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182 results for “Tropical Cyclones”
Coastal extreme sea levels in the Caribbean Sea induced by tropical cyclones
<p>Previous version contained the return levels of coastal significant wave height and sea surface elevation along the Caribbean coastlines corresponding to the periods of 10, 50, 100, 200 and 500 years. The return periods have been computed fitting a Generalised Pareto Distribution to a set of hydrodynamic-wave coupled ocean simulations forced with 1000 synthetic tropical cyclones. See the paper Martin et al (under review) for details. </p> <p> The file (Return_levels.mat) contains a Matlab table (with header names) indicating latitude, longitude and the return levels described above, named as Hs (significant wave height) and SSE (sea surface elevation). </p> <p>Three other datasets have been included, with the subsample of the Tropical Cyclones selected for the study (Subsample.mat), the geographic data of the coastal grid points used for our analysis (Coastaline.mat), and finally a dataset with the results of the analysis presented in the paper (Results_4_runs.mat). All datasets are provided in a .mat file, generated using Matlab. </p> <p>First dataset contains a Matlab table (with header names) indicating latitude and longitude, radius of maximum wind speed, minimum pressure and maximum wind speed a long the track of the Tropical Cyclone, for the 1000 samples selected. </p> <p>Second dataset provides both Latitude and Longitude of the coastal grid points used for the analysis. </p> <p>The last dataset contains a Matlab table (with header names) where the first column represents the Tropical Cyclone, columns 2,3 and 4 contain the maximum of sea surface elevation (SSE) during the lifetime of the Tropical Cyclone for each coastal point, for the 3 decoupled runs, wind-forced only, pressure-forced only and wind and pressure respectively. The next 4 columns contain the values of the variables for the coupled simulations with WWM-III, maximum significant wave height (Hs), maximum SSE, median of Peak Direction (Dp) and median of Peak period (Tp). All these variables contain a description in the dataset as variable information, and are provided for all coastal points of our grid. </p>
An Objective Detection of Separation Scenario in Tropical Cyclone Trajectories Based on Ensemble Weather Forecast Data
<p>This repository contains the data used in "An Objective Detection of Separation Scenario in Tropical Cyclone Trajectories Based on Ensemble Weather Forecast Data" by Oettli and Kotsuki (submitted to Journal of Geophysical Research: Atmospheres).</p>
Data from : Damage to tropical forests caused by tropical cyclones is driven by wind speed but mediated by topographical exposure and tree characteristics
<p>These datasets have been used in the following paper:</p> <p>Ibanez, T., Bauman, B., Aiba, S.-i., Arsouze, T. Bellingham, P.J., Birkinshaw, C., Birnbaum, P., Curran, T.J., DeWalt, S.J., Dwyer, J., Fourcaud, T., Franklin, J., Kohyama, T.S., Menkes, C. Metcalfe, D.J., Murphy, H., Muscarella, R., Plunkett, G.M., Sam, C., Tanner, E., Taylor, B.N., Thompson, J., Ticktin, T., Tuiwawa, M.V., Uriarte, U., Webb, E.L., Zimmerman, J.K., Keppel, G. Damage to tropical forests caused by tropical cyclones is driven by wind speed but mediated by topographical exposure and tree characteristics. Accepted for publication in <em>Global Change Biology</em>.</p> <p>Data users are invited to cite this paper and the original paper(s) corresponding to the data they use (see "Reference" column in each dataset). We also encourage potential users to contact the data owners for collaboration.</p> <p>These datasets are compiled empirical data on the damage caused by 11 cyclones occurring over the past 40 years, from 74 forest plots representing tropical regions worldwide. Damage are given at the tree (whether or not each tree has been uprooted or snapped) and at the plot level (number of uprooted or snapped trees in each plot).</p> <p>MSW: Maximum sustained wind speed (m.s-1)</p> <p>EXP: Topographical exposure to wind</p> <p>DBH: Diameter at breast height (cm)</p> <p>WD: Wood density (g.cm-3)</p>
Tropical Cyclone events in Reanalysis datasets
<p>Tropical Cyclone events detected by an objective tracker in five reanayses: CRA40, ERA5, CFSR, JRA55, MERRA2, during 1981-2020.</p>
Changes in four decades of near-CONUS tropical cyclones in an ensemble of 12km thermodynamic global warming simulations
