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Simulated severe convective wind events and environments from the Bureau of Meteorology Atmospheric Regional Projections for Australia (BARPA)
<p>Contacts for further details:</p> <ul> <li>This data record and associated research: Andrew Brown (andrewb1@student.unimelb.edu.au)</li> <li>BARPA data: Chun Hsu Su (chunhsu.su@bom.gov.au), Christian Stassen (Christian.Stassen@bom.gov.au), Harvey Ye (harvey.ye@bom.gov.au)</li> </ul> <h1>Introduction</h1> <p>This record contains data in support of Brown et al. (2024), including post-processed regional climate model data, automatic weather station observations, and post-processed reanalysis data over southeastern Australia for various time periods <strong>over December-Febrary months only</strong> (see descriptions below). This data relates to analysis of severe convective wind gusts in historical and future climate, with analysis scripts in <a href="https://github.com/andrewbrown31/BARPA/tree/main/wind_gust_analysis">this repository</a>. The data are described here according to the directory structure of this record (noting the files and directories have been compressed into <code>barpa_data.tgz</code>), as well as the relevant data sources. </p> <h1>Data sources</h1> <ul> <li><strong>The Bureau of Meteorology Atmospheric Regional Projections for Australia (BARPA)</strong>. A regional climate model containing a regional (BARPA-R) and convection-permitting (BARPAC-M) configuration, with large-scale forcing from ERA-Interim (1990-2015) and ACCESS1-0 using a historical (1985-2005) and RCP8.5 (2039-2059) forcing. See Brown et al. (2024) and Su et al. (2021) for more details. Note that any reuse of this data should properly acknowledge the Australian Bureau of Meteorology. Note also that the BARPA data used here was produced as part of the <a href="https://www.climatechangeinaustralia.gov.au/en/projects/esci/">Electricity Sector Climate Information project</a> (with licence and disclaimers <a href="https://www.climatechangeinaustralia.gov.au/en/projects/esci/risk-assessment/#Disclaimer">here</a>)<em>,</em> with more current BARPA versions (not used here) available at <a href="https://dx.doi.org/10.25914/z1x6-dq28" target="_blank" rel="noopener">https://dx.doi.org/10.25914/z1x6-dq28</a>. </li> <li><strong>Measured wind gusts from automatic weather stations (AWS)</strong>. Gust data is provided by the <a href="http://www.bom.gov.au/climate/data/stations/">Australian Bureau of Meteorology</a>, from 272 AWS locations over 2005-2015. Note that any reuse of this data should properly acknowledge the Australian Bureau of Meteorology.</li> <li><strong>The ERA5 reanalysis </strong>from the European Center for Medium Range Weather Forecasting (Hersbach 2020).</li> <li><strong>The ERA-Interim reanalysis</strong> from the European Center for Medium Range Weather Forecasting (Dee 2011).</li> </ul> <h1>/10min_points</h1> <p>This directory contains .csv files, with wind gust and related environmental data at 10-minute intervals at point locations, corresponding to automatic weather station locations. Data is available over 2005-2015. Files are named in the form <code>barpac_m_aws_<state>.csv</code> and <code>barpac_m_aws_<state>_barpa_r_interp.csv</code>. Here, <state> represents different administrative regions in southeast Australia, including New South Wales (nsw), Victoria (vic), South Australia (sa) and Tasmania (tas). See Figure 1 in Brown et al. (2024) for a map of station locations, that is also included in the /meta directory. The <code>barpa_r_interp</code> suffix indicates that the BARPAC-M wind gusts have been interpolated to the BARPA-R grid for comparison.</p> <p>This data is used in Brown et al. (2024) for evaluation and analysis of BARPA wind gusts in the historical climate (forced by ERA-Interim). The user is directed to that paper for more information on data processing. For the .csv files here, column descriptions are provided in Table 1, below.