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256 results for “Cyclone”
Fig. 8 in Fibrocapsa japonica (Raphidophyceae) occurrence and ecological features within the phytoplankton assemblage of a cyclonic eddy, offshore the Eastern Alboran Sea
Fig. 8: A) Bacillariophyceae, and B) Prymnesiophyceae cell number map (x 103 cells l-1) overlapped to isohalines (bold lines) as in Fig. 4.
Fig. 4 in Fibrocapsa japonica (Raphidophyceae) occurrence and ecological features within the phytoplankton assemblage of a cyclonic eddy, offshore the Eastern Alboran Sea
Fig. 4: Seawater inorganic nitrogen concentration (nitrates + nitrites, μM) overlapped to the 37.0 and 37.5-isohaline (bold lines).
Fig. 3 in Fibrocapsa japonica (Raphidophyceae) occurrence and ecological features within the phytoplankton assemblage of a cyclonic eddy, offshore the Eastern Alboran Sea
Fig. 3: SeaWiFS map of surface chlorophyll a distribution in the Western Mediterranean Sea at the time of sampling: the circle encloses the cyclonic eddy. Data are integrated on 8 days (8th-15th October 2006), web source: http://reason.gsfc.nasa.gov/Giovanni.
Fig. 1 in Fibrocapsa japonica (Raphidophyceae) occurrence and ecological features within the phytoplankton assemblage of a cyclonic eddy, offshore the Eastern Alboran Sea
Fig. 1: Map showing the sampling locations in the Western Mediterranean Sea. Full symbols indicate the stations extensively analysed in the present study: M15-M19 and A1-A6. The window on top shows typical patterns of circulation of Modified Atlantic Water (MAW) in the Western Mediterranean Sea: continuous lines indicate steady paths and dashed lines outline the mesoscale currents throughout the year (modified from Millot, 1999).
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>
Characterization of the mean and extreme Mediterranean cyclones and their variability during the period 1500 BCE to 1850 CE
<p>Essential files for reproduction of Figures in manuscript "Characterization of the mean and extreme Mediterranean cyclones and their variability during the period 1500 BCE to 1850 CE".</p> <p>Respository contains cyclone tracking output (cyclone_tracking.txt), cyclone frequency files of CESM and ERA5 for the common period 1981-2010 (cyclone_freq_ERA5_1981_2010.nc, cyclone_freq_CESM_1981_2010.nc), the cyclones that make up the composites (cmed_composites.zip, emed_composites.zip, zip files contain multiple nc files), the Pearson correlation coefficients between the PCs of the modes of circulation and cyclone characteristics (correlations.zip, containing multiple nc files), and the cyclone frequencies used for the superposed epoch analysis (freq_pre_eruption.nc, freq_post_eruption.nc)</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>
Identification of high-wind features within extratropical cyclones using a probabilistic random forest - Part 2: Climatology - Video Supplement
<p>These videos provide examples of application of RAMEFI (RAndom-forest based MEsoscale wind Feature Identification) for winter storm cases between 2000 and 2019 and an animation of cyclone-relative occurrence (cf. Fig. 7 of 10.5194/wcd-2023-10).</p>
INCYDE: A large scale cyclone detection and intensity estimation dataset using satellite infrared imagery
<p>INCYDE (INSAT-based Cyclone Detection and Intensity Estimation) is a cyclone detection and intensity estimation dataset. The cyclone images in the dataset are captured from INSAT 3D/3DR satellites over the Indian Ocean. The proposed INCYDE dataset contains over 100k cyclone images with augmentations taken from cyclones over the Indian Ocean from the year 2013 to 2021. The dataset pertains to two specific tasks: cyclone detection as an object detection task, and intensity estimation as a regression task. In addition to the dataset, this study introduces baseline models that were trained on the newly presented dataset</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>
Dataset: Cyclones Modulate the Control of the North Atlantic Oscillation on Transports into the Barents Sea
<p>This repository contains the data presented in '<strong>Heukamp et al., 2023 - Cyclones Modulate the Control of the North Atlantic Oscillation on Transports into the Barents Sea'</strong>.</p>
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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.
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DANDI Archive for NWB datasets
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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.