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256 results for “Cyclone”

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zenodo40/100

"I am a journalist myself, working for a public radio and television in the Netherlands. As a radio reporter Ivisited Bangladesh just after the cyclone Sidr hit the coastal area in November 1997 (…) Itravelled to the islands on a boat. On that boat were two boatmen and one of them started singing while we were sailing. As Igeotagged this song you can see exactly where it was. Iwas staying at that time in Pirojpur, took a taxi to the river and got a boat. Along tall typical motorboat. It was a journey of three-quarters of an hour during which he sang two songs." [Jeroen/zeshoog]12 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice

"I am a journalist myself, working for a public radio and television in the Netherlands. As a radio reporter Ivisited Bangladesh just after the cyclone Sidr hit the coastal area in November 1997 (…) Itravelled to the islands on a boat. On that boat were two boatmen and one of them started singing while we were sailing. As Igeotagged this song you can see exactly where it was. Iwas staying at that time in Pirojpur, took a taxi to the river and got a boat. Along tall typical motorboat. It was a journey of three-quarters of an hour during which he sang two songs." [Jeroen/zeshoog]12

opencc-by-4.0Dec 2019View details →

dryad40/100

Variations in the Intensity and Spatial Extent of Tropical Cyclone Precipitation

Open the record for dataset details and reuse information.

publicDec 2019View details →

dryad40/100

Trait-based sensitivity of large mammals to a catastrophic tropical cyclone: DNA metabarcoding data

Open the record for dataset details and reuse information.

publicNov 2023View details →

dryad40/100

Data from: TC-GEN: Data-driven tropical cyclone downscaling using machine learning-based high-resolution weather model

Open the record for dataset details and reuse information.

publicSep 2024View details →

zenodo36/100

Bangladesh - Tropical Cyclone Historical Catalogue

Gridded data for major historical tropical cyclones over Bangladesh. 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. 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. The catalogue contains the following tropical cyclones (landfall date): BOB01 (30/04/1991 00:00), BOB07 (25/11/1995 09:00), TC01B (19/05/1997 15:00), Akash (14/05/2007 18:00), Sidr (15/11/2007 18:00), Rashmi (26/10/2008 21:00), Aila (25/05/2009 06:00), Viyaru (16/05/2013 09:00), Roanu (21/05/2016 12:00), Mora (30/05/2017 03:00), Fani (04/05/2019 06:00), Bulbul (09/11/2019 18:00).. File Types <ul> <li>tsens.*.tar.gz 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>fpens.*.tar.gz 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>fp.fg.T1Hmax.tar.gz 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>fp.Rmodels.fg.tar.gz  R GAM model data used to create best estimate netCDF footprints.  Compressed tar archive containing output from mgcv gam model saved as an Rdata file.</li> <li>storm_tracks.tar.gz 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>

opencc-by-4.0Jan 2020View details →

zenodo36/100

Surface cyclone mask for the Antarctic Circumnavigation Expedition from December 2016 – March 2017.

Dataset abstract This dataset contains a surface cyclone mask, which records the presence of a surface cyclone along the track of the Antarctic Circumnavigation expedition (ACE). The surface cyclones are calculated applying a 2D cyclone identification algorithm (Wernli and Schwierz, 2006; Sprenger et al., 2017) using global operational analysis data of the European Centre for Medium Range Weather Forecasts. Dataset contents - cyclone_mask_1h.csv, data file, comma-separated values - data_file_header.txt, metadata, text - README.txt, metadata, text Dataset license This surface cyclone mask dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/

opencc-by-4.0Aug 2020View details →

dryad36/100

Replication data for global population profile of tropical cyclone exposure during 2002 and 2019

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.

opencc-zeroJan 2024View details →

zenodo36/100

Spring tropical cyclones modulate near-surface isotopic compositions of atmospheric water vapour at Kathmandu, Nepal

All these data have been published in a ACP paper. If you use these data in any conditions, please cite the following publicaiton: Adhikari, N., Gao, J., Zhao, A., Xu, T., Chen, M., Niu, X., and Yao, T.: Spring tropical cyclones modulate near-surface isotopic compositions of atmospheric water vapour at Kathmandu, Nepal, EGUsphere, https://doi.org/10.5194/egusphere-2023-2186, 2023.

opencc-by-4.0Mar 2024View details →

dryad36/100

Dependence of tropical cyclone weakening rate in response to an imposed moderate environmental vertical wind shear on the warm-core strength and height of the initial vortex

