocessed AROME model output (NetCDF files) of the <em>1.0-km </em>and <em>2.5-km</em> simulations (see description below).</li> </ul> <p>To reproduce part of the figures, users must download the Python scripts and the preprocessed AROME model datasets (NetCDF files). <br>The Python scripts should be placed in the same parent folder because some of them depend on each other <strong>(!! Important !!).</strong><br>Original AROME model output files (GRIB2 format) are not published due to their large size.</p> <p>The naming convention for the AROME simulations uses OP* (where * represents the grid spacing in meters) to differentiate the model runs based on their horizontal<br>grid spacing:</p> <ul> <li><strong><em>OP2500:</em></strong> for 2.5 km</li> <li><em><strong>OP1000: </strong></em>for 1.0 km</li> <li><em><strong>OP500:</strong></em> for 0.5 km</li> </ul> <h3><strong>Datasets Part 2</strong></h3> <p>Due to memory constraints, the datasets needed for the analyses are split up into two parts. The second part of the supplementary material contains:</p> <ul> <li><strong>datasets_OP1000.tar.xz</strong>: Contains the preprocessed AROME-Aut model output data (NetCDF files) of the <em>1.0-km</em> simulation. </li> <li><strong>datasets_OP2500.tar.xz</strong>: Contains the preprocessed AROME-Aut model output data (NetCDF files) of the <em>2.5-km</em> simulation. </li> </ul> <p>The datasets of the<em> 0.5-km</em> simulation (<strong>datasets_OP500.tar.xz</strong>)<strong> </strong>can be found in Part 1 of the supplementary material (available <a href="https://doi.org/10.5281/zenodo.10849397" target="_blank" rel="noopener">here</a>).<br>The NetCDF datasets of the performed AROME-Aut simulations are packaged and compressed into <code><em><strong>.tar.xz</strong></em></code> files.<br>The Python scripts, available <a href="https://doi.org/10.5281/zenodo.10849397" target="_blank" rel="noopener">here</a>, require these NetCDF datasets for plotting and analyses routines.<br>The provided NetCDF datasets are preprocessed from the <em>GRIB2</em> output of the AROME-Aut simulations. <br>For the scripts to function properly, you need to adjust the path to the datasets within <code><strong>path_handling.py</strong></code><strong>.</strong></p> <p>Each <strong><code>datasets_OP*.tar.xz</code></strong> file contains NetCDF files for different type of levels: <em>surface, hybridPressure </em>(model levels)<em>, isobaricInhPa </em>(pressure levels),<em> meanSea </em>(mean sea level)<em>, heightAboveGround </em>(constant height levels)<em>. </em> The following naming convention for the datasets is used:</p> <ul> <li><strong>ds_OP*_<em>var</em>_hybridPressure_<em>[lon1, lon2, lat1, lat2]</em>.nc</strong>: Contains data on <em>hybrid pressure model levels</em> for a specific variable (<em>var</em>; e.g., <em>u, v, z, pres, q, t</em>) for the geographical extent defined in the brackets. </li> <li><strong>ds_OP*_interp_hybridPressure_<em>(lon,lat)</em>.nc</strong>: Combined dataset on <em>hybrid pressure model levels.</em> The data is bilinearly interpolated to the specified location <em>(lon, lat)</em>.</li> <li><strong>ds_OP*_<em>var</em>_surface_<em>whole</em>.nc</strong>: Contains data on <em>model surface </em>for a specified variable (<em>var</em>; e.g., <em>z, sp, t, tcc</em>) for the <em>whole </em>available domain extent.</li> <li><strong>ds_OP*_heightAboveGround_instant_<em>whole</em>.nc</strong>: Combined dataset on <em>height levels </em>(e.g., <em>2-m and 10-m</em>) for the <em>whole </em>available domain extent.</li> <li><strong>ds_OP*_<em>var</em>_meanSea_<em>whole</em>.nc</strong>: Contains data on <em>mean sea level</em> for specified variable (<em>var;</em> e.g., <em>prmsl</em>) for the <em>whole</em> available domain extent. </li> </ul> <p>The original GRIB2 files are not provided due to their large size. For further information about the GRIB2 files or the NetCDF datasets, please feel free to contact me.</p>
CLIMAtic investigation of THUNDERstorm winds
<p>In the framework of the EU Project ERIES : Engineering research Infrastrucures for European Synergies, an experimental campaign was carried out at the Jules Verne Climatic Wind Tunnel Thermal Unit SC2 at the Centre Scientifique et Technique du Bâtiment (CSTB) in Nantes, France.</p> <p>The aim of the experimental activity was to assess the geometric and dynamics characteristics of downburst like impinging jet winds by varying the temperature difference ∆ T between the impinging jet flow and the calm surrounding environment. This could be realized at large geometric scales and Reynolds number thanks to the large.</p> <p>The projet associated University of Genoa, Italy and Centre Scientifique et Technique du Bâtiment, Nantes, France.</p> <p>Contact persons : Federico Canepa <span><span><a title="mailto:federico.canepa@unige.it" href="mailto:federico.canepa@unige.it" target="_blank" rel="noreferrer noopener">federico.canepa@unige.it</a>, Anthony Guibert, <a title="mailto:anthony.guibert@cstb.fr" href="mailto:anthony.guibert@cstb.fr" target="_blank" rel="noreferrer noopener">anthony.guibert@cstb.fr</a> </span></span></p>