</p> <h1>/daily_points</h1> <p>This directory contains .csv files, with data associated with daily maximum wind gusts at point locations. This data is derived from the 10min_points data described above, with the same column descriptions in Table 1, below. The different files in this directory are as follows:</p> <ul> <li> <p><code>barpac_m_aws_dmax_obs.csv</code><br>Daily maximum observed wind gust from AWS measurements, with associated wind gust ratio and environmental conditions from ERA5.</p> </li> <li> <p><code>barpac_m_aws_dmax_2p2km.csv</code><br>Daily maximum simulated wind gust from BARPAC-M at closest grid point to AWS location, with associated wind gust ratio, lighting flash count, and environmental conditions from BARPA-R.</p> </li> <li> <p><code>barpac_m_aws_dmax_12km.csv</code><br>Daily maximum simulated wind gust from BARPA-R at closest grid point to AWS location, with associated wind gust ratio, lightning flash count, and environmental conditions.</p> </li> <li> <p><code>barpac_m_aws_dmax_erai.csv</code><br>Daily maximum simulated wind gust from ERA-Interim at closest grid point to AWS location, with associated wind gust ratio and environmental conditions from ERA5.</p> </li> <li> <p><code>barpac_m_aws_dmax_2p2km_barpa_r_interp.csv</code><br>As in <code>barpac_m_aws_dmax_2p2km.csv</code>, but wind gusts are interpolated to the BARPA-R grid prior to calculating the daily maximum.</p> </li> </ul> <h1>/monthly_nc</h1> <p>This directory contains monthly netcdf files at 12 km horizontal grid spacing, with post-processed BARPA data, relating to simulated severe convective wind gusts (from BARPAC-M), and their associated large-scale environments (from BARPA-R). This includes BARPAC-M and BARPA-R data that has been forced by the ACCESS1-0 global climate model, that is intended for analysis of future changes in severe convective wind events and environments. For further information, the user can refer to the internal file metadata, as well as Brown et al. (2024). The files in this directory are as follows (where <experiment> is either <code>hist</code> for historical climate forcing (1985-2005) or <code>rcp</code> for RCP8.5 climate forcing (2039-2059)):</p> <ul> <li> <p><code>barpac_scws_<experiment>_monthly.nc</code><br>Monthly counts of simulated severe convective wind events from BARPAC-M, for each type of environment (see <code>cluster</code> in Table 1).</p> </li> <li> <p><code>barpar_<experiment>_monthly.nc</code><br>Monthly counts of favouable severe convective wind environments from BARPA-R (using <code>bdsd</code>, see Table 1), for each type of environment (see <code>cluster</code> in Table 1).</p> </li> <li> <p><code>barpac_scws_bdsd_<experiment>.nc</code><br>Monthly counts of simulated severe convective wind events from BARPAC-M, that occur under favourable environmental conditions from BARPA-R. For each type of environment (see <code>cluster</code> in Table 1).</p> </li> <li> <p><code>barpac_max_<experiment>_monthly.nc</code><br>Monthly maximum simulated severe convective wind gust, from BARPAC-M, for each type of environment (see <code>cluster</code> in Table 1).</p> </li> </ul> <h1>/daily_nc</h1> <p>This directory contains monthly netcdf files at 12 km horizontal grid spacing, with daily maximum wind gusts from BARPAC-M, as well as the wind gust ratio (see <code>wgr_4</code> in Table 1) and the type of convective environment (from BARPA-R, see <code>cluster</code> in Table 1). This includes BARPA data that has been forced by ERA-Interim and by ACCESS1-0. For further information, the user can refer to the file metadata, as well as Brown et al. (2024). The files in this directory are as follows (where <code><experiment></code> is either <code>historical</code> for historical climate forcing or <code>rcp85</code> for RCP8.5 climate forcing, <code><forcing_model></code> is either <code>erai</code> for ERA-Interim or <code>ACCESS1-0</code>, <<code>date1></code> is the file start date and <code><date2></code> is the file end date):</p> <ul> <li><code>barpa_scw_<forcing_model>_<experiment>_0_<date1>_<date2>.nc</code></li> </ul> <h1>/meta</h1> <p>Lists of AWS stations, for each administrative state, with a file containing column descriptions. Note that not all of the stations listed in these files are used for analysis. Fig1.jpeg is from Brown et al. (2024), showing the BARPAC-M domain (also defines the netcdf file spatial extents), and the location of AWS.