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HyPer SMM wind tunnel tests: PIV pictures
<p>In this study, windblown sand transport on flat ground is reproduced by means of Wind-Sand Tunnel Tests (WSTT) carried out in the wind tunnel L-1B of von Karman Institute for Fluid Dynamics. The aim of WSTT is twofold. On one hand, they are intended to characterize the incoming sand flux in open field conditions. On the other hand, they allow to properly tune cheaper Wind-Sand Computational Simulations. The wind tunnel setup implements a uniform 5-meter-long sand fetch as sand source. The wind speed boundary layer is characterized through 2D Particle Image Velocimetry (PIV) technique. Wind flow state variables are assessed along the sand fetch by setting the wind speed equal to 1.3, 1.5, 2 times the threshold one. For the complete wind tunnel setup and data analysis please refer to: Raffaele L., Coste, N., and Glabeke G. "Life-Cycle Performance and Cost Analysis of Sand Mitigation Measures: Toward a Hybrid Experimental-Computational Approach." Journal of Structural Engineering 148.7 (2022): 04022082.</p> <p>The study has been developed in the framework of the MSCA-IF-2019 research project Hybrid Performance Assessment of Sand Mitigation Measures (HyPer SMM, https://hypersmm.vki.ac.be/). This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant Agreement No. 885985</p>
HyPer SMM wind tunnel tests: PTV pictures
<p>In this study, windblown sand transport on flat ground is reproduced by means of Wind-Sand Tunnel Tests (WSTT) carried out in the wind tunnel L-1B of von Karman Institute for Fluid Dynamics. The aim of WSTT is twofold. On one hand, they are intended to characterize the incoming sand flux in open field conditions. On the other hand, they allow to properly tune cheaper Wind-Sand Computational Simulations. The wind tunnel setup implements a uniform 5-meter-long sand fetch as sand source. The sand flux saltation layer are characterized through Particle Tracking Velocimetry (PTV) technique. Sand transport is assessed along the sand fetch by setting the wind speed equal to 1.3, 1.5, 2 times the threshold one. For the complete wind tunnel setup and data analysis please refer to: Raffaele L., Coste, N., and Glabeke G. "Life-Cycle Performance and Cost Analysis of Sand Mitigation Measures: Toward a Hybrid Experimental-Computational Approach." Journal of Structural Engineering 148.7 (2022): 04022082.</p> <p>The study has been developed in the framework of the MSCA-IF-2019 research project Hybrid Performance Assessment of Sand Mitigation Measures (HyPer SMM, https://hypersmm.vki.ac.be/). This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant Agreement No. 885985</p>
Additional evidence for a pulsar wind nebula in SN 1987A from multi-epoch X-ray data and MHD modelling
<p>This is a basic reproduction package for the paper "Additional evidence for a pulsar wind nebula in the hearth of sN 1987A from multi-epoch X-ray data and MHD modeling" by Greco et al. 2022. It aims to provide the most important data products to check and reproduce the main results of the paper.</p>
Vertical profiles of urban wind speed, wind direction and turbulence measured by LiDAR on campus of University College Cork, Ireland
<p><strong>Vertical Profiles of Urban wind speed, wind direction and turbulence measured by LiDAR on campus of University College Cork, Ireland</strong></p> <p>=================================</p> <p>README version 1.3, 21/07/2022</p> <p>==================================</p> <p>Contact info:</p> <p>Paul Leahy, University College Cork</p> <p>paul.leahy@ucc.ie | +353 21 4902017</p> <p>================================</p> <p> </p> <p><strong>Contents</strong></p> <p><strong>1. Measurement location and time period</strong></p> <p><strong>2. What is measured (brief description)</strong></p> <p><strong>3. Instrumentation</strong></p> <p><strong>4. CSV file detailed descriptions</strong></p> <p>================================</p> <p> </p> <p><strong>1. Measurement location and time period: </strong></p> <p>North roof of Kane Building, University College Cork (UCC), Ireland.</p> <p>Lat 51 d 53 m 34 s N.</p> <p>Long 8 d 29 m 39 s W.</p> <p>Roof is c. 39 m above sea level, and c. 26 m above ground level (ground level reference point is the car park West of the UCC Kane Building).</p> <p>The measurements were taken over a time period of several months in the years 2013 / 2014.</p> <p>=================================</p> <p><strong>2. What is measured (brief description):</strong></p> <p>* LiDAR Wind speed (horizontal and vertical), wind direction, turbulence intensity at 5 altitudes; reference point (0 m) for these altitudes is the top of the LiDAR instrument c. 1.2 m above roof level.</p> <p>* Air temperature, atmospheric pressure, relative humidity.</p> <p>* Wind speed and direction from an ultrasonic anemometer mounted on top of the instrument (c. 1.2 m above roof level).