<p>Snapshot level data of TC extractions from the thermodynamical global warming runs described in "Changes in four decades of near-CONUS tropical cyclones in an ensemble of 12km thermodynamic global warming simulations."</p>
Supplementary materials for the manuscript "Revisiting the Contributions of Surface Sensible and Latent Heat Fluxes to Tropical Cyclones"
<p>A subset of outputs of the STD, CTL, SH+LH-_OUT and SH-LH+_OUT experiments in "Revisiting the Contributions of Surface Sensible and Latent Heat Fluxes to Tropical Cyclones".</p>
Offshore wind turbine damage probability maps and hub height TC wind speeds for U.S. Atlantic and Gulf Coasts exposed to historical and future tropical cyclones
<p>Damage probability maps for offshore wind turbines exposed to tropical cyclones (TCs) under both historical and future climate scenarios along the U.S. Atlantic and Gulf Coasts are presented in this dataset. TCs are generated using <a href="../records/10392725" target="_blank" rel="noopener">The Risk Analysis Framework for Tropical Cyclones (RAFT)</a>, forced by <a href="https://pcmdi.llnl.gov/CMIP6/" target="_blank" rel="noopener">CMIP6</a> historical and future global climate simulations. Maximum wind speeds for 20- and 50-year TCs are processed through a <a href="https://www.sciencedirect.com/science/article/pii/S0960148120311423">fragility function</a> specific to offshore wind (OSW) turbines in order to estimate the probability of damage – specifically yielding and buckling – based on wind speed intensity. </p> <p><strong>Included data:</strong></p> <ul> <li><strong>TC wind speeds:</strong> Peak 10-min mean hub height (90m) TC wind speed maps</li> <li><strong>Damage states:</strong> Yielding and Buckling probability maps for OSW turbines</li> <li><strong>Geographic coverage:</strong> U.S. Atlantic and Gulf Coasts (up to 200km from the shoreline)</li> <li><strong>Time periods:</strong> Historic (1980-2014) and Future (2066-2100)</li> </ul> <p><strong>Methodology:</strong></p> <ul> <li><strong>Tropical cyclone simulation:</strong> The RAFT TC model is used to simulate storms for historical and future climates using CMIP6 environmental conditions.</li> <li><strong>TC impact metric:</strong> Wind speeds associated with 20- and 50-year return period TCs are used to estimate the aerodynamic and sea wave loading on OSW turbines.</li> <li><strong>Fragility functions:</strong> Wind speeds are input into a fragility function developed for OSW turbines, estimating the probability of yielding and buckling damage.</li> <li><strong>Damage probability maps:</strong> The results consist of eight (8) gridded damage probability maps representing the likelihoods of yielding and buckling to OSW turbines from 20- and 50-year TCs under historical and future climatic conditions.</li> </ul> <p><strong>Potential Uses:</strong></p> <ul> <li>Assessing the spatial vulnerability of OSW infrastructure to TCs</li> <li>Supporting decision-making for the design and siting of turbines</li> <li>Evaluating the impact of climate change on the risk of damage to OSW infrastructure</li> </ul> <p>For further insights into this dataset, users are encouraged to refer to the associated paper: <a href="https://www.nature.com/articles/s43247-024-01887-6">https://www.nature.com/articles/s43247-024-01887-6</a></p> <p>This dataset offers valuable insights into the potential impact of TCs on offshore wind infrastructure, aiding in risk assessment and resilience planning for the renewable energy sector.</p> <p> </p>
Data for publication of "Determining the sensitive parameters of WRF model for the prediction of tropical cyclones in the Bay of Bengal using Global Sensitivity Analysis and Machine Learning"
<p>The data are made available as part of the paper "Determining the sensitive parameters of WRF model for the prediction of tropical cyclones in the Bay of Bengal using Global Sensitivity Analysis and Machine Learning", submitted to Geoscientific Model Development. This data set incorporates selected post-processed files needed to reproduce the results presented in the paper.</p> <p>The data contains six zip files, that are:</p> <ul> <li>Namelist.input files for the WRF model simulations of ten tropical cyclones</li> <li>WRF model simulation outputs using the default parameter values</li> <li>WRF model simulation outputs using the optimal parameter values (which give minimum RMSE value)</li> <li>IMDAA surface observations and IMERG precipitation data</li> <li>IMD observed tracks of ten tropical cyclones</li> <li>Ipython notebooks of sensitivity analysis and machine learning codes</li> </ul> <p>The remaining files are the ncl scripts that were used to obtain the figures. The ncl scripts used the data in the zip files.</p>