Wind turbine blade structural health monitoring dataset
<p>The dataset is related to a unique experiment conducted at ETH Zurich in collaboration with the Institute of Fluid Flow Machinery, Polish Academy of Sciences. The synchronisation between fatigue loading and guided wave excitation and sensing is unique. The dataset can be used to construct and test damage indexes for structural health monitoring.</p> <p>The tests were carried out on a Sonkyo Windspot 3.5 kW wind turbine blade equipped with strain gauges, a thermocouple, and five piezoelectric transducers. One piezoelectric transducer was used for Hann windowed sine excitation whereas the remaining piezoelectric transducers were used as sensors. The fatigue loading was induced by using a 1 kN capable Tira shaker. The fatigue program is explained in the readme.txt file and involves overloading the blade with a crane up to the blade's failure. The shaker was excited by a sine signal of frequency around the first resonant frequency of the wind turbine blade. The synchronisation with guided wave excitation was realised during three characteristic moments: (1) at maximum amplitude of sine, (2) at zero crossing, and (2) at the minimum amplitude of sine. This stage of the experiment is called 'dynamic' for short, and the data is stored in respective 'raw' folders. After each set of 1000 cycles, the shaker was stopped until the blade stopped vibrating. Then another set of guided wave measurements was taken at the blade's rest position. This stage of the experiment is called 'static' for short, and the data is stored in respective 'average' folders. It contains signals averaged over 10 measurements. During the whole process strain as well as temperature were measured.</p> <p>Three files are included for data visualization: (1) 'plot_strain_temperature.m', (2) 'read_plot_static.m', and (3) 'read_plot_dynamic.m'. These are MATLAB scripts showing how to load data and visualize the dependence of strains and temperatures on fatigue cycle number or time, plot exemplary signals of guided waves, and construct a damage index for structural health monitoring of the wind turbine blade.</p> <p>The details of experimental setup can be found in the paper.</p>
Twin Test 2: Wake interactions of a cluster of turbines and wake steering techniques. Wind tunnel data.
<p>The aerodynamic performance of two identical wind turbine models was characterized under various static and dynamic conditions in a synchronous configuration within the wind tunnel test section. Two experimental campaigns were performed at Technische Universität München (TUM) and at the National Technical University of Athens (NTUA) to investigate wake flow control techniques. This document contains the necessary information to understand the performed experiments and to access and use the available data. While both experimental set ups are detailed, only data from the TUM campaign are available at the time of writing, as the NTUA campaign results will form Phase II of an ongoing blind test campaign and cannot be published.</p>
Site-specific results DeltaWind + Innwind 10MW Reference Wind Turbine
<p><strong>Digitalización Offshore EU Project - CENER- Digital Twin site specific results.</strong></p> <p>Related dataset:</p> <ul> <li><a href="https://zenodo.org/records/14070345">Virtual results DeltaWind + Innwind 10MW Reference Wind Turbine</a></li> </ul> <p>Related presentation:</p> <ul> <li><a href="https://zenodo.org/records/14067010">Digitalización de parques eólicos</a></li> </ul> <p>Simulation of floating offshore wind turbine</p> <ul> <li>DeltaWind platform + Innwind 10 MW Reference Wind Turbine)</li> <li>Meteocean conditions of Canary Islands</li> <li>Depth: 350 m</li> </ul> <p>Simulations specifications:</p> <ul> <li>Simulation carried out with OpenFAST v3.4.1 version <a href="https://github.com/OpenFAST/openfast/releases/tag/v3.4.1">Release v3.4.1 · OpenFAST/openfast</a></li> <li>CENER in-house controller</li> <li>400 s transient removed</li> </ul> <p> </p> <p><strong>Dataset: </strong></p> <p>zip that contains 243 csv files.