</p> <p>* 10-minute average values (2 files) and high-resolution (c. 23 sec) data (1 file) are provided.</p> <p>See 'CSV file detailed description' below for detailed information.</p> <p>* Diagnostic information.</p> <p>=================================</p> <p><strong>2.1 Surrounding terrain:</strong></p> <p>Surrounding area is urban/suburban. The aspect is northerly.</p> <p>To the West: 2-5 storey buildings, open spaces, suburban.</p> <p>To the South: 2-3 storey buildings, open spaces, trees, river.</p> <p>To the East: 2-3 storey buildings, open spaces.</p> <p>To the North: A higher section of the Kane Building roof (47 m asl), 1-3 storey buildings, suburban.</p> <p>=================================</p> <p><strong>3. Instrumentation:</strong></p> <p>ZephIR 175 continuous wave wind profiling LiDAR with integrated sonic anemometer, temperature, humidity, air temperature pressure sensors and GPS.</p> <p>=================================</p> <p><strong>4. CSV files detailed description:</strong></p> <p><strong>4.1 Data on 10-minute averages:</strong></p> <p>Filename 05092013-03122013_10min_res.csv contains:</p> <p>10 minute averaged data from 05/09/2013 to 03/12/2013.</p> <p>Measurement altitudes: 148 m, 90 m, 69 m, 44 m, 19m above instrument level.</p> <p> </p> <p>Filename 03122013-07082014_10min_res.csv contains:</p> <p>10 minute averaged data from: 03/12/2013 to 07/08/2014.</p> <p>Measurement altitudes: 148 m, 90 m, 50 m, 35 m, 15 m above instrument level.</p> <p>Note: from 19/06/2014 onwards, LiDAR data missing (MET data continues).</p> <p> </p> <p>The first two rows contain header information.</p> <p>Row 1 contains location information (GPS record)) and the measurement altitudes for wind speeds.</p> <p>Sample GPS record: N51535775W8296590 = 51 d 53.5775 m North; 8 d 29.6590 m West.</p> <p>Row 2 contains the data column headers including units.</p> <p> </p> <p>Wind speeds at each altitude are recorded:</p> <p>No of Packets (= number of scan units averaged over) []</p> <p>Wind direction (mean) [deg]</p> <p>Horizontal wind speed (mean) & standard deviation [m/s]</p> <p>Vertical wind speed (mean) & standard deviation [m/s]</p> <p>Horizontal variance [m^2/s^2] </p> <p>Horizontal min [m/s] </p> <p>Horizontal max [m/s] </p> <p>TI (turbulence intensity) []</p> <p> </p> <p>Other meteorological data:</p> <p>Air temperature [<sup>o</sup>C]</p> <p>Pressure [mbar]</p> <p>Rel. Humidity [%]</p> <p>Rain indicator [unitless] Higher values indicate more rain during the averaging interval.</p> <p>Wind Speed [m/s] (column 'MET Wind Speed' measured at the top of the instrument by the ultrasonic anemometer)</p> <p>Wind direction [deg] (column 'MET Direction' measured at the top of the instrument by the ultrasonic anemometer).</p> <p> </p> <p>Other housekeeping and diagnostic data:</p> <p>Instrument tilt [deg]</p> <p>Instrument bearing [deg]</p> <p>GPS data [degrees N, degrees W]</p> <p>Battery voltage [V] </p> <p>Optics, electronics and battery temperature [<sup>o</sup>C]</p> <p> </p> <p>=====================================================</p> <p> </p> <p><strong>4.2 Data with high time resolution (~23 s):</strong></p> <p> </p> <p>Filename 05092013-11112013_23s_res.csv contains:</p> <p>High resolution data from 05/09/2013 to 11/11/2013</p> <p>Measurement altitudes: 148 m, 90 m, 69 m, 44 m, 19m.</p> <p> </p> <p>Note on time resolution:</p> <p>The time resolution of processed wind measurements is c. 3 seconds per wind level, and around 8 seconds to reset to the first level. A full wind profile measurement at 5 altitudes therefore takes around (5 x 3) + 8 = 23 s to complete.</p> <p>The raw scanning resolution of the instrument is higher than this, as each wind measurement is an average of several values.</p> <p> </p> <p>Row 1 contains location information (lat, long) and the vertical measurement levels for wind speeds.</p> <p>Row 2 contains the data column headers including units.</p> <p> </p> <p>Wind speeds at each altitude are recorded:</p> <p>No of Packets (= scan units averaged over) []</p> <p>Wind direction (mean) [deg]</p> <p>Horizontal wind speed (mean) & standard deviation [m/s]</p> <p>Vertical wind speed (mean) & standard deviation [m/s]</p> <p>Horizontal variance [m^2/s^2] not defined as measurement interval is too short.</p> <p>Horizontal min [m/s] not defined as measurement interval is too short. </p> <p>Horizontal max [m/s] not defined as measurement interval is too short. </p> <p>TI (turbulence intensity) [] not defined as measurement interval is too short.</p> <p> </p> <p>Other meteorological data:</p> <p>Air temperature [<sup>o</sup>C]</p> <p>Pressure [mbar]</p> <p>Rel. Humidity [%]</p> <p>Rain indicator [unitless] Higher values indicate more rain during the scanning interval.