This study investigated the dependence of the early tropical cyclone (TC) weakening rate in response to an imposed moderate environmental vertical wind shear (VWS) on the warm-core strength and height of the TC vortex using idealized numerical simulations. Results show that the weakening of the warm core by upper-level ventilation is the primary factor leading to the early TC weakening in response to an imposed environmental VWS. The upper-level ventilation is dominated by eddy radial advection of the warm-core air. The TC weakening rate is roughly proportional to the warm-core strength and height of the initial TC vortex. The boundary-layer ventilation shows no relationship with the early weakening rate of the TC in response to an imposed moderate VWS. The findings suggest that some previous diverse results regarding the TC weakening in environmental VWS could be partly due to the different warm-core strengths and heights of the initial TC vortex.

opencc-zeroMar 2024View details →

zenodo36/100

Data and models for "Center-fixing of tropical cyclones using uncertainty-aware deep learning applied to high-temporal-resolution geostationary satellite imagery" by Lagerquist et al.

The file geocenter_models.tar contains all models comprising the GeoCenter ensemble: 3 convolutional neural networks (CNN), 3 isotonic-regression files (one for correcting each CNN’s mean estimate), and 3 more isotonic-regression files (one for correcting each CNN’s ensemble spread). Every model is found in a subdirectory whose names indicate which infrared (IR) wavelengths are used as input to the CNN. For example: <ul> <li> wavelengths-microns=3.900-7.340-13.300/model.weights.h5: An HDF5 file containing the trained CNN that uses data from bands 7, 10, 16 (corresponding to 3.9, 7.34, and 13.3 microns on the GOES ABI imager). The trained CNN can always be read by neural_net_utils.read_model() in the ml4tccf library (https://doi.org/10.5281/zenodo.15116854). </li> <li> wavelengths-microns=3.900-7.340-13.300/model_metadata.p: A Pickle file containing metadata for the trained CNN. This file is needed to read the CNN itself with neural_net_utils.read_model(). Otherwise, you will probably never need to access this metafile directly. </li> <li> wavelengths-microns=3.900-7.340-13.300/isotonic_regression/isotonic_regression.dill: A Dill file containing isotonic-regression models used to bias-correct the ensemble mean from the same CNN. The trained isotonic-regression models can always be read by scalar_isotonic_regression.read_file() in the ml4tccf library. Note that there are technically two isotonic-regression models for every CNN’s ensemble mean: one that bias-corrects the x-coordinate of the TC-center, another that bias-corrects the y-coordinate. </li> <li> wavelengths-microns=3.900-7.340-13.300/uncertainty_calibration/uncertainty_calibration.dill: A Dill file containing isotonic-regression models used to bias-correct the ensemble spread from the same CNN. In the ml4tccf code, I make a distinction between “isotonic_regression” (correcting the ensemble mean) and “uncertainty_calibration” (correcting the ensemble spread), but note that both models are isotonic regression and use the sklearn.isotonic.IsotonicRegression class. The trained uncertainty-calibration models can always be read by scalar_uncertainty_calibration.read_file() in the ml4tccf library. Again, note that there are technically two uncertainty-calibration models per CNN: one for spread in the x-coordinate, one for spread in the y-coordinate. </li> </ul>   As mentioned above, every trained CNN can be read by neural_net_utils.read_model(). Also, every trained CNN can be applied to new data (inference mode) by neural_net_utils.apply_model(). The input argument model_object should be the object returned by neural_net_utils.read_model(), and I suggest setting num_examples_per_batch = 10 to avoid out-of-memory errors. The only other input argument is predictor_matrices, which is a list of two numpy arrays. The first numpy array contains IR imagery centered at the first-guess TC center, and the second numpy array contains ATCF scalars. The first numpy array should have dimensions S (number of TC samples) x 300 (grid rows) x 300 (grid columns) x 9 (lag times) x 3 (wavelengths). Lag times should be in the following order: 240, 210, 180, 150, 120, 90, 60, 30, 0 min ago.  Wavelengths should be in the order indicated by the subdirectory name.  The numpy array itself should contain normalized brightness temperatures at the given lag times and wavelengths, following the grid specifications laid out in the journal paper (a plate carrée grid with 2-km spacing). The original IR data (brightness temperatures) must be normalized to z-scores using the same normalization parameters as in the journal paper, i.e., those based on the training data. See details below. The second numpy array in predictor_matrices should have dimensions S (number of TC samples) x 9 (variables). The variables must in the order: absolute latitude, cosine of longitude, sine of longitude, TC intensity, minimum central pressure, tropical flag, subtropical flag, extratropical flag, disturbance flag. The journal paper contains details on all these variables in one table. These variables must come from A-deck files at the second-most recent synoptic time. Like the IR data, these ATCF scalars must be normalized to z-scores using the same normalization parameters as in the journal paper. See details below.   Once you have predictions (estimated TC-center locations) from a CNN, you can bias-correct these predictions. To read the isotonic-regression model for the given CNN’s ensemble mean, use scalar_isotonic_regression.read_file() in the ml4tccf library. To apply the same model, use scalar_isotonic_regression.apply_models(). For the CNN’s ensemble spread, use scalar_uncertainty_calibration.read_file() and scalar_uncertainty_calibration.apply_models().   To normalize the IR data, you will need the file ir_satellite_normalization_params.tar included with this dataset. Within the tar file is a single zarr file. You can read the zarr file with normalization.read_file() in the ml4tccf library; then you can normalize new data with normalization.normalize_data().   To normalize the ATCF data, you will need the file a_deck_normalization_params.nc included with this dataset. This is a NetCDF file, containing the full set of training values for all 5 ATCF variables that are normalized (the binary storm-type flags are not normalized). You can read this file using any of the standard Python methods for reading NetCDF files, such as xarray.open_dataset(). To normalize new ATCF data, you can use the method normalization._normalize_one_variable(), where the argument actual_values_training is the list of training values from a_deck_normalization_params.nc for the given variable, while actual_values_new is the list of values to be normalized (currently in physical units, to be converted to z-score units).