</p> <p>Each file containing one-hour time series of load simulation (time step 0.5s). </p> <p><strong>Filenames</strong> specify details about the simulation:</p> <ul> <li>dlc - Design Load Case [12 : normal power production, 64 : idling]</li> <li>Vh [wind speed]</li> <li>Y [yaw angle]</li> <li>W [wave height _ period]</li> <li>D [wind direction]</li> <li>M [wave misalignment= wave direction with respect to wind direction]</li> </ul> <p>The <strong>columns </strong>of each csv file includes followind signals according to OpenFAST nomenclature and reference frames:</p> <ul> <li>Time (s)</li> <li>PtfmSurge (m)</li> <li>PtfmSway (m)</li> <li>PtfmHeave (m)</li> <li>PtfmRoll (deg)</li> <li>PtfmPitch (deg)</li> <li>PtfmYaw (deg)</li> <li>GenPwr (kW)</li> <li>RotThrust (kN)</li> <li>GenTq (kN-m)</li> <li>RotSpeed (rpm)</li> <li>BlPitch1 (deg)</li> <li>TipDxc1 (m)</li> <li>RootMxc1 (kN-m)</li> <li>RootMyc1 (kN-m)</li> <li>TwrBsMxt (kN-m)</li> <li>TwrBsMyt (kN-m)</li> <li>FAIRTEN1 (N)</li> <li>FAIRTEN2 (N)</li> <li>FAIRTEN3 (N)</li> </ul>
Virtual results DeltaWind + Innwind 10MW Reference Wind Turbine
<p><strong>Digitalización Offshore EU Project - CENER- Digital Twin site specific results.</strong></p> <p>Related dataset:</p> <ul> <li><a href="https://zenodo.org/records/14068807">Site-specific results DeltaWind + Innwind 10MW Reference Wind Turbine</a></li> </ul> <p>Related presentation:</p> <ul> <li><a href="https://zenodo.org/records/14067010">Digitalización de parques eólicos</a></li> </ul> <p> </p> <p>Simulation of floating offshore wind turbine</p> <ul> <li>DeltaWind platform + Innwind 10 MW Reference Wind Turbine)</li> <li>Virtual Meteocean conditions </li> <li>Depth: 350 m</li> </ul> <p>Simulations specifications:</p> <ul> <li>Simulation carried out with OpenFAST v3.4.1 version <a href="https://github.com/OpenFAST/openfast/releases/tag/v3.4.1">Release v3.4.1 · OpenFAST/openfast</a></li> <li>CENER in-house controller</li> <li>400 s transient removed</li> </ul> <p> </p> <p><strong>Dataset:</strong></p> <p>csv that contains statistics from 4320 simulations</p> <p>Statistics obtained from one-hour time series of load simulations</p> <p>- The <strong>definition</strong> of the simulations are included in the csv through the <strong>columns</strong>:</p> <ul> <li>DLC - Design Load Case [12 : production, 64 : idling]</li> <li>Wind Speed </li> <li>WaveHeight</li> <li>Wave Period</li> <li>Wind Direction</li> <li>Wave Direction</li> </ul> <p>- The <strong>statistics calculated</strong> are included in columns:</p> <ul> <li> <div>BlPitch1_avg (deg)</div> </li> <li> <div>FAIRTEN1_avg (N)</div> </li> <li> <div>FAIRTEN2_avg (N)</div> </li> <li> <div>FAIRTEN3_avg (N)</div> </li> <li> <div>GenPwr_avg (kW)</div> </li> <li> <div>GenTq_avg (kN-m)</div> </li> <li> <div>PtfmHeave_avg (m)</div> </li> <li> <div>PtfmPitch_avg (deg)</div> </li> <li> <div>PtfmRoll_avg (deg)</div> </li> <li> <div>PtfmSurge_avg (m)</div> </li> <li> <div>PtfmSway_avg (m)</div> </li> <li> <div>PtfmYaw_avg (deg)</div> </li> <li> <div>RootMxc1_avg (kN-m)</div> </li> <li> <div>RootMyc1_avg (kN-m)</div> </li> <li> <div>RotSpeed_avg (rpm)</div> </li> <li> <div>RotThrust_avg (kN)</div> </li> <li> <div>TipDxc1_avg (m)</div> </li> <li> <div>TwrBsMxt_avg (kN-m)</div> </li> <li> <div>TwrBsMyt_avg (kN-m)</div> </li> <li> <div>BlPitch1_std (deg)</div> <p> </p> </li> </ul>
Power production from the U.S. east coast offshore wind lease areas
<p>The accompanying file include information regarding the set up of WRF simulations of power production and wake extents from offshore wind lease areas along the U.S. east coast, and also data presented in figures in the "Wind power production from the U.S. east coast offshore lease areas" paper and the associated MATLAB data processing code.</p> <p>The US Department of Energy Office of Science (DE-SC0016605), the US Department of Energy Office of Energy Efficiency and Renewable Energy and New York State Energy Research and Development Authority via the National Offshore Wind Research and Development consortium (147505) funded this research. This research was enabled by computational resources supported by the U.S. National Science Foundation via the Extreme Science and Engineering Discovery Environment (XSEDE) (award TG-ATM170024) and ACI-1541215, and those of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.</p>
Wind speed and power potential for Switzerland