WRF output for the Geophysical Research Letters publication "Potential Impacts of Radio Occultation Data Assimilation on Forecast Skill of Tropical Cyclone Formation in the Western North Pacific"
<p>This dataset is the Weather Research and Forecasting (WRF) model output for the <em>Geophysical Research Letters</em> publication entitled "Potential Impacts of Radio Occultation Data Assimilation on Forecast Skill of Tropical Cyclone Formation in the Western North Pacific". The dataset includes the azimuthal-averaged parameters with 0.2°resolution, three-day forecast, and two experiments for all cases analyzed in the publication. Due to the data size, only the variables used in the figures are uploaded (i.e., relative humidity, relative vorticity, temperature, and water vapor mixing ratio). Detailed information, composite calculation, and model settings can be found in the publication.</p>
Tropical cyclones facilitate recovery of forest leaf area from dry spells in East Asia
<p>This online repository copies the source code and the download link of the input data for the research work of analyzing forest leaf area change due to the TC activities in the west pacific ocean basin. </p> <p><strong>TC Track data, mask, climate reanalysis, leaf area, ERA5 (wind speed, surface pressure data), and SPEI dataset: </strong></p> <p><a href="http://YYCdb.synology.me:5833/sharing/YizTR8HPR">http://YYCdb.synology.me:5833/sharing/YizTR8HPR</a></p> <p>password:bg-2022-115</p> <p>File size: 373G</p> <p><strong>The path for downloading the source code/script for analyzing the LAI changes:</strong></p> <p><a href="http://YYCdb.synology.me:5833/sharing/JC2AGt9Kh">http://YYCdb.synology.me:5833/sharing/JC2AGt9Kh</a></p> <p>password:bg-2022-115</p> <p>File size: 880M</p> <p><strong>Data table for all events used in this study:</strong></p> <p><a href="http://YYCdb.synology.me:5833/sharing/MqA4YFBHk">http://YYCdb.synology.me:5833/sharing/MqA4YFBHk</a></p> <p>password:bg-2022-115</p> <p>Filesize:824K</p>
TROVA outputs for the extratropical transition of tropical cyclones in the North Atlantic basin
<p>Output data from the TRansport Of water Vapor (TROVA) tool for the research paper "Evaluating changes in the moisture sources for tropical cyclones precipitation in the North Atlantic that underwent extratropical transition".</p>
Underwater ambient sound in tropical cyclones
<p>Underwater ambient sound measurements were made in three tropical cyclones: Hurricane Gustav (2008) in the Gulf of Mexico, and Typhoons Fanapi (September 2010) and Megi (October 2010) in the western Pacific Ocean as part of the ITOP (Impact of Typhoons on the Ocean in the Pacific) program. Measurements were made from eight Lagrangian floats, each equipped with one hydrophone, air deployed ahead of these storms by WC-130J aircraft operated by the U.S. Air Force Reserve 53rd Weather Reconnaissance squadron Hurricane Hunters. Floats 50 and 51 were in Gustav, 60, 61 and 62 in Fanapi, and 66, 67 and 68 in Megi. After the storm passage, the Lagrangian floats were recovered by a research vessel. Float positions were determined by interpolating between a few GPS positions taken during the storm passage guided by time-integrated velocity measurements from Electromagnetic Autonomous Profiling Explorer (EM-APEX) floats deployed at the same time.</p> <p>During the passage of tropical cyclones, the hydrophone switched between the work and sleep modes every 30 minutes due to limited data storage. In the work mode, the hydrophones sampled underwater ambient sound twice per second. The sound measurements thus are in 30-min segments. There are 39 (50), 38 (51), 60 (60), 56 (61), 56 (62), 55 (66), 53 (67) and 51 (68) segments (float serial numbers are in brackets), respectively, giving a total of 408 data segments and about 190 hours of sound measurements. Each raw time series has been Fourier transformed to obtain a power spectrum from 40 Hz to 50 kHz, with a spectral resolution of 40 Hz. The sound pressure level (SPL) in decibels (dB) is defined as <span class="math-tex">\(\textrm{SPL} = 20\cdot \textrm{log}(\textrm{P}/\textrm{P}_\textrm{ref}),\)</span> where <span class="math-tex">\(\textrm{P}\)</span> is the hydrophone measured sound pressure, and <span class="math-tex">\(\textrm{P}_\textrm{ref}\)</span> is the reference pressure 1 <span class="math-tex">\(\mu \textrm{Pa}^{2} \textrm{Hz}^{-2}\)</span>. The hydrophones were inter-calibrated in laboratory before and after the deployments, and agreed with a root-mean-square difference of 1–2 dB. The raw sound measurements are labeled SpdbP_raw.