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256 results for “Cyclone”
Material for manuscript submitted to Earth and Space Science "Evaluation of a mesoscale coupled ocean-atmosphere configuration for tropical cyclone forecasting in the South West Indian Ocean basin"
<p>Configuration files for AROME Indian Ocean, NEMO and OASIS which are necessary to reproduce the results in the publication :</p> <p>Corale, L; Malardel S. , Bielli S. and M-N Bouin (2022) Evaluation of a mesoscale coupled ocean-atmosphere configuration for tropical cyclone forecasting in the South West Indian Ocean basin. <em>Earth and Space Science.</em></p>
Dataset for model input of WRF model for the paper:Modulation of Extratropical Cyclones by Previous Cyclones via the Sea Surface Temperature Anomaly over the Sea of Japan in Winter
<p>This is the dataset and code for generating the lower boundary condition which used in our study submitted to the JGR-Atmospheres. The meteorological data for the initial condition are available on NCEP-FNL ftp database.</p>
A Numerical Study of Tropical Cyclone and Ocean Responses to Air-sea Momentum Flux at High Winds
<p>The simulation data output from FIO-AOW for tropical cyclone study</p>
Data for "Examining outer band supercell environments in landfalling tropical cyclones using ground-based radar analyses" v3
<p>The data here are archived for open data access for the publication entitled "Examining outer band supercell environments in landfalling tropical cyclones using ground-based radar analyses" submitted to <em>Monthly Weather Review</em>.</p> <p>Radar data are archived in netCDF format in which variables are identified by their radar moment. The radar data are separated by SR1 and KLCH for Hurricane Laura. For Hurricane Frances, the relevant SR data are contained in the frances_sr_data.tar.gz file.</p> <p>The csv archive contains the track information for objectively identified supercell storms from the manuscript.</p> <p>Questions about the data may be directed to addison.alford@noaa.gov.</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
Data for Tropical Cyclones flood hazards and impacts in Beira for study "Exploring coastal climate adaptation through storylines: Insights from Cyclone Idai in Beira, Mozambique"
<p>Data for Tropical Cyclones flood hazards and impacts in Beira for study "Exploring coastal climate adaptation through storylines: Insights from Cyclone Idai in Beira, Mozambique"<br><br><span><a href="../api/records/12664900/draft/files/hmax_idai_ifs_rebuild_bc_hist_rain_surge_noadapt.tiff/content" target="_blank" rel="noopener noreferrer">hmax_idai_ifs_rebuild*</a> -> Flood maps<br><a href="../api/records/12664900/draft/files/spatial_idai_ifs_rebuild_bc_3c-hightide_rain_surge_retreat.gpkg/content" target="_blank" rel="noopener noreferrer">spatial_idai_ifs_rebuild*</a> -> Impacts<br></span></p>
Data for "Why Does Atmospheric Radiative Heating Weaken Midlatitude Cyclones?"
<p>Datasets of eddy kinetic energy, generation of eddy available potential energy due to radiation, and Northern Hemisphere composites of the covariance between temperaturea and radiative heating anomalies for the control and climatological radiation experiments. </p>
Response of a fringing reef coastline to the direct impact of a tropical cyclone
<p>The data contains observations from the impact of Tropical Cyclone Olwyn to northwest Western Australia in March 2015. A cross-shore array of 5 pressure sensors were deployed to measure wave heights and water levels at Point Jurabi (~5 km north of Tantabiddi), Ningaloo Reef; beach morphology was measured pre- and post-cyclone using RTK-DGPS; and a two-way coupled model was developed using Delft3D and SWAN. </p> <p> </p> <p>The data sets contains the raw pressure sensor measurements (.rsk), beach morphology grids (.txt), and model input/output files. Please see the 'Cuttler_etal_2017_Metadata.pdf', 'ReadMe.txt' file or contact michael.cuttler@uwa.edu.au for further information</p>
Southern Hemisphere cyclones tracks 1979-2013
<p>This includes the filtered cyclone tracks which combined with ERA-Interim reanalysis data provide the basis for the results published in "Which extra-tropical cyclones contribute most to the transport of moisture in the Southern Hemisphere?" by Sinclair and Dacre in Journal of Geophysical Research - Atmospheres. ERA-Interim reanalysis data can be obtained freely from https://apps.ecmwf.int/datasets/</p> <p>The cyclone tracks were produced using TRACK. For the method and relevant references please see the paper. For details of the data files, see the README file (README_tracks).</p>
The Diurnal Cycle of Integrated Kinetic Energy and Wind Radii in a Simulated Tropical Cyclone
<p>Model source code and output of a 340-day-long Cloud Model 1 simulation of a tropical cyclone and associated post-processing scripts.</p>
