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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>
Low Reynolds number response of a symmetric airfoil to viscous vortical gusts
<p>The response of a NACA0012 airfoil impacted by viscous vortical gusts at low Reynolds numbers is investigated performing Direct Numerical Simulations of the two-dimensional incompressible flow. This database contains the time history of the aerodynamic force coefficients of the airfoil during the interaction with the vortical gust. The airfoil, set at a fixed angle of attack alpha, is impacted by Taylor/Lamb-Oseen vortical gust, which are characterized by a diameter <em>D</em>, a intensity <em>v<sub>0m</sub></em>, and a vertical separation <em>h</em>. Direct Numerical Simulations are run for a range of values for the angle of attack, the size and intensity of the vortical gust, and the vertical separations. All simulations are run at a fixed Reynolds number Re=1000, based on the airfoil chord <em>c</em> and the free-stream velocity <em>U<sub>∞</sub></em>.</p> <p>More details on the database and the corresponding simulations can be found in Martínez-Muriel & Flores (2020), Analysis of vortical gust impact on airfoils at low Reynolds number, J. Fluids and Struct, 99. </p> <p><strong>Contents</strong><br> The database consist on a single ASCII file for each case. After a short, self-explanatory header, each file has 7 columns with the following data: </p> <ul> <li>time, <em>t U<sub>∞</sub>/c</em></li> <li>cl: lift coefficient, <em>c<sub>l</sub></em></li> <li>cd: drag coefficient, <em>c<sub>d</sub></em></li> <li>cm: coefficient of moments with respect to c/4, <em>c<sub>m</sub></em></li> <li>dcl: perturbation of <em>c<sub>l</sub></em> with respect to steady state value, ∆<em>c<sub>l</sub></em> </li> <li>dcd: perturbation of <em>c<sub>d</sub></em> with respect to steady state value, ∆<em>c<sub>d</sub></em> </li> <li>dcm: perturbation of <em>c<sub>m</sub></em> with respect to steady state value, ∆<em>c<sub>m</sub></em> </li> </ul> <p>Reference time (t=0) is taken as the time at which the center of the vortical gust reaches the position of the leading edge of the airfoil (if advected at a velocity <em>U<sub>∞</sub></em>). <br> <br> <strong>Nomenclature </strong><br> The names of the files will follow the acronym t_AaYyDdVv.txt, where the lowecase letters are placeholders for: </p> <table> <tbody> <tr> <td> t </td> <td> Type of vortical gust</td> <td> T: Taylor, LO: Lamb-Oseen</td> </tr> <tr> <td> a </td> <td> Angle of attack </td> <td><em> α</em> = [+8,0,-8] deg</td> </tr> <tr> <td> y </td> <td> Initial vertical position of the centre of the vortex</td> <td><em> h/c </em>= [0,0.5,1]</td> </tr> <tr> <td> d </td> <td> Diameter of the core of the vortex </td> <td><em> D/c </em>= [0.5,1,2]</td> </tr> <tr> <td> v </td> <td> Circumferential velocity </td> <td><em> v<sub>0m</sub></em>/<em>U<sub>∞</sub></em> = [0.1,0.3,1]</td> </tr> </tbody> </table>
Martin Gust (g2053)
<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: Martin Gust<br><u>musiXplora-ID</u>: g2053<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/g2053">https://musixplora.de/mxp/g2053</a><br><u>Gender</u>: m<br><u>First Mentioned</u>: 1988<br><u>Sectors</u>: Instrumentenbau<br><u>Professions (Historical)</u>: Cembalobauer, Clavichordbauer<br><u>Professions (Musical)</u>: Tasteninstrumentenhersteller, Zupfinstrumentenbauer<br><u>Other Places of Activity</u>: Warpe<br><br><br><br><u>Changelog</u>:<br> - v0.0.1: Initial Upload.<br>
Experimental gust response and flutter test of a wing with a fixed and a hinged wingtip
<p>Gust responses and flutter test of a wing with a hinged and a fixed wingtip. The experiments were performed at the Swansea University wind tunnel by Davide Balatti as part of his research. Additional information in:</p> <p>[1] D. Balatti, H.H. Khodaparast, M.I. Friswell, & M. Manolesos (2022). Aeroelastic model validation through wind tunnel testing of a wing with hinged wingtip. In International Forum on Aeroelasticity and Structural Dynamics (IFASD), Madrid (https://www.researchgate.net/publication/361418631_AEROELASTIC_MODEL_VALIDATION_THROUGH_WIND_TUNNEL_TESTING_OF_A_WING_WITH_HINGED_WINGTIP)</p> <p>[2] Balatti, D., Khodaparast, H. H., Friswell, M. I., Manolesos, M., & Castrichini, A. Improving gust load alleviation performance of hinge wingtip using validated aeroelastic models. <em>Available at SSRN 4258795</em>.(https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4258795)</p>
Swansea University Wind Tunnel Gust Generator
<p>Initial experimental test of the gust generator at the Swansea University wind tunnel. As a preliminary study smoke test has been used to prove the concept. Experimental data collected from a cross-hot wire sensor indicate that the system is reliably capable of creating single and continuous gusts.</p> <p>More information:</p> <p>[1] D. Balatti, H. Haddad Khodaparast, M. I. Friswell, & M. Manolesos. Improving wind tunnel ‘1-cos’ gust profiles. Journal of Aircraft, https://doi.org/10.2514/1.C036772.</p> <p>[2] D. Balatti, H. Haddad Khodaparast, M. I. Friswell, & M. Manolesos. Improving wind tunnel ‘1-cos’ gust profiles. AIAA 2022-2485. <em>AIAA SCITECH 2022 Forum</em>. January 2022.</p>
Data from Zeppenfeld et al. 2023: "Winter storm risk assessment in forests with high resolution gust speed data", Eur. J. For. Res.