</p> <h3>Table 1</h3> <table> <tbody> <tr> <td><strong>Column</strong></td> <td><strong>Name</strong></td> <td><strong>Notes</strong></td> </tr> <tr> <td>stn_id</td> <td>Automatic weather station (AWS) identifier</td> <td> </td> </tr> <tr> <td>time</td> <td>Wind gust time (UTC)</td> <td> </td> </tr> <tr> <td>gust</td> <td>Observed wind gust speed from AWS (m/s)</td> <td>Observed wind gusts are measured at a height of 10 m, and represent a 3-second average. Data is provided as a one-minute maximum, and is resampled to a 10-minute maximum here for comparison with BARPA</td> </tr> <tr> <td>wgr_4</td> <td>Wind gust ratio</td> <td>The observed wind gust ratio, defined as the ratio between <code>gust</code>, and the 4-hour mean from the 10-minute data here.</td> </tr> <tr> <td>time_6hr</td> <td>6-hourly time (UTC)</td> <td>The most recent 6-hourly time step prior to <code>time</code>, associated with environmental diagnostics.</td> </tr> <tr> <td>mu_cape</td> <td>Most unstable convective available potential energy (J/kg)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>s06</td> <td>Bulk vertical wind shear from the surface to 6 km above ground level (m/s)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>dcape</td> <td>Downdraft convective available potential energy (J/kg)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>bdsd</td> <td>Brown and Dowdy (2021) Statistical Diagnostic (BDSD) for identifying favourable severe convective wind environments</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>qmean01</td> <td>Mass-weighted mean mixing ratio from the surface to 1 km (g/kg)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>Umean06</td> <td>Mass-weighted mean wind speed from the surface to 6 km above ground level (m/s)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>lr13</td> <td>Temperature lapse rate from 1 km above ground level to 3 km above ground level (◦C/km)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>cluster</td> <td>Environment type</td> <td> <p>Based on statistical clustering of severe convective wind environments by Brown et al. 2023<br>0: Strong background wind cluster<br>1: Steep lapse rate cluster<br>2: High moisture cluster</p> <p>Derived from BARPA-R</p> </td> </tr> <tr> <td>s06_era5</td> <td>See s06</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>qmean01_era5</td> <td>See qmean01</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>Umean06_era5</td> <td>See Umea06</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>lr13_era5</td> <td>See lr13</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>bdsd_era5</td> <td>See bdsd</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>dcape_era5</td> <td>See dcape</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>mu_cape_era5</td> <td>See mu_cape</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>cluster_era5</td> <td>See cluster</td> <td> <p>Based on statistical clustering of severe convective wind environments by Brown et al. 2023<br>0: Strong background wind cluster<br>1: Steep lapse rate cluster<br>2: High moisture cluster</p> <p>Derived from ERA5</p> </td> </tr> <tr> <td>wg10_12km_point</td> <td>Simulated wind gust from BARPA-R (m/s)</td> <td>Intended to represent a 10 meter wind gust. See Ma et al. (2018) for gust parameterisation details.</td> </tr> <tr> <td>wgr_12km_point</td> <td>Wind gust ratio from BARPA-R </td> <td>See wgr_4</td> </tr> <tr> <td>wg10_2p2km_point</td> <td>Simulated wind gust from BARPC-M (m/s)</td> <td>Intended to represent a 10 meter wind gust. See Ma et al. (2018) for gust parameterisation details.</td> </tr> <tr> <td>wgr_2p2km_point</td> <td>Wind gust ratio from BARPAC-M (see wgr_4)</td> <td>See wgr_4</td> </tr> <tr> <td>n_lightning_fl</td> <td>Number of daily lightning flashes from BARPAC-M</td> <td>See Brown et al. (2024) for more information.</td> </tr> <tr> <td>erai_wg10</td> <td>Simulated wind gust from ERA-Interim (m/s).</td> <td>Intended to represent a 10 meter wind gust. Note that ERA-Interim is provided in 3-hourly intervals, rather than 10-minute intervals for BARPA.</td> </tr> </tbody> </table> <p> </p>