<p>When using the provided data, please cite the following article:</p> <p>Amato, F., Guignard, F., Walch, A., Mohajeri, N., Scartezzini, J. L., & Kanevski, M. (2021). Spatio-temporal estimation of wind speed and wind power using machine learning: predictions, uncertainty and technical potential. arXiv preprint arXiv:2108.00859.</p> <p> </p> <p><strong>Summary: </strong></p> <p>This dataset contains an estimation of the average yearly wind speed and of the wind power potential for Switzerland, at a spatial resolution of 250 x 250 meters and over the period from 2008 to 2017.</p> <p>Wind speed data are obtained by modelling data collected at an hourly frequency on a set of up to 208 monitoring stations over the country. The data are then interpolated using a spatio-temporal machine learning model, allowing the estimation of wind speed and its uncertainty at unsampled locations. Then, the modelled spatio-temporal wind speed field is used to estimate the wind power. This is computed based on the characteristic parameters of an Enercon E-101 wind turbine at 100 meters hub height. The latter indicates the distance from the turbine platform to the rotor of an installed wind turbine, showing how high the turbine stands above the ground without considering the length of the turbine blades.</p> <p>The hourly estimations of wind speed are then averaged over each of the ten years studied, for each 250 x 250 spatial location, while wind power data are summed over each year for each spatial unit. Advantages and limitations of the proposed method are discussed in Amato et al. (2021).</p> <p><strong>Data description: </strong></p> <p>The hourly estimation of wind speed and power for Switzerland from 2008 to 2017 are available under request. Here we share the annual values. For both wind speed and power, the data are available over 660697 spatial units of 250 x 250 meters each, covering the entire Swiss territory. Check details in the file Data_description.pdf.</p> <p>Data are provided in the pickle format, see <a href="https://docs.python.org/3/library/pickle.html#module-pickle">https://docs.python.org/3/library/pickle.html#module-pickle</a>.</p>
Tri-hourly dataset of wind and wave anomalies of the GFS and WAVEWATCH III models in the entire tropic region (TROPWA).
<p>This dataset contains the anomalies of the total height and peak period of the waves and of the zonal and meridional components of the wind at 10 m above the sea surface obtained from the outputs of the GFS and WAVEWATCH III coupled global models in the entire tropical region (180°W to 178.75°E longitude/30°S to 30°N latitude), with a spatial resolution of 1.25°x1°. It is made up of two files in NetCDF format, where the data for wind anomalies (speed module and its zonal and meridional components) and wave anomalies (total wave height and peak period) are contained separately. This dataset was created in order to study all types of tropical storms, cold fronts and other physical processes that influence significant changes in wave parameters, as well as for the design and construction of coastal engineering works. The authors thanks to Tropical Atlantic Interdisciplinary Laboratory on physical, biogeochemical, ecological and human dynamics (IJL TAPIOCA).</p>
zEPHYR - Large On Shore Wind Turbine Benchmark
<p>Large On Shore Wind Turbine Benchmark - This benchmark collects data for the validation of wind turbine noise prediction methods to be applied in realistic weather conditions. It includes metmast data for the weather prediction model validation, acoustic measurements and an approached model of the SWT2.3-93 wind turbine used during the test campaign, as well as the corresponding<br> CAD.</p>
Result data related to "Cost-potential curves of onshore wind energy: the role of disamenity costs"
<p>This dataset estimates the impact of incorporating disamenity costs of wind onshore in Europe (in addition to technology cost). The data haset has been generated and used for the publication:</p> <blockquote> <p>Ruhnau, O., Eicke, A., Sgarlato, R., Tröndle, T., Hirth, L., 2022. Cost-potential curves of onshore wind energy: the role of disamenity costs. Environmental and Resource Economics. DOI: <a href="https://doi.org/10.1007/s10640-022-00746-2">10.1007/s10640-022-00746-2</a></p> </blockquote> <p>The corresponding code is published on <a href="https://github.com/timtroendle/wind-onshore-cost-potential">GitHub</a>.</p> <p>The dataset includes:</p> <ol> <li>Maps that exhibit the population count within a predefined distance (e.g., "population-within-1km.tif") and the resulting disamenity costs ("disamenity-cost.tif")</li> <li>Tables that summarize the engineering and disamenity costs faced at each potential turbine location in the EU (e.g., "turbines-AT.csv")</li> </ol>
Datasets of the work named Development of a Low-Cost Smart Sensor GNSS System for Real-Time Positioning and Orientation for Floating Offshore Wind Platform
