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510 results for “storms”
Geomagnetic field variations recorded at EMMA during the November 2021 geomagnetic storm
<p>This data set contains daily files of 1-sec geomagnetic field variations from 22 stations of the European quasi-Meridional Magnetometer Array (EMMA) collected in the interval 29 October - 8 November 2021. The stations available are AQU, BEL, BRZ, HAN, HLP, IVA, KEV, KIL, LOP, MAS, MUO, NUR, OUJ, PEL, PPN, RAN, RNC, SOD, SUW, TAR, THY, ZAG.</p> <p>The header lines in each file indicate the station name, geodetic coordinates, and the reference frame (HDZ or XYZ). Data are expressed in nanoTesla. Missing data are represented by 99999.99.</p>
Pelagic seabirds reduce risk by flying into the eye of the storm
<p><span>Cyclones can cause mass mortality of seabirds, sometimes wrecking thousands of individuals. The few studies to track pelagic seabirds during cyclones show they tend to circumnavigate the strongest winds. We tracked adult shearwaters in the Sea of Japan over 11 years and find that the response to cyclones varied according to the wind speed and direction. In strong winds, birds that were sandwiched between the storm and mainland Japan flew away from land and towards the eye of the storm, flying within ≤ 30 km of the eye and tracking it for up to 8 hours. This exposed shearwaters to some of the highest wind speeds near the eye wall (≤ 21 m s<sup>-1</sup>), but enabled them to avoid strong onshore winds in the storm's wake. Extreme winds may therefore become a threat when an inability to compensate for drift could lead to forced landings and collisions. Birds may need to know where land is in order to avoid it</span><span>. This provides additional selective pressure for a map sense and could explain why juvenile shearwaters, which lack a map sense, instead navigating using a compass heading, are susceptible to being wrecked. We suggest that the ability to respond to storms is influenced by both flight and navigational capacities. This may become increasingly pertinent due to changes in extreme weather patterns. </span></p>
Preprocessed Data for "Comparing Storm Resolving Models and Climates via Unsupervised Machine Learning"
<p>Preprocessed Data (training and test) for 3 SRMs used in "Comparing Storm Resolving Models and Climates via Unsupervised Machine Learning". Here we included ICON, SPCAM, and SPCAM with sea surface tem[eratures warmed by +4K. Additionally we include lat/lon information for the test data.</p>
Supporting dataset: "Analysis of tide and offshore storm-induced water table fluctuations for structural characterization of a coastal island aquifer"
<p>Included in this repository are supporting field data and final model input files used to produce the results of the manuscript:</p> <p>Trglavcnik, V., Morrow, D., Weber, K. P., Li, L., & Robinson, C. E. (2017), "Analysis of tide and offshore storm-induced water table fluctuations for structural characterization of a coastal island aquifer."</p> <p>This dataset contains:</p> <ul> <li>SableIsland_data.xlsx <ul> <li>Data used to produce figures in the above manuscript. </li> </ul> </li> <li>Final_SS.zip <ul> <li>Input files for the final model (see Figure 3 in manuscript), steady-state SEAWAT simulation. </li> </ul> </li> <li>Final_PBC.zip <ul> <li>Input files for the final model, transient SEAWAT simulation with a sinusoidal tidal boundary implemented by the Periodic Boundary Condition package (developed for MODFLOW by Post, 2011).</li> </ul> </li> </ul> <p>All data and files are licensed under Creative Commons Attribution Share Alike 4.0 International.</p> <p> </p> <p> </p>
Wave climate simulations for Denmark - for paper 'Coinciding storm surge and wave setup: A regional assessment of sea level rise impact'
