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510 results for “STORM”
STORMS_Check_list_for_manuscripts
<p>STORMS (Strengthening The Organization and Reporting of Microbiome Studies) checklist for submitted manuscript</p>
Replication files for the publication "The Global Long-Term Effects of Storm Surge Flooding on Human Settlements in Coastal Areas"
<p>This repisority contains code and data to replicate the main results of the publication Kunze & Strobl (forthcoming) "The Global Long-Term Effects of Storm Surge Flooding on Human Settlements in Coastal Areas".</p>
Coupled Thermosphere-Ionosphere Tongue-like Structure During the Recovery Phase of the Geomagnetic Storm on May 12, 2021
<p>The file named 'Indices' includes Kp, F10.7p, By and Bz indices, AE and Dst indices, which are used to plot Figure 1. The file named 'GOLD_131', 'GOLD_132' and 'GOLD_133' include the parameters of O/N2 and temperature from GOLD observations, which are used to plot for Figures 2 and S1. The file named 'GPS_131', 'GPS_132' and 'GPS_133' include the parameters of TEC from GPS observations, which are used to plot for Figures 4 and S2. The file named 'TIEGCM_131' and 'TIEGCM_133' includes O/N2, Temperature, horizontal winds, TEC and diagnostic analysis terms of O+ density from the TIEGCM simulations on DOY 131 and 133 in 2021, which are used to plot for Figures 3 and 4, Movie S1 and S2.</p>
X-SHiELD model verification figures, related to: "The Precipitation Response to Warming and CO$_2$ Increase: A Comparison of a Global Storm Resolving Model and CMIP6 Models"
<p>Figures comparing differrent fields in X-SHiELD with observations. Similar information can be recived at https://extranet.gfdl.noaa.gov/%7EAlex.Kaltenbaugh/verification/</p>
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>
Number of Joint Precipitation and either Wind or Storm Surge Extremes Based on CMIP6 Simulations
<p>This data accompanies the manuscript ‘Projecting Changes in the Drivers of Compound Flooding in Europe Using CMIP6 Models’, Hermans et al. (2024). The dataset consists of the number of joint wind speed & precipitation or storm surge &precipitation extremes derived from CMIP6 models, for different time periods in the 20th and 21st centuries. </p> <p>The code used to produce this dataset can be accessed here: https://github.com/Timh37/CMIP6cex. </p> <p><em>For the production of this dataset, we acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid </em><em>Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF.580. We also acknowledge the computing and storage resources provided by the ‘NSF Science and Technology Center (STC) Learning the Earth with Artificial intelligence and Physics (LEAP)‘ (Award #2019625).</em></p>
Data from: Catastrophic storms, forest disturbance, and the natural history of Swainson's warbler
<p>The core breeding range of Swainson's warbler (<em>Limnothlypis swainsonii</em>) overlaps a zone of exceptionally high tornado frequency in southeastern North America. The importance of tornadoes in creating breeding habitat for this globally rare warbler and other disturbance-dependent species has been largely overlooked. This paper estimates tornado frequency (1950–2021) and forest disturbance in the 240 counties and parishes in which breeding was documented from 1988 to 2014. The frequency of destructive tornadoes (EF1-EF5) varied 6-fold across the breeding range with a peak in the Gulf Coast states. Counties from east Texas to Alabama experienced the lowest median return interval of 5.4 years per 1000 km<sup>2</sup>, resulting in approximately 2477 ha of forest damage per 1000 km<sup>2</sup> per century, based on current forestland cover. Tornadoes were significantly less frequent north and east of the core breeding range, with return intervals increasing to 9.1 years per 1000 km<sup>2</sup> for breeding counties on the Atlantic coastal plain, 10.2 years per 1000 km<sup>2</sup> in the Ozark Mountains, and 32.3 years per 1000 km<sup>2</sup> in the Appalachian Mountains. Breeding counties within 150 km of the coastline from east Texas to North Carolina are also subjected to the highest frequency of hurricanes in the Western Hemisphere. Hurricanes often cause massive forest damage but archived meteorological and forestry data are insufficient to estimate the aggregate extent of forest disturbance in breeding counties. Nevertheless, the combined impact of tornadoes and hurricanes in the pre-Anthropogenic era was likely sufficient to produce a dynamic mosaic of early-successional forest crucial for the breeding ecology of Swainson's warbler. To ensure the long-term survival of this rare warbler, it is advisable to develop habitat management plans that incorporate remote sensing data on early-successional forest generated by catastrophic storms as well as anthropogenic activities.</p> <p>This dataset comprises a catalog of 1717 song recordings of male Swainson's warblers (<em>Limnothlypis swainsonii</em>) on breeding territories in the southeastern United States. Songs were recorded from 1988 to 2014. The spreadsheet includes song recording field number (GRG), state, county or parish, date, latitude, and longitude. Breeding territories were located in 240 counties and parishes, which served as the geographic template for storm data analysis. Geographic coordinates were plotted in Fig 1 of "Catastrophic storms, forest disturbance, and the natural history of Swainson's warbler" (doi.org/10.1002/ece3.11099). Questions or inquiries regarding the dataset can be directed to the author. </p>