</p> <p>Wind Speed [m/s] (column 'MET Wind Speed' measured at the top of the instrument by the ultrasonic anemometer)</p> <p>Wind direction [deg] (column 'MET Direction' measured at the top of the instrument by the ultrasonic anemometer.</p> <p> </p> <p>Other housekeeping and diagnostic data:</p> <p>Instrument tilt [deg]</p> <p>Instrument bearing [deg]</p> <p>GPS data [degrees N, degrees W]</p> <p>Battery voltage [V] </p> <p>Optics, electronics and battery temperature [<sup>o</sup>C]</p> <p> </p> <p>=====================================================</p> <p><strong>4.3 Quality control indicators:</strong></p> <p> </p> <p>9998 atmospheric conditions which adversely affect LiDAR wind speed measurements e.g. fog</p> <p>9999 high quality wind speed measurement not possible e.g. very low wind speed or obscuration of optical path</p> <p>Status Flag 'Green' => good</p> <p>=======================================================</p> <p> </p>
Na Wind-Temperature Lidar Data at Andes Lidar Observatory on 10/30/2016
<p>Measurement made by the Na Wind-Temperature Lidar at the Andes Lidar Observatory in Cerro Pachón, Chile. It includes Na density, temperature, zonal, meridional, and vertical wind, from 80 to 115 km altitude at 0.5-km intervals and from 23.7 UT 10/29/2016 to 8.9 UT 10/30/2016 at 0.1-hour intervals. Errors of these values are also included. -999 represents missing values. </p>
Stiffness of randomly sampled stainless steel frames under gravity and gravity plus wind load scenarios
<p>Data was generated using the general purpose finite element software ABAQUS and performing advanced nonlinear analyses. The database is comprised of vertical and lateral system stiffness values corresponding to different random samples of six different nominal stainless steel frames under gravity and gravity plus wind load combinations. The values of the random variable assignments are given for each case. </p> <p>The full details of the finite element model can be found in: Arrayago, I.; Rasmussen, K.J.R. Reliability of stainless steel frames designed using the Direct Design Method in serviceability limit states. Journal of Constructional Steel Research 196, 107425, 2022. DOI: https://doi.org/10.1016/j.jcsr.2022.107425</p> <p>The data included in the dataset corresponds to the vertical & lateral stiffness of each frame under different load conditions.</p> <p>Although the data has been generated using the finite element software ABAQUS, no special software is required to read or interpret the data.</p>
Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer
<p>Dataset of the paper "Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer" published in Remote Sensing [1].</p> <p>[1] Brugger P, Fuertes FC, Vahidzadeh M, Markfort CD, Porté-Agel F. Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer. <em>Remote Sensing</em>. 2019; 11(19):2247. https://doi.org/10.3390/rs11192247.</p>
Field measurements of wake meandering at a utility-scale wind turbine with nacelle-mounted Doppler lidars
<p>Dataset of the paper " Dataset of the paper "Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer" published in Remote Sensing [1]. " published in Wind Energy Science [1].</p> <p>[1] Brugger, P., Markfort, C., and Porté-Agel, F.: Field measurements of wake meandering at a utility-scale wind turbine with nacelle-mounted Doppler lidars, Wind Energ. Sci., 7, 185–199, https://doi.org/10.5194/wes-7-185-2022, 2022.</p>
Costal operating wind farms: two datasets with concurrent SCADA, LiDAR and turbulent fluxes
<p>This data collection consists of two datasets from a micrometeorological experiment conducted in two distinct operating wind farms in a coastal area of the northeast region of Brazil, called Pedra do Sal Wind Farm (UEPS) and Beberibe Wind Farm (UEBB). These wind farms are located on the northeast coast of Brazil where meteorological conditions are strongly influenced by trade winds and sea breeze. Both datasets represent a full-year of measurements from August/2013 to July/2014.</p> <p>On both operating wind farms it was commissioned a fully instrumented IEC-compliant 100m met mast, with five levels of first-class calibrated cup anemometers and one level (100m) with 3D sonic anemometer. Additionally at UEPS there's an extra 3D sonic at 20m height on the met mast, as well as a VAISALA LEOSPHERE Windcube8 doppler wind lidar with a range up to 500m height and located 2.5D upwind of one of the wind turbines.</p> <p>The Pedra do Sal wind farm (UEPS) has an installed capacity of 18MW, with 20 Enercon E-44 installed at 55m a.g.l. At Beberibe wind farm (UEBB) there are 32 Enercon E-48 wind turbines installed at 75m a.g.l. The dataset includes 10min SCADA data for all wind turbines on both wind farms.</p> <p>This dataset has a high-quality combination of meteorological, SCADA and turbulent flux data of two operating wind farms in Brazil. During a full-year of measurements both datasets had a high data recovery rate (see attached tables). The dataset has already been used to assess the impact of atmospheric stability on the wind farm performance, as well as the effect of mesoscale patterns on the wind profile and wind farm power production. Recirculation of the sea breeze and the development of an internal boundary layer upwind the wind turbines were also characterized.</p> <p>For more details on the experimental layout, wind turbine locations, meso and microcale wind conditions and any other information not stated in the NetCDF4 files, please refer to the reference material or contact one of the authors.</p> <p> </p>