Potential vorticity and wind from ERA5 at several isentropic surfaces
<p>This datasets collects winds (u and v components) and potential vorticity from ERA5 at four isentropic surfaces: 475, 600, 700 and 800 K. Data are available daily and monthly. Potential vorticity and modified potential vorticity are stored. </p>
Low-severity winds reduce tropical forest structural complexity regardless of climate, topography or forest age
<p>Forests are often exposed to regular, non-severe winds (chronic wind exposure), yet the effect of such winds on canopy structure in tropical forests remains understudied. The height and structural complexity of a forest canopy are strongly and positively correlated with biodiversity and carbon accumulation. Understanding the drivers of canopy structural complexity across broad environmental gradients can therefore improve the mapping and modeling of diversity and carbon dynamics. Here we predict the height and structural complexity of forests in the heterogeneous island of Puerto Rico, with a particular focus on the impacts of chronic wind exposure. To do so, we used remote sensing to randomly sample ~20,000, 0.28 ha forested sites stratified by forest age, and used airborne LiDAR data from 2016 to quantify canopy height and a key metric of structural complexity, rugosity – the standard deviation in canopy height. We then ran random forest models to predict canopy height and rugosity based on chronic wind exposure, forest age, mean annual precipitation, elevation, slope, soil type, soil available water storage, and exposure to two previous hurricanes (in 1989 and 1998). Canopy height was 4 m taller on average (41%) between forests aged 17-25 years and old-growth forests and by 4 m on average (41%) between 1,000 and 2,000 mm<sup>-yr</sup> precipitation, leveling off at 2,000 mm<sup>-yr</sup>. Height was 2.12 m (16%) shorter on average between sites exposed to chronic winds and protected sites after accounting for all other factors. Rugosity was 1 m (32%) greater between the tallest and shortest forests, by 0.5 m (15%) between 1,000 and 2,000 mm<sup>-yr</sup> precipitation, and smaller by 0.5 m (15%) between forests above and below 1,000 m elevation. Rugosity was highest in forests of intermediate age (25-40 years), and lowest in old-growth forests, possibly because of higher elevation and chronic wind exposure in old-growth forests. We found no effect of slope, soil characteristics or previous hurricane exposure on either height or rugosity. Our results suggest that alongside forest age and climate context, chronic wind exposure plays an integral role in shaping the structure and carbon cycle of tropical forests.</p>
Low-severity winds reduce tropical forest structural complexity regardless of climate, topography or forest age
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Wind River Experimental Forest site, station Washington Region 4, East Olympic Cascades Foothills, study of Palmer Drought Severity Index in units of dimensionless on a monthly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Wind River Experimental Forest (WIN) contains Palmer Drought Severity Index measurements in dimensionless units and were aggregated to a monthly timescale.
Wind River Experimental Forest site, station Washington Region 4, East Olympic Cascades Foothills, study of Palmer Drought Severity Index in units of dimensionless on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Wind River Experimental Forest (WIN) contains Palmer Drought Severity Index measurements in dimensionless units and were aggregated to a yearly timescale.