<pre>- 1_Motion_Simulator/ - IMU_results/ - 20211028101756.csv - 20220114101543.csv - 20220117000000.csv - Rotary_Table/ - 15/ - rover_20220105.nav - rover_20220105.obs - solution_20220105_CAS.log - 360/ - rover_20211221.nav - rover_20211221.obs - solution_20211221_CAS.log - 360-15/ - rover_20220202_CAS.nav - rover_20220202_CAS.obs - solution_20220202_CAS.log - Static_Tests/ - solution_SSRA00CAS0 - solution_SSRA00WHU0 - 2_GNSS_Signal_Simulator/ - platformmov_C1.xtd - platformmov_C2.xtd - platformmov_C3.xtd - TestBetaNoneMov_C1 - TestBetaNoneMov_C1.nav - TestBetaNoneMov_C1.obs - TestBetaNoneMov_C1.ubx - TestBetaNoneMov_C2 - TestBetaNoneMov_C2.nav - TestBetaNoneMov_C2.obs - TestBetaNoneMov_C2.ubx - TestBetaNoneMov_C3 - TestBetaNoneMov_C3.nav - TestBetaNoneMov_C3.obs - TestBetaNoneMov_C3.ubx - 3_Test_Sea/ - 20220503000000.xlsx - solution_28.nav - solution_28.obs - solution_28.ubx Background: {Journal Article using this dataset} 'Development of a Low-Cost Smart Sensor GNSS System for Real-Time Positioning and Orientation for Floating Offshore Wind Platform' Paper DOI: <a href="https://doi.org/10.3390/s23020925">https://doi.org/10.3390/s23020925</a> Abstract: a low-cost smart sensor GNSS system has been developed to provide accurate real-time position and orientation measurements on a floating offshore wind platform. The approach chosen to offer a viable and reliable solution for this application is based on the use of the well-known advantages of the GNSS system as the main driver for enhancing the accuracy of positioning. For this purpose, the data reported in this work are captured through a GNSS receiver operating over multiple frequency bands (L1, L2, L5) and combining signals from different constellations of navigation satellites (GPS, Galileo, and GLONASS), and they are processed through the precise point positioning (PPP) and real-time kinematic (RTK) techniques. Furthermore, aiming to improve global positioning, the processing unit fuses the results obtained with the data acquired through an inertial measurement unit (IMU), reaching final accuracy of a few centimeters. To validate the system designed and developed in this proposal, three different sets of tests were carried out in a (i) rotary table at the laboratory, (ii) GNSS simulator, and (iii) real conditions in an oceanic buoy at sea. The real-time positioning solution was compared to solutions obtained by post-processing techniques in these three scenarios and similar results were satisfactorily achieved. </pre>
Wind and Waves in Tropical Cyclones (1985-2022)
<p>Dataset contains information on wind speed and wave height in Tropical Cyclones (TCs) obtained from the Best Track Data during the period from 1985 to 2022 and along-track altimeter measurements from 17 satellites in 1985-2018 (IMOS archive, Ribal and Young, 2019) and in 2020-2022 (CMEMS archive).</p> <p>For each TC, in which the maximum wind speed exceeded 30 m/s (1905 cases), files are created to combine altimetry data on the significant wave height and wind speed in the cyclone area (+-7 degrees from TC center) and information on each cyclone trajectory and its main characteristics (maximum wind speed, radius of maximum winds, heading velocity vector).</p> <p>To describe the radial distribution of wind speed, standard data on distances from the cyclone center to points with wind speeds of 34, 50, and 64 knots are approximated with the analytical function suggested by Holland (1980).</p> <p>For each cyclone, graphical files are provided to illustrate the evolution of every TC parameters, the quality of the wind prifile approximation, the location of altimeter tracks, and the along-track values of significant wave height and wind speed. </p> <p>Files given in NetCDF and MAT formats contain</p> <p>- TC coordinates, heading velocity and direction, maximum wind speed and radius of maximum wind speed every 3 hours</p> <p>- parameters of wind radial distributions for TC central and far zone every 3 hours</p> <p>- altimetry data: time and along-track significant wave height and wind speed in TC region (+-7 degrees from TC center) from satellites GEOSAT, ERS-1, TOPEX, ERS-2, GFO, ENVISAT, JASON-1, JASON-2, JASON-3, SARAL/AltiKa, CryoSat-2, HY-2A, HY-2B, CFOSAT, Sentinel-3A, Sentinel-3B, Sentinel-6А</p> <p> </p> <p>To form the dataset, the NOAA archive with data on tropical cyclones (https://www.ncei.noaa.gov/data/international-best-track-archive-for-climate-stewardship-ibtracs/v04r00/access/netcdf/, DOI :10.48670/moi-00178) and archives CMEMS (https://resources.marine.copernicus.eu/products, DOI:10.48670/moi-00178) and IMOS (https://catalogue-imos. aodn.org.au/geonetwork/srv/rus/catalog.search#/metadata/c6d5c7b4-323e-4979-9364-cdfa59684163, DOI:10.26198/5c184f4a5cd2e) with altimetry data were used .</p> <p> </p>
TEAMx-PC22 (TEAMx pre-campaign 2022) - ACINN Doppler wind lidar data sets (SL88, SLXR142)