Meteor observations and wind estimates from the northern Germany SIMONe radar network on November 5, 2018
<p>This dataset includes meteor observations and wind estimates taken as part of the SIMONe 2018 campaign in northern Germany on November 5, 2018. The files are in netCDF-4 format and follow CF conventions (https://cfconventions.org/). We recommend loading the data using the xarray Python package.</p> <p>The SIMONe 2018 campaign ran from November 2, 2018 through November 9, 2018 in northern Germany. The radar network consisted of two pulsed transmitters in Juliusruh and Collm and a five-element interferometric MIMO-CW transmitter located in Kühlungsborn. Monostatic receiver stations co-located with the pulsed transmitters and six additional receiver stations located in Mechelsdorf, Breege, Neustrelitz, Guderup, Salzwedel, and Bornim were used to form a total of two monostatic and ten bistatic links. The data from these individual links were then processed to detect specular meteor echoes and estimate their parameters, including Doppler shift. The Doppler shifts, imposed by movement of the meteor trail due to the neutral winds, were then used to estimate the 4-D wind field. More details about the campaign can be found in Vierinen et al. (2019). Details for the wind field estimation can be found in Volz et al. (submitted).</p>
QBO: monthly zonal stratospheric winds from tropical radiosonde data (mainly Singapore)
<p><strong>Monthly Tropical Stratospheric Zonal Winds from Radiosondes</strong></p> <p><strong>Data Source and Processing:</strong></p> <p>Monthly mean zonal wind data for the tropics are provided as a service for the global QBO and trend analysis communities. The original data source and processing chain were established by the Free University of Berlin (FUB). Currently, the data is processed at the Karlsruhe Institute of Technology (KIT, ROR:04t3en479), Institute of Meteorology and Climate Research (IMK), Germany with the tools developed at FUB.</p> <p><strong>Data Description:</strong></p> <p>The dataset includes monthly mean zonal wind values at pressure levels 100, 90, 80, 70, 60, 50, 45, 40, 35, 30, 25, 20, 15, 12, and 10 hPa, derived from radiosonde observations at four equatorial stations:</p> <ul> <li>Kiribati (Canton Island) - data from 1953 to 1967 (closed)</li> <li>Maldives (Gan Island) - data from 1967 to 1975 (closed)</li> <li>Singapore (Payalebar) - data from 1975 to 1989</li> <li>Singapore (Changi) - data from 1989 onwards</li> </ul> <p><strong>Important Notes:</strong></p> <ul> <li>Values for 100 hPa from October 1967 are solely from Singapore (Changi).</li> <li>Values for 100 hPa before October 1967 are from Kiribati (Canton Island) when available.</li> <li>Detailed information about the radiosonde stations and their periods of operation is provided below.</li> </ul> <p><strong>Additional Information:</strong></p> <ul> <li>Access the data in other formats also published here: <a href="https://www.atmohub.kit.edu/english/807.php" target="_blank" rel="noopener noreferrer">https://www.atmohub.kit.edu/english/807.php</a></li> </ul> <p><strong>Detailed List of Radiosonde Stations:</strong></p> <div> <div> <div> <div> <table> <tbody> <tr> <th>Station Name</th> <th>Location (Lat, Lon)</th> <th>Data Period</th> <th>Pressure Levels (hPa)</th> </tr> <tr> <td>Kiribati (Canton Island)</td> <td>-2.7667, -171.7167</td> <td>1953 - 1967 (closed)</td> <td> <p>Above 100 (until August 1967)</p> <p>100 (until September 1967)</p> </td> </tr> <tr> <td>Maldives (Gan Island)</td> <td>-0.6933, 73.1556</td> <td>1967 - 1975 (closed)</td> <td>Above 100 (September 1967 to December 1975)</td> </tr> <tr> <td>Singapore (Payalebar)</td> <td>1.3667, 103.9167</td> <td>1975 - 1989</td> <td>Above 100 (January 1976 to May 1989)</td> </tr> <tr> <td>Singapore Upper Air Observatory</td> <td>1.3404, 103.8879</td> <td>1989 - present</td> <td> <p>100 (October 1967 to May 1989), </p> <p>All levels from June 1989</p> </td> </tr> </tbody> </table> </div> </div> </div> </div> <div> </div>
Wind energy production in forests conflicts with tree - roosting bats