</p> <p>Each Lagrangian float carried a variety of instruments including a pumped CTD (conductivity, temperature and depth) sensor, a pumped GTD (gas tension device), a motor to control drogue, and another motor to control the float's buoyancy. These instruments generated noise of different temporal and spectral features. Sound measurements contaminated by noise were removed as described in <em>Zhao et al. (2014 JPO)</em>. One exception is GTD, which ran for 90% of the time for floats 50 and 51 (Gustav) and 66, 67, and 68 (Megi), and caused significant contamination on the < 5-kHz sound data. However, the > 5-kHz sound measurements are not affected by the GTD noise, and thus kept for future studies (detect rain events and breaking waves). The cleaned sound measurements are labeled SpdbP_clean.</p> <p>We decomposed the underwater ambient sound into three components according to their time scales. First, we calculate background sound, defined as the mean of the lowest 10% sound level over the 30 min period. The background sound generally rises and falls with increasing/decreasing wind speed and the presence of bubble clouds. Second, sound fluctuation is obtained by removing the background sound from the original data. Third, the sound fluctuation is divided into two components using two matched temporal filters. The second-scale fluctuation is obtained by high-pass filtering the sound fluctuation using 20-second running mean. The minute-scale fluctuation is obtained by low-pass filtering the sound fluctuation. By this method, the original underwater ambient sound is decomposed into three components: background (SpdbP_background), minute-scale fluctuation (SpdbP_MidFreq), and second-scale fluctuation (SpdbP_HighFreq). We applied the above decomposition method to all 408 30-min sound segments from eight Lagrangian floats, and created 408 figures with the same format. We share here the raw, de-noised, and decomposed sound data for all eight Lagrangian floats (eight Matlab data files) and demonstrate their decomposed sound data (eight PDF files). <a href="/api/files/d9886aea-3829-43d2-b10e-d57bdd77ae7a/Fig-5-Q101623.pdf?versionId=fee16fdd-adef-4419-98f7-3f05188065dd">Fig-5-Q101623.pdf </a> and <a href="/api/files/d9886aea-3829-43d2-b10e-d57bdd77ae7a/Fig-6-J091801.pdf?versionId=1607d29d-cb3c-4cbb-bb58-12c09b3a788c">Fig-6-J091801.pdf </a>are Figures 5 and 6 in a recent paper (<a href="https://journals.ametsoc.org/view/journals/atot/aop/JTECH-D-22-0078.1/JTECH-D-22-0078.1.xml">https://journals.ametsoc.org/view/journals/atot/aop/JTECH-D-22-0078.1/JTECH-D-22-0078.1.xml</a>). </p>
Supporting Dataset for "Reducing a tropical cyclone weak-intensity bias in a global numerical weather prediction system"
<p>This archive supports the submission of "Reducing a tropical cyclone weak intensity bias in a global numerical weather prediction system" to Monthly Weather Review. It contains model configurations, the software used to create ensemble perturbations, the software used to compute the diagnostics discussed in the text, and the software use to plot figures.</p> <p>After downloading, the contents can be extracted using:</p> <blockquote> <p>tar -xzf idealtc1_archive-1.tgz</p> </blockquote>
Data for "Tropical Cyclone Supercell Response to the Coast using a Climatology of Radar-Derived Azimuthal Shear"
<p>This dataset contains a .csv file published alongside the article entitled "Tropical Cyclone Supercell Response to the Coast using a Climatology of Radar-Derived Azimuthal Shear" for consideration in <em>Geophysical Research Letters</em>.</p>
Dataset for "Assessing Storm Surge Multi-Scenarios based on Ensemble Tropical Cyclone Forecasting" paper
<p>1000 ensemble track forecast of tropical cyclone Hagibis (2019) is provided in NetCDF format and the computed storm surge forecast is provided in the Excel file.</p>
Variations in the Intensity and Spatial Extent of Tropical Cyclone Precipitation
Open the record for dataset details and reuse information.