<p><strong>ABSTRACT</strong></p> <p>The data sets found here were collected with <a href="http://acinn.uibk.ac.at/">ACINN</a>'s Doppler wind lidars SL88 and SLXR142 in Innsbruck, Austria, in summer 2022 in the framework of the TEAMx pre-campaign 2022 (TEAMx-PC22). The aim of TEAMx-PC22 was to test new instruments, new instrument configurations and new measurement sites to support the planning of the main TEAMx observational campaign (TOC) in 2024/2025. More details about TEAMx can be found at <a href="http://www.teamx-programme.org">http://www.teamx-programme.org</a> as well as in Serafin et al. (2020) and in Rotach et al. (2022).</p> <p><strong>DATA SET DESCRIPTION</strong></p> <p><strong>1. Spatial coverage and locations</strong></p> <p>Measurements with the SL88 and SLXR142 lidar were collected during TEAMx-PC22 in Innsbruck, Austria, at the Campus Innrain of the University of Innsbruck. More specifically, the SLXR142 lidar was located on the rooftop of one of the university buildings (Bruno-Sander-Haus) at Innrain 52f. The SL88 lidar was located in the forecourt of the Campus Innrain, the so-called GEIWI-Forum, next to the Bruno-Sander-Haus. The exact lidar locations are:</p> <ul> <li>SL88: 47.264083°N / 11.384986°E / 575 m MSL</li> <li>SLXR142: 47.26431°N / 11.38529°E / 613 m MSL</li> </ul> <p><strong>2. Temporal coverage</strong></p> <p>The TEAMx-PC22 lasted from mid-May 2022 to early October 2022. However, the SL88 data set contains a shorter period from 11 August to 02 October 2022 (1 Hz data, vertical stares). The SLXR142 data set covers an extended period from 01 May to 31 October 2022 (VAD products, 10-min averages) as this lidar was operated in a semi-permanent mode.</p> <p><strong>3. Instrument details</strong></p> <p><em><strong>General</strong></em></p> <p>Measurements were taken with two scanning Doppler wind lidars, model Stream Line (SL88) and Stream Line XR (SLXR142), manufactured by HALO Photonics. The SL88 and SLXR142 are part of the Innsbruck Atmospheric Observatory (IAO; Karl et al. 2020). Available here are vertical profiles of radial velocity and backscatter data based on vertical stares at 1 Hz for the SL88 lidar and vertical profiles of horizontal winds (10-min averages) derived from plan position indicator (PPI) scans by applying the VAD method for the SLXR142 lidar. PPI scans were performed as continuous motion scans (CSM mode) at an azimuth angle of 70°. For continuous motion scans, the scanner moves continuously (changing its azimuth angle) while data is being acquired.</p> <p><em><strong>Data correction</strong></em></p> <p>No corrections were applied to the data (level0 data).</p> <p><strong>4. Data file structure</strong></p> <p><em><strong>File format</strong></em></p> <p>Provided are data in netCDF format. File names contain date and time information in UTC. The following wildcard characters are used in the file examples below: yyyy - year; mm - month, dd - day; HH - hour, MM - minute, `SS` - second. NetCDF data files are zipped together into the following zip files.</p> <p><em><strong>Zip files</strong></em></p> <p>SL88.zip contains netCDF files of SL88 data structured into subdirectories (one subdirectory for each month, yyyymm, and one for each day, yyyymmdd).</p> <p>SLXR142.zip contains netCDF files of SLXR142 data structured into subdirectories (one subdirectory for each month, yyyymm).</p> <p><em><strong>NetCDF files for uncorrected SL88 data</strong></em></p> <p>Stare_88_yyyymmdd_HH_l0.nc contains vertical stare measurements aggregated together in one netCDF file for each hour (uncorrected level0 data).</p> <p><em><strong>NetCDF files for SLXR142 data products</strong></em></p> <p>yyyymmdd.nc contains vertical profiles of the horizontal wind vector derived from PPI scans by applying the VAD technique. Each vertical profile is based on several PPI scans conducted at an elevation angle of 70° within 10 minutes. Hence, each profile represents a 10-min average. Profiles are aggregated together for each day in a separate netCDF file.</p> <p><strong>6. Contact</strong></p> <p>Contact alexander.gohm(at)uibk.ac.at for any questions regarding the data set.</p> <p><strong>7. References</strong></p> <p>Karl, T., A. Gohm, M.W. Rotach, H.C. Ward, M. Graus, A. Cede, G. Wohlfahrt, A. Hammerle, M. Haid, M. Tiefengraber, C. Lamprecht, J. Vergeiner, A. Kreuter, J. Wagner, M. Staudinger, 2020: Studying urban climate and air quality in the Alps: The Innsbruck Atmospheric Observatory. <em>Bulletin of the American Meteorological Society,</em> <strong>101,</strong> E488–E507, <a href="https://doi.org/10.1175/bams-d-19-0270.1">https://doi.org/10.1175/bams-d-19-0270.1</a></p> <p>Serafin, S., M. W. Rotach, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. De Wekker, M. Evans, V. Grubišić, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Raudzens Bailey, J. Schmidli, G. Wohlfahrt, B. Zardi, 2020: <em>Multi-scale transport and exchange processes in the atmosphere over mountains: Programme and experiment.</em> Innsbruck University Press. <a href="https://doi.org/10.15203/99106-003-1">https://doi.org/10.15203/99106-003-1</a></p> <p>Rotach, M. W., S. Serafin, H. C. Ward, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. D. Wekker, V. Grubišic, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Bailey, J. Schmidli, C. Wittmann, G. Wohlfahrt, D. Zardi, 2022: A collaborative effort to better understand, measure, and model atmospheric exchange processes over mountains. <em>Bulletin of the American Meteorological Society,</em> <strong>103,</strong> E1282–E1295. <a href="https://doi.org/10.1175/bams-d-21-0232.1">https://doi.org/10.1175/bams-d-21-0232.1</a></p>