<p>Many countries are investing heavily in wind power generation,<sup>1</sup> triggering a high demand for suitable land. As a result, wind energy facilities are increasingly being installed in forests,<sup>2,3</sup> despite the fact that forests are crucial for the protection of terrestrial biodiversity.<sup>4</sup> This green-green dilemma is particularly evident for bats, as most species at risk of colliding with wind turbines roost in trees.<sup>2</sup> With some of these species reported to be declining,<sup>5-8</sup> we see an urgent need to understand how bats respond to wind turbines in forested areas, especially in Europe where all bat species are legally protected. We used miniaturized global positioning system (GPS) units to study how European common noctule bats (<em>Nyctalus noctula</em>), a species that is highly vulnerable at turbines,<sup>9</sup> respond to wind turbines in forests. Data from 60 tagged common noctules yielded a total of 8129 positions, of which 2.3% were recorded at distances <100 m from the nearest turbine. Bats were particularly active at turbines <500 m near roosts, which may require such turbines to be shut down more frequently at times of high bat activity to reduce collision risk. Beyond roosts, bats avoided turbines over several kilometers, supporting earlier findings on habitat loss for forest-associated bats.<sup>10</sup> This habitat loss should be compensated by developing parts of the forest as refugia for bats. Our study highlights that it can be particularly challenging to generate wind energy in forested areas in an ecologically sustainable manner with minimal impact on forests and the wildlife that inhabit them.</p>
Solar Wind properties measured with instruments on the Advanced Composition Explorer (ACE)
<p>Combined ACE/SWEPAM, ACE/Mag, and ACE/SWICS data set<br> ACE/MAG and ACE/SWEPAM data are taken from the ACE Science center (https://izw1.caltech.edu/ACE/ASC/) and binned to the 12-minute time resolution of SWICS.<br> The SWICS data is based on the PHA data and analyzed as described in Berger (2008).<br> This data set is used in the following two publications:<br> Teichmann, S. Heidrich-Meisner, V, Berger, L, Wimmer-Schweingruber, R.F. (2023, submitted), "Influence of solar wind parameters on unsupervised solar wind classification with k-means" source code available: 10.5281/zenodo.7695074<br> Hecht, M, Heidrich-Meisner, V, Berger, L, Wimmer-Schweingruber, R.F. 2023 (in preparation) "Scope and limitations of ad-hoc neural network reconstructions of solar wind parameters", source code available: 10.5281/zenodo.7681047.</p> <p>Contact: Verena Heidrich-Meisner, CAU Kiel heidrich@physik.uni-kiel.de</p> <p>We thank the science teams of ACE/SWEPAM, ACE/MAG as well as<br> ACE/SWICS for developing, maintaining and calibrating the instruments and for providing the respective level 2 and level 1 data products.<br> This work was supported by the Deutsches Zentrum für Luft- und Raumfahrt (DLR) as SOHO/CELIAS 50 OC 2104.</p> <p>Data products description:<br> year: year of observation (int)<br> time: day of year in current year as float<br> yeartime: time in years as float (UTC)<br> vsw: solar wind proton speed in km/s, measured by ACE/SWEPAM (level 2 from ACE Science Center) and rebinned to 12 minute time resolution<br> dsw: solar wind proton density in cm^{-3}, measured by ACE/SWEPAM (level 2 from ACE Science Center)and rebinned to 12 minute time resolution<br> tsw: solar wind proton temperature in K, measured by ACE/SWEPAM (level 2 from ACE Science Center) and rebinned to 12 minute time resolution<br> B: magnetic feld strength in nT, measured by ACE/MAG (level 2 from ACE Science Center)<br> colage: proton-proton collisional age computed as 6.4* 1e8 * dsw /(vsw* tsw**(3/2)) in K^{3/2} s^2 cm^3 km^{-1}<br> dO7_6: ratio of the O7+ to O6+ charge state densities, measured by ACE/SWICS, derived directly from PHA (pulse height analysis) data<br> eO7_6: estimate of the relative error of dO7_6 based on the counting statistics<br> ldO7_6: decadic logarithm of dO7_6<br> elO7_6: estimate of the relative error of the decadic logarithm dO7_6 based on the counting statistics<br> mcsFe: mean charge state of Fe, based on SWICS PHA of Fe8+, Fe9+, Fe10+, Fe11+, and Fe12+ in units of the elementary charge e. At least 10 counts distributed over Fe8+, Fe9+, Fe10+, Fe11+ and Fe12+ are required<br> emcsFe: estimate of the relative error of dO7_the mean Fe charge state in e (assumes 10% relative error for each Fe charge state)<br> cor_hole: coronal hole wind category in the categorization of Xu&Borovsky (2015) The ejecta category is disregarded, see for example Heidrich-Meisner (2020). Entries are 0 or 1, 1 of the data point is assigned to this type.<br> sec_rev: sector reversal plasma wind category in the categorization of Xu&Borovsky (2015) The ejecta category is disregarded, see for example Heidrich-Meisner (2020). Entries are 0 or 1, 1 of the data point is assigned to this type.<br> stream_belt: streamer belt wind category in the categorization of Xu&Borovsky (2015) The ejecta category is disregarded, see for example Heidrich-Meisner (2020). Entries are 0 or 1, 1 of the data point is assigned to this type.