Solar wind parameters and geomagnetic indices for severe (SYM-H < -100nT) geomagnetic storms over two solar cycles
<p><em><strong>Severe storms data set (1996-2022)</strong></em></p> <p><em>Lotz, Grant, Davel (2024).</em></p> <p><strong>Changes from previous version:</strong></p> <ul> <li>Previous version had errors in calculation of `HER_eh` and `ABK_eh`. The mistake is corrected in this version.</li> <li>This version includes storms from 1996-2022. Previous version had 1996-2019.</li> </ul> <p>This data set is curated from 1-minute time resolution OMNI [4] and INTERMAGNET [7] data, spanning the period 1996 - 2022.</p> <p>It consists of all geomagnetic storms that reached minimum SYM-H (Symmetric-H index) of < -100 nT. We classified these as "severe" storms. There are 115 such events in the period 1996 - 2018. No severe storms detected between 2007-2010 or in 2019. Each file in the set contains the events in that year (YYYY) in the format "severe_YYYY.pickle". The geomagnetic storms are identified by the procedure described in [1], with thresholds -100 nT and -20 nT (see [1] for further detail).</p> <p>The data sets are Pandas "dataframes" [2], saved in the Python "pickle" format [3]. Each dataframe contains 23 variables, listed in the table below, with their description and unit.</p> <p>Fourteen of the parameters are solar wind plasma and magnetic field parameters from the High Resolution OMNI data set [4].</p> <p>There are 7 geomagnetic variables:</p> <ul> <li>one is the global Sym-H index,</li> <li>four are H-component geomagnetic observations from Hermanus, South Africa (HER: 34.4 S, 19.22 E) and Abisko, Sweden (ABK: 68.35 N, 18.82 E) and their time derivatives</li> <li>two are the e_h index [5] calculated at HER and ABK</li> </ul> <p>Finally, we list the time shift applied to each data point to shift to the bow shock nose (described for OMNI at [6]) and a string identifier for each geomagnetic storm.</p> <p> </p> <table> <tbody> <tr> <td><strong>#</strong></td> <td><strong>Name</strong></td> <td><strong>Description</strong></td> <td><strong>Unit</strong></td> </tr> <tr> <td>1</td> <td>time_shift</td> <td> <p>Time shift applied to shift solar wind parameters to bow shock</p> </td> <td>s</td> </tr> <tr> <td>2</td> <td>Bt</td> <td>Magnitude of interplanetary magnetic field (IMF)</td> <td>nT</td> </tr> <tr> <td>3</td> <td>Bx</td> <td>X-component of IMF</td> <td>nT</td> </tr> <tr> <td>4</td> <td>By_GSE</td> <td>Y-component of IMF (GSE coordinates)</td> <td>nT</td> </tr> <tr> <td>5</td> <td>Bz_GSE</td> <td>Z-component of IMF (GSE coordinates)</td> <td>nT</td> </tr> <tr> <td>6</td> <td>By_GSM</td> <td>Y-component of IMF (GSM coordinates)</td> <td>nT</td> </tr> <tr> <td>7</td> <td>Bz_GSM</td> <td>Z-component of IMF (GSM coordinates)</td> <td>nT</td> </tr> <tr> <td>8</td> <td>Vsw</td> <td>Bulk solar wind speed</td> <td>km/s</td> </tr> <tr> <td>9</td> <td>Vx</td> <td>X-component of solar wind</td> <td>km/s</td> </tr> <tr> <td>10</td> <td>Vy</td> <td>Y-component of solar wind</td> <td>km/s</td> </tr> <tr> <td>11</td> <td>Vz</td> <td>Z-component of solar wind</td> <td>km/s</td> </tr> <tr> <td>12</td> <td>Np</td> <td>Proton number density in solar wind plasma</td> <td>#/cc</td> </tr> <tr> <td>13</td> <td>Temp</td> <td>Temperature of solar wind plasma</td> <td>K</td> </tr> <tr> <td>14</td> <td>Pd</td> <td>Dynamic or flow pressure of solar wind plasma</td> <td>nPa</td> </tr> <tr> <td>15</td> <td>E_field</td> <td>Solar wind electric field</td> <td>mV/km</td> </tr> <tr> <td>16</td> <td>SymH</td> <td>Symmetric-H index of the geomagnetic field</td> <td>nT</td> </tr> <tr> <td>17</td> <td>HER_H</td> <td>H-component of the geomagnetic field at