Three-component modelling of O-rich AGB star winds I. Effects of drift using forsterite – dataset
<p>The data provided here include all parameter files, log files, and a set of the<br> binary output files that are the basis for the publication in A&A.</p> <p>The file 'file_listing.txt' contains a complete list of files and directories<br> in all gzipped tar files. Each individual gzipped tar file is formatted as<br> follows:</p> <p> Mm.m_Ll.ll_Ttttt.tar.gz</p> <p>where<br> m.m :: the assumed mass of the model, in solar masses<br> l.ll :: The assumed luminosity, in log10(solar luminosities)<br> tttt :: The effective temperature of the star, in Kelvin.</p> <p><br> The contents of the tar files vary according to the model, but here is the<br> general directory structure:</p> <p> nodr/ :: non-drift / PC models<br> drift/ :: drift models</p> <p> nodr/init<br> drift/init :: Initial model files created using John Connor.</p> <p><br> File suffixes are the following:</p> <p> .par :: Plain-text parameter file that contains all parameters that are<br> different from the respective default value in the model.<br> Consequently, to see what parameters were actually used, it is<br> necessary to look in the log file (see below).</p> <p> .bin :: Binary file that contains output of converged models. Each model is<br> stored in two versions, first the previous time step and then the<br> current time step (having access to the model code T-800, data of both<br> time steps are needed to restart model calculations at that time<br> step).</p> <p> The initial model file only contains one model; where the previous<br> time step data are the same as the current time step data.</p> <p> We provide a tool to read this file, see below.</p> <p> Note! These files can get pretty large and are therefore only<br> available for a smaller number of the models in the Zenodo dataset.<br> Please ask the corresponding author for the missing files is the<br> need should appear.</p> <p> .log :: Plain-text log file that shows the used model parameters and a number<br> of key properties for each converged model.<br> The encoding of this file is UTF-8.</p> <p> .inf :: Plain-text secondary log file that contains the header of the<br> [primary] log file as well as timing information.<br> The encoding of this file is UTF-8.</p> <p> .tpb :: Secondary binary file that contains a number of properties specified<br> at the outer boundary, typically for each consecutive time step.</p> <p> We provide a tool to read this file, see below.</p> <p> .lis :: Plain-text file with the iteration history. Unavailable here.</p> <p> .liv :: Plain-text file with values specified for a number of properties at<br> each gridpoint. Unavailable here.</p> <p> .inp :: Plain-text file that is used to launch a model; some are present.<br> This file is automatically generated by the tool that launches T-800<br> and is typically removed when T-800 launches. Unavailable here.</p> <p> .eps :: Encapsulated PostScript file created by John Connor when calculating<br> the initial model.</p> <p><br> Model evolution structure - file endings before the suffix:</p> <p> _rlx :: Files related to relaxing the T-800 calculations on the initial model<br> created by John Connor.</p> <p> _exp :: Files related to expanding the initially compact model to using the<br> full radial domain.</p> <p> _fix :: Files related to the intermediate stage where calculations are changed<br> from expansion to outflow.<br> <br> _out :: Files related to the outflow stage of the calculations; this is what<br> you want to look at to see the wind evolution. Results in the paper<br> are calculated using these data.</p> <p> Note! Some outflow stage calculations continue the evolution of the previous<br> set of files. The underlying reason for continued calculations is typically<br> that the calculated time interval is too short. Such files are typically<br> given the extension '_cont.lin_out', '_cont2.lin_out', etc.