<br> ICME: interplanatery coronal mass ejections time periods (with a six hour safety margin before and after each ICME) from the Jian (2006,2011) and Richardson & Cane (2014, 2018) ICME lists. Entries are 0 or 1, 1 of the data point is assigned to this type.<br> totalCountsFe: number of counts in ACE/SWICS distributed over Fe8+-Fe12+<br> The data set is restricted to data points where valid data points are available for all listed data products. Only for the mean charge state of Fe invalid data points are indicated with nan (not a number)</p> <p>References:<br> Berger, L. 2008, PhD thesis, Kiel, Christian-Albrechts-Universität, Diss., 2008<br> Gloeckler, G., Cain, J., Ipavich, F., et al. 1998, in The Advanced Composition Explorer Mission (Springer), 497–539<br> McComas, D., Bame, S., Barker, P., et al. 1998b, in The Advanced Composition Explorer Mission (Springer), 563–612<br> Smith, C. W., L’Heureux, J., Ness, N. F., et al. 1998, in The Advanced Composition Explorer Mission (Springer), 613–632</p> <p>Xu, F. & Borovsky, J. E. 2015, Journal of Geophysical Research: Space Physics, 120, 70<br> Heidrich-Meisner, V., Berger, L., & Wimmer-Schweingruber, R. F. 2020, Astronomy & Astrophysics, 636, A103<br> Jian, L., Russell, C., & Luhmann, J. 2011, Solar Physics, 274, 321<br> Jian, L., Russell, C., Luhmann, J., & Skoug, R. 2006, Solar Physics, 239, 393<br> Richardson, I. G. 2004, Space Science Reviews, 111, 267<br> Richardson, I. G. 2018, Living reviews in solar physics, 15, 1</p> <p>Teichmann, S. Heidrich-Meisner, V, Berger, L, Wimmer-Schweingruber, R.F. (2023), "Influence of solar wind parameters on unsupervised solar wind classification with k-means" source code available: 10.5281/zenodo.7695074<br> Hecht, M, Heidrich-Meisner, V, Berger, L, Wimmer-Schweingruber, R.F. 2023 (in preparation) "Scope and limitations of ad-hoc neural network reconstructions of solar wind parameters", source code available: 10.5281/zenodo.7681047.</p> <p>year/1 time/day of year yeartime/UTC vsw/km/s dsw/cm^{-3} tsw/K B/nT colage/(K^{3/2} s^2 cm^3 km^{-1}) dO7_6/1 eO7_6/1 ldO7_6/1 elO7_6/1 mcsFe/e emcsFe/e cor_hole/bool sec_rev/bool stream_belt/bool ICME/bool totalCountsFe/1</p>
Fatigue properties of wind turbine rotor blade hybrid epoxy adhesives
<p>This dataset includes the tensile data at two different strain rates and tensile-tensile fatigue data of epoxy adhesives used in wind turbine rotor blades. SPABOND™ 820HTA (non-toughened) and SPABOND™ 840HTA (toughened) epoxy adhesives are combined at different weight proportions to develop the hybrid adhesives. The hybrid and ASTM D638-22 tensile specimen geometry (Type I and Type II) effects on fatigue performance are determined through instrumented experiments. </p>
Radar-derived storm characteristics and convective diagnostics associated with hourly maximum measured wind gusts around Australia
<p>The data in this record describes various characteristics associated with hourly measured surface wind gusts across various locations in Australia, with these characteristics and data sources described below.</p> <p>This record provides all data used in the preparation of Brown et al. (2023a), except for lightning data that can be obtained from the <a href="https://wwlln.net/">World Wide Lightning Location Network archive</a></p> <p><strong>Record contents</strong></p> <ul> <li><em>gust_observations.zip</em><br> Within this zip archive, a <em>.csv</em> file is provided for wind gust observations, along with associated storm statistics from radar, and convective diagnostics from a global reanalysis. These data are provided for each of the 20 radar domains listed in Brown et al. (2023a). The <em>.csv</em> files follow the structure: <em>gust_observations_x.csv, </em>where <em>x </em>is the identification number for each radar from the <a href="https://www.openradar.io/operational-network">Australian Unified Radar Archive</a>.<br> </li> <li><em>station_details.csv</em><br> This file provides details on the automatic weather stations that measure the wind gusts, with station identifiers (column=Station_id) consistent between <em>station_details.csv </em>and<em> gust_observations_x.csv</em>.<br> </li> <li><em>Table1.pdf</em> <br> Descriptions of convective diagnostics from reanalysis, that are provided in <em>gust_observations_x.csv</em>. This table has been extracted from the supplementary information of Brown et al. (2023a), and references in this table can be found therein.<br> </li> <li><em>radar_details.pdf</em> <br> Taken from Table 1 from Brown et al. (2023a), showing the details of radars used here for storm statistics in <em>gust_observations_x.csv</em>.<br> </li> <li><em>Fig1.jpeg</em><br> Taken from from Brown et al. (2023a), showing a map of the radar domains used here for storm statistics in <em>gust_observations_x.csv</em>.