Tropical sand cays as natural paleo-cyclone archives
<p>Sand cays are valuable paleo-archives that can significantly increase our understanding of Holocene tropical cyclone variability. Here we conducted detailed sedimentological and chronological analyses from a 195-cm-depth pit excavated on Guangjin Island (northern South China Sea), a cay influenced by frequent tropical cyclones. Radiometric dating of multiple deposits revealed that foraminifera, soft coral spicules, and gastropod shells yielded variable age distributions, while U/Th ages of pristine <em>Acropora</em>branches provided a clear record of deposition and cay formation. Based on this robust chronostratigraphy, the proportions of > 2mm grain-size fraction within the deposits corresponded with the frequency of paleo-typhoons recorded by historical records in recent centuries. U/Th ages of <em>Acropora </em>branches from the deposits matched with three known historical typhoon events. Our results highlight the potential of cyclone-deposited sand cays as new archives for recording paleo-cyclones.</p>
MITgcm idealized test-case : cyclonic gyre in a mid-latitude closed basin
<p>Model grid data and hydrodynamic output files of the Z-coordinate Massachusetts Institute of Technology general circulation model (MITgcm) for an idealized case-study describing a cyclonic gyre in a mid-latitude closed basin.<br> <br> The domain size is (Ni,Nj,Nk)=(256,256,60).<br> <br> The grid files (Computational_[...]) contain single precision variables (4 bytes), while the hydrodynamic output files contain variables stored in double precision (8 bytes).<br> </p>
Effects of surface fluxes on the moist potential vorticity distribution in the tropical cyclone boundary layer
<p>Hourly model outputs (t=150-240 hrs) from five axisymmetric simulations of tropical cyclones are provided as follows :</p> <ol> <li>cm1_test40_ver2 (referred to as CONTROL in the manuscript)</li> <li>cm1_test41_ver2 (referred to as H2.0 in the manuscript)</li> <li>cm1_test42_ver2 (referred to as H0.5 in the manuscript)</li> <li>cm1_test43_ver2 (referred to as M2.0 in the manuscript)</li> <li>cm1_test44_ver2 (referred to as M0.5 in the manuscript)</li> </ol> <p>Model outputs from the 3D simulation are interpolated to cylindrical coordinates and saved individually for each variable in binary format, and these are provided for t=150-240 hrs as follows:</p> <ol> <li>cm1_test13_ver2 (referred to as 3D-TC in the manuscript)</li> </ol> <p>Jupyter notebooks are also provided to read and analyze processed outputs from the datasets described above and plot the figures included in the manuscript. The datasets used in "<em>figure_05.ipynb</em>", "<em>figure_06.ipynb</em>", and "<em>figure_07.ipynb</em>" are large and can be made available by the authors upon request.</p>
Extratropical cyclone tracks in Northern Europe for 2005-2018
<p>This dataset includes filtered extratropical cyclone tracks computed from ERA5 reanalysis data for the region of 0-60E, 50-75N. The tracks were produced using TRACK, ERA5 data is freely available from Copernicus Climate Data Store (<a href="https://cds-beta.climate.copernicus.eu/" target="_blank" rel="noopener noreferrer">https://cds-beta.climate.copernicus.eu/</a>). For details of the data files, see the README file. This track dataset is used in a paper "Classifying extratropical cyclones and their impact on Finland’s electricity grid: Insights from 92 damaging windstorms" by Láng-Ritter et al. (submitted to Natural Hazards and Earth System Sciences).</p>
Global tropical cyclone size and intensity reconstruction dataset for 1959–2022 based on IBTrACS and ERA5 data
<p>A global long-term tropical cyclone (TC) size and intensity reconstruction dataset is generated, covering a time period from 1959 to 2022, with a 3-hour temporal resolution. The machine learning model was established by taking ERA5-derived 10 m azimuthal mean azimuthal wind profiles in six basins for which TCs were generated as input, while the maximum sustained wind speed and radius of maximum wind from the International Best Track Archive for Climate Stewardship (IBTrACS) was used as the learning target. An empirical wind–pressure relationship and six wind profile models were employed to estimate the minimum central pressure and outer sizes (radial distances from the cyclone center to locations where sustained wind speeds of 34, 50 and 64 knots are observed on surface) of the TCs, respectively. Compared to the IBTrACS dataset, the reconsturction dataset contains approximately 3–4 times more data points per characteristic.</p> <p>Over all, this dataset is in terms of both coverage and good accuracy.</p>
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International Brain Laboratory public data
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OpenNeuro
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