</p> <p><br> Stored data in the binary files:</p> <p> The binary files (suffix '.bin') contain the full radial structure in the<br> following 10 (PC models) or 11 (drift models) primary variables:</p> <p> mr: radius<br> mm: integrated [gas] mass<br> md: gas density<br> mu: gas velocity<br> me: internal energy<br> mj: radiative energy<br> mh: radiative flux<br> n0: dust moment, forsterite (Fo)<br> nm: number density of magnesium atoms<br> ns: number density of silicon atoms<br> v0: dust velocity, forsterite (only drift models)</p> <p> Other properties are derived from these primary variables using auxiliary code<br> that isn't part of this dataset.</p> <p><br> Load files:</p> <p> Two tools are provided here that can load the binary data files using the<br> Interactive Data Language (IDL):</p> <p> sc_load_bin (for files with the suffix '.bin'):</p> <p> Loads the full content of a T-800 binary file and returns a structure<br> with the data.</p> <p><br> sc_load_tpb (for files with the suffix '.tpb'):</p> <p> Loads the full content of a T-800 'tpb' binary file and returns a<br> structure with the data.</p> <p> Note! Due to the way models run on clusters, this file is sometimes<br> incomplete; this happens when the model code T-800 is stopped as the<br> cluster-specific walltime is reached. If this is the case, it is<br> necessary to use the binary file instead, where data are saved<br> typically every 20:th time step.</p> <p> Alternative tools for use with Python and Julia could be considered for<br> writing, but where not yet available when this dataset was made public.<br> Please contact the corresponding author for a current status on this issue.</p> <p> </p>
High-wind events on the Southern New England continental shelf (2015-2022), their impact on shelf stratification, and corresponding high-wind event category: Dataset and Code
<p>Dataset of identified high-wind events on the Southern New England continental shelf (2015-2022), their impact on shelf stratification, and corresponding high-wind event category, as well as the associated code to reproduce the figures of accompanying publication. The data have been recorded by the Ocean Observatories Initiative (OOI) Coastal Pioneer New England Shelf Array. </p><p><i>Accompanying publication:</i> Taenzer, L.L., Gawarkiewicz, G., and Plueddemann, A. (2023). Categorization of High-Wind Events and Their Contribution to the Seasonal Breakdown of Stratification on the Southern New England Shelf. Journal of Geophysical Research: Oceans, 128, e2022JC019625. https://doi.org/10.1029/2022JC019625</p><p><i>Contact:</i> Lukas Taenzer (lukas.taenzer@whoi.edu)</p><p><strong>Structure of provided code:</strong></p><ul><li>PART A: Local high-wind ocean impact analysis</li><li>PART B: Analysis of seasonal high-wind impacts on stratification</li><li>PART C: High-wind event categorization and the impact of different categories</li></ul><p>Code has been written in MATLAB R2023a.</p><p><strong>Output:</strong></p><ul><li>Processed data of all locally detected high-wind events incl. scalar forcing and shelf impact estimates as well as their corresponding high-wind event category:<ul><li>'OOIcp_HighWindEvents_ScalarMetrics.nc' (see userflag 'save_peak_ooi')</li><li>See README_HighWindEvents_ScalarMetrics for further details and license.</li></ul></li><li>Figures 2, 3, 4, 5, 6, 7, 8, and 9 of accompanying publication<ul><li>saved as .png file (always)</li><li>saves as .eps file (see userflag 'save_fig_eps')</li></ul></li></ul><p><strong>Input for Analysis:</strong></p><ul><li>Gridded Hydrography and Bulk Air-Sea interactions time series observed by the Ocean Observatories Initiative (OOI) Coastal Pioneer New England Shelf Mooring Array (2015-2022) (Taenzer et al., 2023). The required fields to reproduce the results of the accompanying publication are provided:<ul><li>Input/OOIcp_Met_Combined.nc</li><li>Input/OOIcp_CTD_ISSM_stat.nc</li><li>Input/OOIcp_CTD_PMUI_prof.nc</li></ul></li><li>High-wind event categorization based on their spatio-temporal sea level pressure and temporal surface wind stress signatures around/at the OOI Coastal Pioneer Array location:<ul><li>Input/storm_type_2015-2021_v5.mat</li></ul></li></ul><p><strong>Additional input for reproducing figures:</strong></p><ul><li>Manually determined cyclone tracks for cyclones that occur during the fall destratification seasons 2015-2021:<ul><li>Input/stormtracks_cyclones_20152021_save.mat</li></ul></li><li>ERA5 sea level pressure data (Hersbach et al., 2018) on a 6-hour temporal and a 1°x1° spatial resolution for the time period 2015-01-01 to 2022-06-30 and across the Eastern US, Canada, and the Northwest Atlantic with the OOI Coastal Pioneer Array in the center<ul><li>Input/ERA5_6h_2015-2022_region_1x1.mat</li></ul></li></ul>
ScienceDex guides
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