</li> </ul> <p><strong>Wind gust data</strong></p> <p>Measured wind gusts here represent a 3-second average wind speed, at a height of 10 m above ground level. We also provide some derived quantities from the gust data (see table below). Gust data is provided by the <a href="http://www.bom.gov.au/climate/data/stations/">Australian Bureau of Meteorology</a>, from 204 automatic weather stations (<em>station_details.csv)</em>, chosen to be within 100 km of a weather radar with sufficient archived data. These data are originally provided by the Bureau of Meteorology at 1-minute frequency, representing a maximum over a 1-minute interval, but are resampled in this record to hourly frequency, for comparisons with other hourly data below (see Brown et al. (2023a) for details of this resampling). Note that any reuse of this data should properly acknowledge the Australian Bureau of Meteorology.</p> <p><strong>Radar data</strong></p> <p>Radar data is obtained by the <a href="https://www.openradar.io/">Australian Unified Radar Archive</a> (AURA), produced from operational weather radar within the Australian Bureau of Meteorology network. The level1b data used here is available from the AURA dataset on the Australian NCI under a CC4-BY-NC licence from <a href="https://dx.doi.org/10.25914/5f4c85732ee80">https://dx.doi.org/10.25914/5f4c85732ee80</a>. Various properties derived from radar reflectivity and Doppler velocity data is reported here in association with the wind gust observations. These properties are only reported if there is a storm object within 10 km and 10 minutes of the gust location (see Brown et al. (2023a) for storm object definition). Radar properties are described in the table below.</p> <p><strong>Environmental data</strong></p> <p>Various convective diagnostics are associated with wind gust observations, representing the convective environment and large-scale wind profile. These diagnostics are derived from a combination of pressure-level and surface-level ERA5 data (Hersbach et al. 2020), which is provided at hourly intervals on a 0.25-degree latitude-longitude grid, hosted on the Australian NCI (<a href="http://dx.doi.org/10.25914/5fb115b82e2ba">http:// dx.doi.org/10.25914/5fb115b82e2ba</a>). Details on these convective diagnostics are provided in Brown et al. (2023a), and<strong> </strong>in <em>Table1.pdf</em> as provided in this record.</p> <p><strong>Column descriptions</strong></p> <p>The following table provides descriptions of columns of <em>gust_observations_x.csv</em></p> <table> <thead> <tr> <th scope="col">Column name</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>dt_utc</td> <td>Time of the measured wind gust, from the automatic weather station data (YYYY-MM-DD HH:MM:SS UTC)</td> </tr> <tr> <td>Station_id</td> <td>Identification number of the weather station that measured the gust. See <em>station_details.csv </em>for details of each station</td> </tr> <tr> <td>Wind_gust_observed</td> <td>The measured wind gust speed (m/s)</td> </tr> <tr> <td>Peak_to_mean_wind_gust_ratio</td> <td>Ratio of the measured wind gust to the 4-hour mean at that station (with the window centred on the gust time)</td> </tr> <tr> <td>SCW</td> <td>Is the measured gust a severe convective wind event?<br> 0: Gust is either less than 25 m/s, does not have a storm object within 10 km, or has a peak-to-mean wind gust ratio less than 2.<br> 1: Gust is greater than 25 m/s, has a storm object within 10 km, and has a peak-to-mean wind gust ratio greater than 2.</td> </tr> <tr> <td>Radar_id</td> <td>Radar identification number (see <em>radar_details.pdf)</em></td> </tr> <tr> <td>Storm_speed</td> <td>Translational speed of the parent storm object (m/s). Only defined if Storm_in10km=1</td> </tr> <tr> <td>Storm_angle</td> <td>Angle of parent storm object movement. In units of degrees from N. Only defined if Storm_in10km=1</td> </tr> <tr> <td>Parent_storm_class</td> <td>The type of parent storm associated with a gust. Only defined if Storm_in10km=1. Possible types are:<br> "Non-linear"<br> "Linear"<br> "Cellular"<br> "Cell cluster"<br> "Supercellular"<br> "Embedded supercell"<br> See Brown et al. (2023a) for classification details</td> </tr> <tr> <td>Storm_in10km</td> <td>Is there a radar-derived storm object within 10 km of the gust, observed no more than 10 minutes prior to the gust?<br> 0: No<br> 1: Yes<br> See Brown et al. (2023a) for a definition of "storm object"</td> </tr> <tr> <td>Major_axis_length</td> <td>The length of the major axis of an ellipse fitted to the parent storm object (km). Only defined if Storm_in10km=1</td> </tr> <tr> <td>Minor_axis_length</td> <td>The length of the minor axis of an ellipse fitted to the parent storm object (km). Only defined if Storm_in10km=1</td> </tr> <tr> <td>Local_reflectivity_maxima</td> <td>Number of local reflectivity maxima within the parent storm object. Only defined if Storm_in10km=1</td> </tr> <tr> <td>Maximum_storm_altitude</td> <td>The maximum height of the parent storm radar reflectivity object (km). Only defined if Storm_in10km=1</td> </tr> <tr> <td>Azimuthal_shear</td> <td>Azimuthal shear of the parent storm object derived from radar data (s<sup>-1 </sup>x 1000). Only defined if Storm_in10km=1. See Brown et al (2023a) for a discussion of azimuthal shear and processing applied to this quantity here.