<p>This wave climate dataset are the results for the paper titled 'Coinciding storm surge and wave setup: A regional assessment of sea level rise impact'.</p> <p>The operational wave forecasting service provided by DMI-WAM uses the WAM Cycle version 4.5.4, a third-generation spectral wave model. DMI-WAM is used for the wave climate simulations. The meteorological forcing used in this study was obtained from the regional climate model DMI-HIRHAM, developed by the Danish Meteorological Institute (DMI). It is a component of the CORDEX (Coordinated Regional Climate Downscaling Experiment) ensemble in Europe. Regarding the selection of the time frame and IPCC scenarios in our study, we adhered to the recommendations provided by municipalities. Municipalities are keenly interested in obtaining near-future wind wave data for the specific purpose of using them for risk management. Therefore, the examination of forthcoming weather extremes in the near future within the context of the high greenhouse gas emission scenario (RCP8.5 scenario) is of significance within this investigation. We conduct simulations that encompass two distinct time periods: the historical period spanning from 1976 to 2005, and the near-future period from 2041 to 2070. We analyse the WAM model results for wave climate under both present climate conditions (1976-2005) and future climate scenarios (2041-2070) under the RCP8.5 scenario. Furthermore, note that while our wave climate simulations provide valuable insights into the dynamics of wind-induced waves, the mean SLR is not explicitly taken into account. The mean SLR component is considered in the storm surge simulations.</p> <p>Description of files:</p> <p><a href="../api/records/11052226/draft/files/sla.swh.slope.hist.final.max.nc/content" target="_blank" rel="noopener noreferrer">sla.swh.slope.hist.final.max.nc</a> - Maximum sea level, significant wave height, wave length and slope for the historical period.</p> <p><a href="../api/records/11052226/draft/files/sla.swh.slope.rcp85.final.max.MSLR35.nc/content" target="_blank" rel="noopener noreferrer">sla.swh.slope.rcp85.final.max.MSLR35.nc</a> - Maximum sea level, significant wave height, wave length and slope for the RCP8.5 period.</p> <p><a href="../api/records/11052226/draft/files/wavesetup.hist.final.max.nc/content" target="_blank" rel="noopener noreferrer">wavesetup.hist.final.max.nc</a> - Maximum wave setup for the historical period.</p> <p><a href="../api/records/11052226/draft/files/wavesetup.rcp85.final.max.MSLR35.nc/content" target="_blank" rel="noopener noreferrer">wavesetup.rcp85.final.max.MSLR35.nc</a> - Maximum wave setup for the RCP8.5 period.</p> <p><a href="../api/records/11052226/draft/files/wam.grib.his.swh.98p.nc/content" target="_blank" rel="noopener noreferrer">wam.grib.his.swh.98p.nc</a> - 2% exceedence of significant wave height for the historical period.</p> <p><a href="../api/records/11052226/draft/files/wam.grib.rcp8.swh.98p.nc/content" target="_blank" rel="noopener noreferrer">wam.grib.rcp8.swh.98p.nc</a> - 2% exceedence of significant wave height for the RCP8.5 period.</p>
Joule Heating calculation during St Patrick's day storm of March 2015, using GCMs and Emprirical formulation
<p>This dataset contains globally integrated, and height integrated Joule heating on St Patrick's day storm of March 2015.</p> <p>Date calculated based on Empirical formulations, GITM and TIEGCM driven by two different specifications of high-latitude electric fields, namely the Weimer 2005 and the Assimilative Mapping of Ionospheric Electrodynamics (AMIE) models.</p> <p> </p>
Data for "Hunting for gravity waves in non-orographic winter storms using 3+ years of regional surface air pressure networks and radar observations"
<p>These data are shown in the figures included with the article "Hunting for gravity waves in non-orographic winter storms using 3+ years of regional surface air pressure networks and radar observations," submitted to Atmospheric Chemistry and Physics.</p>
Fig. 4 in Very severe cyclonic storm "Gaja" and its impact off the Tamil Nadu coastline
Fig. 4 — Extent of damages documented off the Nagapattinam coast, Tamil Nadu
Fig. 3 in Very severe cyclonic storm "Gaja" and its impact off the Tamil Nadu coastline
Fig. 3 — Cyclone shelters in the villages showing distance to access
Fig. 6 in Very severe cyclonic storm "Gaja" and its impact off the Tamil Nadu coastline
Fig. 6 — Periodicity and usage frequency of the weather forecast
Fig. 5 in Very severe cyclonic storm "Gaja" and its impact off the Tamil Nadu coastline
Fig. 5 — Percentage variations in respondents using different source of forecast
Fig. 1 in Very severe cyclonic storm "Gaja" and its impact off the Tamil Nadu coastline
Fig. 1 — Study area – Nagapattinam coast. Source: Central ground water board, Tamil Nadu
Fig. 2 — a in Very severe cyclonic storm "Gaja" and its impact off the Tamil Nadu coastline