opencc-by-4.0Sep 2024View details →

zenodo36/100

Tropical Cyclone Characteristics Represented by the Ocean Wave Coupled Atmospheric Global Climate Model Incorporating Wave-Dependent Momentum Flux

This is dataset of global climate model simulation used in the paper "Tropical Cyclone Characteristics Represented by the Ocean Wave Coupled Atmospheric Global Climate Model Incorporating Wave-Dependent Momentum Flux" by Shimura et al. (2021) Followings are the explanation of data file. *** File naming rule ***     {data_group_name}_Exp{experiment_name}_TCnumber{tropical_cyclone_case_number}.nc         data_group_name             - atm             - track         experiment_name             - Wind             - Wave             - SlabO         tropical_cyclone_case_number             - 001             - 002                 ...             - 099             - 100 *** Description on each data group ***          atm: three dimentional atmospheric velocity data         - level: pressure levels for vertical atmospheric data     - longitude: Longitude         - latitude:  Latitude         - velocity_u_component: averaged atmospheric eastward velocity     track: data around tropical cyclone track         - time: UTC time (YYYYMMDDHH)         - longitude_center: Longitude of typhoon center         - latitude_center: Latitude of typhoon center         - central_pressure: typhoon central pressure         - maximum_surface_wind: typhoon maximum surface wind speed         - longitude_sfc: Longitude for surface data around typhoon center         - latitude_sfc: Latitude for surface data around typhoon center         - surface_wind_u_component: surface eastward wind around typhoon         - surface_wind_v_component: surface northward wind around typhoon         - sea_level_pressure: sea level pressure around typhoon         - latent_heat_flux: surface upward latent heat flux         - sensible_heat_flux: surface upward sensible heat flux         - time_atm: UTC time (YYYYMMDDHH) for atmospheric data         - level: pressure levels for atmospheric data         - longitude_atm: Longitude for atmospheric data around typhoon center         - latitude_atm: Latitude for atmospheric data around typhoon center         - velocity_u_component: 3d eastward velocity around typhoon         - velocity_v_component: 3d northward velocity around typhoon

opencc-by-4.0Nov 2021View details →

dryad36/100

Data from: Cyclone-anticyclone asymmetry of eddy detection on gridded altimetry product in the Mediterranean Sea

We perform an Observing System Simulation Experiment that simulates the satellite sampling and the mapping procedure on the sea surface of the high-resolution model CROCO-MED60v40, to investigate the reliability and the accuracy of the eddy detection. The main result of this study is a strong cyclone-anticyclone asymmetry of the eddy detection on the altimetry products AVISO/CMEMS in the Mediterranean Sea. Large-scale cyclones having a characteristic radius larger than the local deformation radius are much less reliable than large-scale anticyclones. We estimate that less than 60% of these cyclones detected on gridded altimetry product are reliable, while more than 85% of mesoscale anticyclones are reliable. Besides, both the barycenter and the size of these mesoscale anticyclones are relatively accurate. This asymmetry comes from the difference of stability between cyclonic and anticyclonic eddies. Large mesoscale cyclones often split into smaller sub-mesoscale structures having a rapid dynamical evolution. The numerical model CROCO-MED60v40 shows that this complex dynamic is too fast and too small to be accurately captured by the gridded altimetry products. The spatio-temporal interpolation smoothes out this sub-mesoscale dynamics and tends to generate an excessive number of unrealistic mesoscale cyclones in comparison with the reference field. On the other hand, large mesoscale anticyclones, which are more robust and which evolve more slowly, can be accurately tracked by standard altimetry products. We also confirm that the AVISO/CMEMS products induce a bias on the eddy intensity. The azimuthal geostrophic velocities are always underestimated for large mesoscale anticyclones.