<p>Single-tree damage data from winter storm event "Lothar" 1999 in Baden-Wurttemberg, Germany. The data set includes the response (damage or no damage) and covariates for model parametrisation as described in Zeppenfeld et al. (2023).</p>
Dataset for Article: Potential of dynamic wind farm control by axial induction in the case of wind gusts
<p>This is the dataset for the article "Potential of dynamic wind farm control by axial induction in the case of wind gusts". We provide the data as OUT-files from FAST.Farm simulation. Submission of revised manuscript: November 2023.</p>
Dataset - Experimental analysis of the dynamic inflow effect due to coherent gusts
<p>Here the processed experimental data of the accepted paper given below is documented and made available:</p> <p>Berger, F., Neuhaus, L., Onnen, D., Hölling, M., Schepers, G., and Kühn, M.: Experimental analysis of the dynamic inflow effect due to coherent gusts, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2022-2, accepted, 2022.</p>
Experimental gust response and flutter test of a wing with a fixed and a hinged wingtip
<p>Gust responses and flutter test of a wing with a hinged and a fixed wingtip. The experiments were performed at the Swansea University wind tunnel by Davide Balatti as part of his research. Additional information in:</p> <p>[1] D. Balatti, H.H. Khodaparast, M.I. Friswell, & M. Manolesos (2022). Aeroelastic model validation through wind tunnel testing of a wing with hinged wingtip. In International Forum on Aeroelasticity and Structural Dynamics (IFASD), Madrid (https://www.researchgate.net/publication/361418631_AEROELASTIC_MODEL_VALIDATION_THROUGH_WIND_TUNNEL_TESTING_OF_A_WING_WITH_HINGED_WINGTIP)</p> <p>[2] Balatti, D., Khodaparast, H. H., Friswell, M. I., Manolesos, M., & Castrichini, A. Improving gust load alleviation performance of hinge wingtip using validated aeroelastic models. <em>Available at SSRN 4258795</em>.(https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4258795)</p>
Data from: Influence of topography and the underlying surface of the Bohai Sea on wind and gust forecasts
<p class="MsoNormal"><span>Accurate gust forecasts can reduce potential threats to people's lives and properties, but we need more reliable forecasting methods and models. A recent development is the meteorologically stratified gust factor (MSGF) model, which is more accurate in forecasting gusts than the previous gust factor model. The regional terrain and underlying surface both have crucial effects on the gust factor. We therefore combined observations from the China Meteorological Administration over the ocean surface and along the coast with the MSGF model to explore the influence of topography and the underlying surface on wind and gust forecasts. The regional terrain and underlying surface affected the peak gust climatologies, the mean wind speed, the mean prevailing wind direction and the gust factors. The topography and the underlying surface had different impacts in different ranges of the mean wind speed. The strong turbulence that causes changes in the gust factor under light winds is not initiated over rough underlying surfaces. When the mean wind speed is >2.5 m s<sup>−1</sup>, the underlying surface influences both the wind speed and the gust factor. A rough underlying surface stimulates stronger turbulence and increases the gust speed and gust factor, whereas a smooth underlying surface directly increases the mean wind speed and the gust speed by different magnitudes to reduce the difference between them, thus decreasing the gust factor. We evaluated the ability of the MSGF model to forecast gusts and verified a method combining the products of a numerical model and the MSGF model in gust forecasts.</span></p>
Wind gust during tropical cyclone Ida, with and without deep convection parametrization from 1.4km global simulation with ECMWF IFS
<p>Animations of wind gust during tropical cyclone Ida, from global TCo7999L137 (1.4km horizontal grid-spacing) simulation with hydrostatic IFS from INCITE2022 project. </p> <p>One animation shows simulation with deep convection parametrization on and the other with off.</p>
Data from: Influence of topography and the underlying surface of the Bohai Sea on wind and gust forecasts
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Data from: How lovebirds maneuver through lateral gusts with minimal visual information