Trait-based sensitivity of large mammals to a catastrophic tropical cyclone: DNA metabarcoding data
Open the record for dataset details and reuse information.
Data from: TC-GEN: Data-driven tropical cyclone downscaling using machine learning-based high-resolution weather model
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Bangladesh - Tropical Cyclone Historical Catalogue
<p><strong>Gridded data for major historical tropical cyclones over Bangladesh.</strong></p> <p>Each tropical cyclone has a 9 member ensemble, and comprises of time series and footprints at resolutions of 4.4km and 1.5km based on the Met Office Unified Model dynamically downscaling ECMWF ERA5 data.</p> <p>There are 12 available variables, including: air temperature, maximum wind gust speed, minimum air pressure at sea level and precipitation amounts, available at a range of temporal scales, including model instantaneous values, and hourly and daily aggregations.</p> <p>The catalogue contains the following tropical cyclones (landfall date): <strong>BOB01</strong> (30/04/1991 00:00), <strong>BOB07</strong> (25/11/1995 09:00), <strong>TC01B</strong> (19/05/1997 15:00), <strong>Akash</strong> (14/05/2007 18:00), <strong>Sidr</strong> (15/11/2007 18:00), <strong>Rashmi</strong> (26/10/2008 21:00), <strong>Aila</strong> (25/05/2009 06:00), <strong>Viyaru</strong> (16/05/2013 09:00), <strong>Roanu</strong> (21/05/2016 12:00), <strong>Mora</strong> (30/05/2017 03:00), <strong>Fani</strong> (04/05/2019 06:00), <strong>Bulbul</strong> (09/11/2019 18:00)..</p> <p><strong>File Types</strong></p> <ul> <li><strong>tsens.*.tar.gz </strong>Time series data for each named storm. Dimensions are typically: forecast_period, forecast_reference_time, latitude and longitude. Compressed tar archive containing multiple netCDF files.</li> <li><strong>fpens.*.tar.gz </strong>Time-aggregated data for each ensemble member for each storm. Variables: max gust speed (fg), minimum sea-level pressure (psl), instantaneous u-wind (ua) and v-wind (va) components. Dimensions are typically: forecast_reference_time, latitude and longitude. Compressed tar archive containing multiple netCDF files.</li> <li><strong>fp.fg.T1Hmax.tar.gz </strong>A single best estimate gust-speed (fg) footprint with lower, median and upper bounds accounting for ensemble variation, for each names storm. Compressed tar archive containing multiple netCDF files.</li> <li><strong>fp.Rmodels.fg.tar.gz </strong>R GAM model data used to create best estimate netCDF footprints. Compressed tar archive containing output from <em>mgcv</em> gam model saved as an Rdata file.</li> <li><strong>storm_tracks.tar.gz </strong>Storm tracks for each ensemble member of each names storm from Tempest Extremes tracking algorithm. Compressed tar archive containing multiple .DAT text files.</li> </ul>
Replication data for global population profile of tropical cyclone exposure during 2002 and 2019
<p>Tropical cyclones have far-reaching impacts on livelihoods and population health that often persist years after the event. Characterizing the demographic and socioeconomic profile and the vulnerabilities of the exposed populations is essential to assess health and other risks associated with future tropical cyclone events. Estimates of exposure to tropical cyclones are often regional rather than global and do not consider population vulnerabilities. Here, we combine spatially resolved annual demographic estimates with tropical cyclone wind fields estimates to construct a global profile of the populations exposed to tropical cyclones between 2002 and 2019. We find that approximately 560 million people are exposed yearly and that the number of people exposed has increased across all cyclone intensities over the study period. The age distribution of those exposed has shifted away from children (under-5) and towards older people (over-60) in recent years compared to the early 2000s. Populations exposed to tropical cyclones are more socioeconomically deprived than those unexposed within the same country, and this relationship is more pronounced for people exposed to higher intensity storms. By characterizing the patterns and vulnerabilities of populations exposed to tropical cyclones, our results can help identify mitigation strategies and assess the global burden and future risks of tropical cyclones.</p>
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