Effects of subauroral polarization streams on ionospheric radial currents during the geomagnetic storm on April 23, 2023
<p>The simulation from TIEGCM used in the manuscript entitled"</p> <p><strong><span>Effects of subauroral polarization streams on ionospheric radial currents during the geomagnetic storm on April 23, 2023</span></strong></p> <p>"</p>
Replication data for "Raided by the storm: How three decades of thunderstorms shaped U.S. incomes and wages"
<p>Replication data for "Raided by the storm: How three decades of thunderstorms shaped U.S. incomes and wages" (Coronese et al., JEEM 2024) - <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jeem.2024.103074" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.jeem.2024.103074</span></span></a></p> <p>This repository contains raw data supporting the replication of figures and tables presented in both the main paper and the Supplementary Information. Replication codes are available at: <a href="https://github.com/CoMoS-SA/thunderstorms" target="_new" rel="noopener">https://github.com/CoMoS-SA/thunderstorms</a></p>
In situ characterization of dust storms and their snow darkening effect over Himalayas
<p>The dataset provided here pertains to the Mukteshwar region. Users are requested to acknowledge the data curators before utilizing it.</p>
Data for the paper Robust expansion of extreme extratropical storms under global warming
<p>Data for the paper "Robust expansion of extreme extratropical storms under global warming"</p>
Code for GMD publication - Simulated microphysical properties of winter storms from bulk-type microphysics schemes and their evaluation in WRF (v4.1.3) model during ICE-POP 2018
<p>In this repository, we include the source codes for WRF microphysics parameterization used in the GMD publication "Simulated microphysical properties of winter storms from bulk-type microphysics schemes and their evaluation in WRF (v4.1.3) model during ICE-POP 2018."</p> <p>The four 2-moment bulk microphysical parameterization codes, WDM6, WDM7, Thompson, and Morrison, are divided into 3 WDM, Thompson, and Morrison codes, and each code has been modified so that detailed microphysical processes can be checked in wrfout.</p> <p>The WDM6 and WDM7 schemes include numerical errors for ice microphysical parameterization (Kim and lim, 2021) and for cloud evaporation and melting processes (Lei et al., 2020).</p> <p>Namelist files shows the namelist.input for each case.</p> <p> </p> <p> </p>
Effect of the 2018 Martian global dust storm on the main species in the upper ionosphere: observations and simulations
<p>This dataset contains the MCD outputs and photochemical calculation results for the manuscript "Effect of the 2018 Martian global dust storm on the main species in the upper ionosphere: observations and simulations"</p>
Data for the paper Robust expansion of extreme extratropical storms under global warming (CMIP6)
<p>Data for the paper "Robust expansion of extreme extratropical storms under global warming"</p>
ground-based air quality measurements during the 2021 spring super dust storms
<p>The attachment stores the hourly ground-based PM10 concentration measurements from the China air quality monitoring network during the 2021 spring super dust storms. </p>
2D STORM dataset T4 phage A647-NHS
<p>2D STORM dataset</p> <p>sample: T4 phage labelled with A647-NHS</p> <p>imaging: STORM, COT buffer, pxlsize 106 nm</p>
Data for 'Storm Surge Modeling as an Application of Local Time-stepping in MPAS-Ocean'
<p>Data for 'Storm Surge Modeling as an Application of Local Time-stepping in MPAS-Ocean', submitted to the Journal for Advances in Modeling Earth Systems (JAMES).</p> <p>The repository consists of three main parts:</p> <ol> <li>The `run` directory contains a pre-built run directory as an example.</li> <li>The `scripts` directory contains the scripts that were used to generate important plots.</li> <li>The `data` directory contains model output from performance and accuracy experiments.</li> </ol>
Burst data for "Arecibo observations of a burst storm from FRB 20121102A in 2016" by Hewitt et al. 2021
<p>Numpy arrays of the bursts presented in the MNRAS article "Arecibo observations of a burst storm from FRB 20121102A in 2016" by Hewitt et al. 2021.</p>
Storm God
Storm God. Bible Lands Museum Jerusalem, Bronze. 25 cm height Source: Objaverse 1.0 / Sketchfab
TEC Map during Halloween Storm 2003
<p>The file shows the spatiotemporal change of TEC during the Halloween Storm 2003.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.