Fig. 2 — a) Percentage variability of total fishing population; and b) affected individuals
STORMS_checklist_Bencic_2024
<p>Completed STORMS checklist for the publication of manuscript <em>Metrological evaluation of DNA extraction method effects on the bacterial microbiome and resistome in sputum</em> in scientific journal mSystems.</p>
Data for paper "Investigating the sign of stratocumulus adjustments to aerosols in the global storm-resolving model ICON"
<p>Data for paper "Investigating the sign of stratocumulus adjustments to aerosols in the global storm-resolving model ICON". The code used to generate, analyze and plot these data is provided separately on Zenodo. The zip files named 2.zip_file_name are used in the 2.make_comparison_plots notebooks in the companion Zenodo software repository. The zip files named 3.zip_file_name are generated using the code contained in 1.process_data in the software repository and used for the analyses in 3.calculate_causal_effects in the software repository. </p> <p><strong>References - Code ______________________________________________</strong></p> <p>J. Runge et al. (2015): Identifying causal gateways and mediators in complex spatio-temporal systems. Nature Communications, 6, 8502. <a href="http://doi.org/10.1038/ncomms9502">http://doi.org/10.1038/ncomms9502</a></p> <p>J. Runge, Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables, Advances in Neural Information Processing Systems, 2021, 34. <a href="https://proceedings.neurips.cc/paper/2021/hash/8485ae387a981d783f8764e508151cd9-Abstract.html">https://proceedings.neurips.cc/paper/2021/hash/8485ae387a981d783f8764e508151cd9-Abstract.html</a></p> <p><strong>References - Data ______________________________________________</strong></p> <p><em>SEVIRI</em> <br>Benas, N., Solodovnik, I., Stengel, M., Hüser, I., Karlsson, K.-G., Håkansson, N., Johansson, E., Eliasson, S., Schröder, M., Hollmann, R., and Meirink, J. F.: CLAAS-3: The Third Edition of the CM SAF Cloud Data Record Based on SEVIRI Observations, Earth System , Science Data Discussions, pp. 1-38, <a href="https://doi.org/10.5194/essd-2023-79">https://doi.org/10.5194/essd-2023-79</a>, 2023.</p> <p><em>GOES</em><br>Walther, A. and Straka, W.: Algorithm Theoretical Basis Document For Daytime Cloud Optical and Microphysical Properties (DCOMP), 2020</p> <p><em>ERA5</em><br>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thébaut, J.-N.: ERA5 Hourly Data on Single Levels from 1959 to Present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), <a href="https://doi.org/10.24381/cds.adbb2d47">https://doi.org/10.24381/cds.adbb2d47</a>, 2018a.<br>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thébaut, J.-N.: ERA5 Hourly Data on Pressure Levels from 1959 to Present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), <a href="https://doi.org/10.24381/cds.bd0915c6">https://doi.org/10.24381/cds.bd0915c6</a>, 2018b.</p> <p><em>GPM</em><br>Huffman, G., Stocker, E., Bolvin, D., Nelkin, E., and Tan, J.: GPM IMERG Final Precipitation L3 Half Hourly 0.1 Degree x 0.1 Degree V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), <a href="https://doi.org/10.5067/GPM/IMERG/3B-HH/07">https://doi.org/10.5067/GPM/IMERG/3B-</a><a href="https://doi.org/10.5067/GPM/IMERG/3B-HH/07">HH/07</a>, 2023</p> <p><em>MODIS</em><br>Platnick, S. et al. MODIS atmosphere L3 daily product. NASA<a href="https://doi.org/10.5067/MODIS/MOD08_D3.006"> https://doi.org/10.5067/MODIS/MOD08_D3.006 </a>, 2015.</p> <p><em>MIDAS</em><br>Eastman, R., McCoy, I. L., Schulz, H., and Wood, R.: A Survey of Radiative and Physical Properties of North Atlantic Mesoscale Cloud Morphologies from Multiple Identification Methodologies, EGUsphere, pp. 1–33, <a href="https://doi.org/10.5194/egusphere-2023-2118">https://doi.org/10.5194/egusphere-2023-2118</a>, 2023. <br>McCoy, I. L., McCoy, D. T., Wood, R., Zuidema, P., and Bender, F. A.-M.: The Role of Mesoscale Cloud Morphology in the Shortwave Cloud Feedback, Geophysical Research Letters, 50, e2022GL101 042, <a href="https://doi.org/10.1029/2022GL101042">https://doi.org/10.1029/2022GL101042</a>, 2023.</p> <p><em>ICON</em><br>Zängl, G., Reinert, D., Rípodas, P., and Baldauf, M.: The ICON (ICOsahedral Non-hydrostatic) Modelling Framework of DWD and MPI-M: Description of the Non-Hydrostatic Dynamical Core, Quarterly Journal of the Royal Meteorological Society, 141, 563–579, <a href="https://doi.org/10.1002/qj.2378">https://doi.org/10.1002/qj.2378</a>, 2015<br><a href="https://code.mpimet.mpg.de/projects/iconpublic/wiki/Instructions_to_obtain_the_ICON_model_code_with_a_personal_non-commercial_research_license">https://code.mpimet.mpg.de/projects/iconpublic/wiki/Instructions_to_obtain_the_ICON_model_code_with_a_personal_non-commercial_research_license </a></p>