Hermanus</td> <td>nT</td> </tr> <tr> <td>18</td> <td>HER_dHdt</td> <td>Time derivative of HER_H</td> <td>nT/min</td> </tr> <tr> <td>19</td> <td>HER_eh</td> <td>e_h index at HER</td> <td>nT/min</td> </tr> <tr> <td>20</td> <td>ABK_H</td> <td>H-component of the geomagnetic field at Abisko</td> <td>nT</td> </tr> <tr> <td>21</td> <td>ABK_dHdt</td> <td>Time derivative of ABK_H</td> <td>nT/min</td> </tr> <tr> <td>22</td> <td>ABK_eh</td> <td>e_h index at ABK</td> <td>nT/min</td> </tr> <tr> <td>23</td> <td>Storm_ID</td> <td>String identifier of the geomagnetic storm</td> <td>-</td> </tr> </tbody> </table> <p> </p> <p><em><strong>References</strong></em></p> <ol> <li>Lotz, S. I., & Danskin, D. W. (2017). <em>Space Weather</em>, 15, 1347– 1356. <a href="https://doi.org/10.1002/2017SW001662">https://doi.org/10.1002/2017SW001662</a></li> <li><a href="https://omniweb.gsfc.nasa.gov/ow_min.html">https://omniweb.gsfc.nasa.gov/ow_min.html</a></li> <li><a href="https://pandas.pydata.org/">https://pandas.pydata.org/</a></li> <li><a href="https://docs.python.org/3/library/pickle.html">https://docs.python.org/3/library/pickle.html</a></li> <li>Wintoft, P., Wik, M., & Viljanen, A. J. Space Weather Space Clim., 5, A7 (2015). <a href="http://dx.doi.org/10.1051/swsc/2015008">http://dx.doi.org/10.1051/swsc/2015008</a></li> <li><a href="https://omniweb.gsfc.nasa.gov/html/ow_data.html#time_shift">https://omniweb.gsfc.nasa.gov/html/ow_data.html#time_shift</a></li> <li><a href="https://www.intermagnet.org/index-eng.php">http://www.intermagnet.org</a></li> </ol>
Data from: Wind pollination over 70 years reduces the negative genetic effects of severe forest fragmentation in the tropical oak Quercus bambusifolia
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HURRICANE AND SEVERE STORM SENTINEL (HS3) HIGH-ALTITUDE IMAGING WIND AND RAIN AIRBORNE PROFILER (HIWRAP) V1
The Hurricane and Severe Storm Sentinel (HS3) High-Altitude Imaging Wind and Rain dataset was collected from the High-altitude Imaging Wind and Rain Airborne Profiler (HIWRAP), which is a dual-frequency (Ku- and Ka-band, or approximately 14 and 35 GHz), dual-beam (30 degree and 40 degree incidence angle), conically scanning radar that has been designed for the Global Hawk aircraft during the HS3 campaign. Goals for HS3 included: assessing the relative roles of large-scale environment and storm-scale internal processes; and addressing the controversial role of the Saharan Air Layer (SAL) in tropical storm formation and intensification as well as the role of deep convection in the inner-core region of storms. HIWRAP uses solid state transmitters along with a novel pulse compression scheme that results in a system that is considerably more compact and requires less power than typical radars used for precipitation and wind measurements. By conically scanning at 10-20 rpm, its beams sweeped below the Global Hawk collecting Doppler velocity/reflectivity profiles. The unique HIWRAP sampling and phase correction strategy implemented (frequency diversity Doppler processing technique). HIWRAP's dual-wavelength operation enables it to map full tropospheric winds from cloud and precipitation volume backscatter measurements, derive information about precipitation drop-size distributions, and estimate the ocean surface winds using scatterometry techniques. Winds will be retrieved using a gridding approach similar to well-established ground-based multi-Doppler radar wind analyses. More information can be found at http://har.gsfc.nasa.gov/index.php?section
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Allen Brain Atlas
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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.