opencc-zeroDec 2021View details →

dryad36/100

Tree dynamic response and survival in a category-5 tropical cyclone: The case of super typhoon Trami

In the future with climate change, we expect more forest and tree damage due to the increasing strength and changing trajectories of tropical cyclones (TCs). However, to date, we have limited information to estimate likely damage levels, and nobody has ever measured exactly how forest trees behave mechanically during a TC. In 2018, a category-5 TC destroyed trees in our ongoing research plots, in which we were measuring tree movement and wind speed in two different tree spacing plots. We found damaged trees in only the wider spaced plot. Here, we present how trees dynamically respond to strong winds during a TC. Sustained strong winds obviously trigger the damage to trees and forests but inter-tree spacing is also a key factor because the level of support from neighboring trees modifies the effective "stiffness" against the wind both at the single tree and whole forest stand level.

opencc-zeroJan 2022View details →

zenodo36/100

Bifurcation points for tropical cyclone genesis in sheared and dry environments - simulation data

Key information to reproduce the idealized WRF ensemble simulations used for tropical cyclone genesis

opencc-by-4.0Apr 2022View details →

zenodo36/100

Driving Forces of Extreme Updrafts Associated with Convective Bursts in the Eyewall of a Simulated Tropical Cyclone

The model-simulated data used in this study are uploaded here. Due to the large number, the original simulation data are available on request (qnn_nancy@yahoo.com).

opencc-by-4.0May 2022View details →

zenodo36/100

Objective identification of high-wind features within extratropical cyclones using a probabilistic random forest (RAMEFI). Part I: Method and illustrative case studies - Video Supplement

These videos provide examples of application of RAMEFI (RAndom-forest based MEsoscale wind Feature Identification), a new objective identification of high-wind features within extratropical cyclones, for twelve selected case studies.

opencc-by-4.0May 2022View details →

zenodo36/100

Bottom-reached Near-inertial Waves Induced by the Tropical Cyclones, Conson and Mindulle, in the South China Sea

This dataset contains the near-inertial velocity data observed by the mooring at 110.4°E, 17.1°N. It is supplementary to the paper "Bottom-reached Near-inertial Waves Induced by the Tropical Cyclones, Conson and Mindulle, in the South China Sea" submitted to the Journal of Geophysical Research: Oceans.

opencc-by-4.0May 2022View details →

zenodo36/100

Classification of tropical cyclone containing images using a convolutional neural network: performance and sensitivity to the learning dataset

NXTensor extraction library, experiment code, tropical cyclone and background images and their metadata generated from the meterological reanalysis ERA5 and MERRA-2 according to the HURDAT2 cyclone tracks. Version specifications: <ul> <li>NXTensor: v0.3.3.10</li> <li>Experiment code: v2.0.3</li> <li>Image sets: v1</li> </ul>

opencecill-2.1Apr 2022View details →

zenodo36/100

Dataset to adjusted spectral correction method for calculating extreme winds in tropical cyclone affected water areas

This is a dataset of the 50-year wind of an effective temporal resolution of 10 min over three areas with the presence of tropical cyclones at 10 m, 50 m, 100 m and 150 m. There are 18 files in total, with 12 files for the 50-year winds: 'XXcfsru50atYYmcorr_revision.dat' and 6 files for the corresponding latitudes and longitudes: 'TC_XX_lat.dat' and 'TC_XX_lon.dat' The three areas are indexed as E1, W1, W2 (as in the file names 'XX'). The heights are 10 m, 50 m, 100 m and 150 m (as in the filenames 'YY').

opencc-by-4.0Sep 2022View details →

zenodo36/100

TCRIP-MIM: Rapid Intensification Prediction for Tropical Cyclone by Combining Memory In Memory Network with Sequential Satellite Images

This is the official repository for the paper TCRIP-MIM: Rapid Intensification Prediction for Tropical Cyclone by Combining Memory In Memory Network with Sequential Satellite Images. We use the publicly available dataset from Taiwan University (Bai et al., 2019) as experimental data, consisting of four channels of TC satellite images with a temporal resolution of 3 hours, whose preprocessing method is also publicly available. We use infrared and passive microwave TC satellite image sequences for our experiments, each divided into 24-hour segments (8 infrared and 8 passive microwave satellite images, 16 in total), preprocessing and enhancing data as noted above, so there is no experimental error due to different data preprocessing methods. In this study, the 2003–2017 TC dataset from various global basins was divided into training (1097 TCs, 43528 events), validation (188 TCs, 7884 events), and test sets (94 TCs, 3196 events).

opencc-by-4.0Sep 2022View details →

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record