</td> </tr> <tr> <td>ERA5_time</td> <td>Time of the ERA5 environmental data that is associated with the measured gust, corresponding to the closest previous hour (YYYY-MM-DD HH:MM:SS UTC).</td> </tr> <tr> <td>ERA5_latitude</td> <td>Location of closest ERA5 (land) grid point to measured gust, in degrees of latitude</td> </tr> <tr> <td>ERA5_longitude</td> <td>Location of closest ERA5 (land) grid point to measured gust, in degrees of longitude</td> </tr> <tr> <td>Environmental_cluster</td> <td>Event type, based on statistical clustering of environmental data (Brown et al. 2023b)<br> 0: Strong background wind cluster<br> 1: Steep lapse rate cluster<br> 2: High moisture cluster</td> </tr> <tr> <td>Umean06</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>Umean01</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>U10</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>WindGust10</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>S06</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>EBWD</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>Umeanwindinf</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SRHE</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SRH06</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>DMI</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>LR_subcloud</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>LR_freezing</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>LR03</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>LR13</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>WMSI</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>BDSD</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>ConvGust_wet</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>ConvGust_dry</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>GUSTEX</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>DmgWind</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>DmgWind_fixed</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>DCAPE</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>WMPI</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>WINDEX</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>DowndraftTemp</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>ThetaeDiff</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>TEI</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>WNDG</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>DCP</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SCP</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SCP_fixed</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SHERB</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SHERBE</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SWEAT</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>MUCS6</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>MLCS6</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>EffCS6</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>T_Totals</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>K_Index</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>Eff_CAPE</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>Eff_LCL</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>MLCAPE</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>ML_LCL</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>MUCAPE</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>MU_LCL</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>Qmean01</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>Qmean06</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> </tbody> </table> <p><strong>References</strong></p> <p>Brown, A., Dowdy, A., Lane, T. P., & Hitchcock, S. (2023b). Types of Severe Convective Wind Events in Eastern Australia. <em>Monthly Weather Review</em>, <em>151</em>(2), 419–448. https://doi.org/10.1175/MWR-D-22-0096.1</p> <p>Brown, A., A. Dowdy, T. P. Lane, & Hitchcock, S. (2023a). Long-term observational characteristics of different severe convective wind types around Australia. <em>Wea. Forecasting</em>, <a href="https://doi.org/10.1175/WAF-D-23-0069.1">https://doi.org/10.1175/WAF-D-23-0069.1</a>, in press.</p> <p>Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., et al. (2020). The ERA5 Global Reanalysis. <em>Quarterly Journal of the Royal Meteorological Society</em>, qj.3803. https://doi.org/10.1002/qj.3803</p>
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