Flying birds maneuver effectively through lateral gusts, even when gust speeds are as high as flight speeds. What information birds use to sense gusts and how they compensate is largely unknown. We found that lovebirds can maneuver through 45-degree lateral gusts similarly well in forest, lake, and cave-like visual environments. Despite being diurnal and raised in captivity, the birds fly to their goal perch with only a dim point light source as a beacon, showing that they do not need optic flow or a visual horizon to maneuver. To accomplish this feat, lovebirds primarily yaw their bodies into the gust while fixating their head on the goal using neck angles of up to 30 degrees. Our corroborated model for proportional yaw reorientation and speed control shows how lovebirds can compensate for lateral gusts informed by muscle proprioceptive cues from neck twist. The neck muscles not only stabilize the lovebirds' visual and inertial head orientations by compensating low-frequency body maneuvers, but also attenuate faster 3D wingbeat-induced perturbations. This head stabilization enables the vestibular system to sense the direction of gravity. Apparently, the visual horizon can be replaced by a gravitational horizon to inform the observed horizontal gust compensation maneuvers in the dark. Our scaling analysis shows how this minimal sensorimotor solution scales favorably for bigger birds, offering local wind angle feedback within a wingbeat. The way lovebirds glean wind orientation may thus inform minimal control algorithms that enable aerial robots to maneuver in similar windy and dark environments.
Data from: Avoiding topsy-turvy: how Anna's Hummingbirds (Calypte anna) fly through upward gusts
Flying organisms frequently confront the challenge of maintaining stability when moving within highly dynamic airflows near the Earth's surface. Either aerodynamic or inertial forces generated by appendages and other structures, such as the tail, may be used to offset aerial perturbations, but these responses have not been well characterized. To better understand how hummingbirds modify wing and tail motions in response to individual gusts, we filmed Anna's Hummingbirds as they negotiated an upward jet of fast moving air. Birds exhibited large variation in wing elevation, tail pitch, and tail fan angles among transits as they repeatedly negotiated the same gust, and often exhibited a dramatic decrease in body angle (28 ± 6 degrees) post-transit. After extracting three-dimensional kinematic features, we identified a spectrum of control strategies for gust transit, with one extreme involving continuous flapping, no tail fanning, and little disruption to body posture (23 ± 3 degrees downward pitch), and the other extreme characterized by dorsal wing pausing, tail fanning, and greater downward body pitch (38 ± 4 degrees). The use of a deflectable tail on a glider model transiting the same gust resulted in enhanced stability and can easily be implemented in design of aerial robots.
Gust data for Qingdao City, Shandong Province, China from April 27 to June 8, 2021
<p>This data set shows gust parameters calculated using Doppler Lidar deployed in Qingdao, Shandong Province, China from April 27 to June 8, 2021.</p>
Data from: Avoiding topsy-turvy: how Anna's Hummingbirds (Calypte anna) fly through upward gusts
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Data from: How lovebirds maneuver through lateral gusts with minimal visual information
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Vertical coupling of gusts in the lower boundary layer during super typhoons and squall lines
<p>10 Hz Wind data of four cases.</p>
Hazard Atlas from "Quantifying the Compound Hazard of Freezing Rain and Wind Gusts Across CONUS"
<p>Summary data from the analysis of compound freezing rain-wind gust hazards using Automated Surface Observing Station (ASOS) network data for all hours from January 1, 2005 to December 31, 2022.</p> <p>Note (added 2024-03-06): The SPI index within the Atlas data refers to the SPIA index (https://spia-index.com/), which is copyrighted (https://spia-index.com/copyright.php). Special thanks to Sidney Sperry and Steven Piltz for permission to use the index in this work.</p>
Promoter-enhancer communication occurs primarily within Gene Unit Sub-TADs (GUSTs)
GEO Series GSE115340. Mus musculus. 60 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing; Other.
ScienceDex guides
Understand access before you commit
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.