Supporting materials for 'Building quantitative skills with a simplified physical model of coastal storm deposition'
<p>Supporting Materials for Lazarus (2024): "Building quantitative skills with a simplified physical model of coastal storm deposition" (preprint <a href="https://doi.org/10.31223/X56H5B">here</a>).</p> <p>Files include:</p> <ul> <li><strong>DEM_washover_final_demo.tif </strong>– "final" DEM for a bare back-barrier floodplain in a physical laboratory experiment of coastal barrier overwash</li> <li><strong>Lazarus_2024_experimental_washover_exercise_instructions_Zenodo_release.pdf</strong> – step-by-step instructions for a classroom exercise that guides students through using the 'DEM_washover_final_demo' file to digitise, measure, and plot washover deposits with QGIS and Python</li> <li><strong>experiments_plotting_simple.ipynb</strong> – Python notebook for plotting results from classroom exercise</li> <li><strong>Lazarus_2024_washover_exercise_figs.ipynb</strong> – Python notebook for plotting Figs. 3 & 4 in the accompanying manuscript (Lazarus, 2024)</li> <li><strong>GGES2021_S24_data_all_release.csv</strong> – dataset of morphometric measurements presented and discussed in the accompanying manuscript (<a href="https://doi.org/10.31223/X56H5B">Lazarus, 2024</a>)</li> </ul>
Molecular Dynamics Simulation and Docking Studies Reveals Inhibition of NF-kB signaling as a Promising Therapeutic Drug Target for reduction in Cytokines Storms
<p><span>The complexes of the top identified molecules with NF-kB-kB site, as well as all the designed molecules used in the screening process. </span></p>
Citizen Science Reports on Aurora Sighting and Technological Disruptions during the 10 May 2024 Geomagnetic Storm – ARCTICS Survey
<div> <div> <div> <div> <p>The geomagnetic storm that began on 10 May 2024 provided stunning auroral displays observed worldwide. This dataset contains data collected via an online survey designed and distributed by the "Auroral Research Coordination – Towards Internationalised Citizen Science" (ARCTICS) collaboration sponsored by the International Space Science Institute (ISSI) in Bern, Switzerland (see https://collab.issibern.ch/arctics/). A total of 696 observers from over 30 countries filled in the survey and reported on aurora sightings and experienced disruptions in technological systems during the superstorm.</p> <p>The dataset consists of two data files in the CSV format and a text file providing a detailed description of the data. The collected data have been anonymised and pre-processed to obtain a homogeneous data set.</p> <p>Dataset associated with the <span>egusphere-2024-2174 preprint by Grandin et al. ("<span>The geomagnetic superstorm of 10 May 2024: Citizen science observations</span>"), submitted to Geoscience Communication.</span></p> </div> </div> </div> </div>
Formal Verification of Storm Topologies through D-VerT
<p>This archive includes the research data associated to the paper:<br> Formal verification of storm topologies through D-VerT. In <em>Proceedings of the Symposium on Applied Computing</em> (SAC '17). Francesco Marconi, Marcello M. Bersani, and Matteo Rossi. 2017. ACM, New York, NY, USA, 1168-1174. DOI: https://doi.org/10.1145/3019612.3019769</p> <p>Specifically it includes the UML models shown in the paper (Figures 7 and 8), the corresponding instances of the Temporal logic models automatically generated by means of the D-VerT and the output files of the experiments.</p>
Modeling the Depletion and Recovery of the Outer Radiation Belt During a Geomagnetic Storm: Combined MHD and Test Particle Simulations
<p>Data associated with JGR: Space Physics paper, "Modeling the Depletion and Recovery of the Outer Radiation Belt During a Geomagnetic Storm: Combined MHD and Test Particle Simulations".</p>
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.