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83 results for “typhoons”
Typhoon radar images
<p>The high-resolution radar reflectivity images from 2010 to 2023. The data is for South China, especially for the Great Bay Area</p>
Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery
<p>Damage estimation status of coconut trees plantation in the Phillippines derived from Sentinel-2 Imagery. Overall we estimated that 14.1 M coconut trees were affected by the typhoon inside our area of study. Please refer to our <a href="https://www.mdpi.com/2072-4292/13/21/4302">original</a> publication for more details.</p> <p>0: Uncertain</p> <p>1: No Data</p> <p>2: Background class</p> <p>3: Unchanged coconut plantation</p> <p>4: Damaged coconut plantation</p> <p>5: New coconut plantation</p> <p> </p> <p> </p> <p> </p>
Resampled FY4A GIIRS radiances from targeted observations for Typhoon Maria(2018) with high temporal resolution of 15 minutes
<p><strong>GIIRS_SSEC_Maria.tar.gz</strong> is the FY-4A GIIRS targeted observations for Typhoon Maria(2018) with 15 minutes temporal resolution, 00z – 23z 10 July, 2018. The radiances are re-sampled data generated at Space Science and Engineering Center of the University of Wisconsin-Madison. These are the data used in the study of Ma et al.(2021).</p> <p>The HDF files are the original GIIRS targeted observations for Typhoon Maria (2018) with 15 minutes temporal resolution which are used in the study of Yin et al.(2021). These radiances have not been re-sampled and for details please refer to Version 1.0 of this dataset: http://doi.org/10.5281/zenodo.4656877 (Han & Yin, 2021).</p>
Data used in a manuscript entitled "Large ensemble simulation for investigating predictability of precursor vortices of Typhoon Faxai in 2019 with a 14-km mesh global nonhydrostatic atmospheric model" submitted to Geophysical Research Letters
<p>This include a dataset used in a manuscript entitled “Large ensemble simulation for investigating predictability of precursor vortices of Typhoon Faxai in 2019 with a 14-km mesh global nonhydrostatic atmospheric model” by Yamada and co-authors, which is submitted to Geophysical Research Letters.</p> <p>Contact: Yohei Yamada (yoheiy@jamstec.go.jp)</p>
model data for "Observed and Model-Simulated Dramatic Bottom Temperature Variations During a Weakened Typhoon in the Northern Yellow Sea"-part06
<p>This dataset is part six of the FVCOM model data of typhoon Lekima during August 2019. The data is in netcdf format. <br>Each nc file contained varaibles including sea level elevation, current, temperature, salinity and mixing parameters of 24 hours dring one day.</p>
model data for "Observed and Model-Simulated Dramatic Bottom Temperature Variations During a Weakened Typhoon in the Northern Yellow Sea"-part05
<p>This dataset is part five of the FVCOM model data of typhoon Lekima during August 2019. The data is in netcdf format. <br>Each nc file contained varaibles including sea level elevation, current, temperature, salinity and mixing parameters of 24 hours dring one day.</p>
model data for "Observed and Model-Simulated Dramatic Bottom Temperature Variations During a Weakened Typhoon in the Northern Yellow Sea"-part02
<p>This dataset is part two of the FVCOM model data of typhoon Lekima during August 2019. The data is in netcdf format. <br>Each nc file contained varaibles including sea level elevation, current, temperature, salinity and mixing parameters of 24 hours dring one day.</p>
model data for "Observed and Model-Simulated Dramatic Bottom Temperature Variations During a Weakened Typhoon in the Northern Yellow Sea"-part03
<p>This dataset is part three of the FVCOM model data of typhoon Lekima during August 2019. The data is in netcdf format. <br>Each nc file contained varaibles including sea level elevation, current, temperature, salinity and mixing parameters of 24 hours dring one day.</p>
model data for "Observed and Model-Simulated Dramatic Bottom Temperature Variations During a Weakened Typhoon in the Northern Yellow Sea"-part04
<p>This dataset is part one of the? FVCOM model data of typhoon Lekima during August 2019. The data is in netcdf format. <br>Each nc file contained varaibles including sea level elevation, current, temperature, salinity and mixing parameters of 24 hours dring one day.</p>
model data for "Observed and Model-Simulated Dramatic Bottom Temperature Variations During a Weakened Typhoon in the Northern Yellow Sea"-Part01
<p>This dataset is part one of the FVCOM model data of typhoon Lekima during August 2019. The data is in netcdf format. Each nc file contained varaibles including sea level elevation, current, temperature, salinity and mixing parameters of 24 hours dring one day.</p>
Four-dimensional wind fields retrieved from GIIRS hyperspectral measurements with 15-minute temporal resolution during Typhoon Maria (2018)
<p>These data were four-dimensional wind fields retrieved from GIIRS hyperspectral measurements with 15-minute temporal resolution during Typhoon Maria (2018). They were also the output results of the findings of Ma et al. (2021).</p>
Dataset for mammatus-like radar echoes along the bases of upper-tropospheric outflow-layer clouds of typhoons.
<p>PPI and RHI data of radar observations of mammatus-like echoes at the cloud bases of upper-level clouds of typhoons observed by the Nagoya-University cloud radar. The observations were carried out at Okinawa, Japan in 2016 and 2019, and Kobe, Japan in 2018 This dataset includes raw data at polar coordinates and grid data interpolated to Cartesian coordinates. This dataset also includes upper-level sounding observation data during the appearance of mammatus-like echoes at Okinawa, Japan in 2016.</p> <p>In this version 2, all RHI data obtained in 3 October 2016 were added.</p>
Four-dimensional horizontal wind fields retrieved from FY4B GIIRS during typhoon Mulan (2022)
<p>This dataset is the four-dimensional (time, pressure, u, v) wind fields retrieved from FY4B GIIRS observations during typhoon Mulan (2022) with a 15-minute interval. </p>
Data and scripts for the study "Resilience of international trade to typhoon-related supply disruptions"
<p>This are the i) input data, ii) final output data, iii) python scripts to reproduce the figures of the study "Resilience of international trade to typhoon-related supply disruptions".</p> <p>i) Input data</p> <p>The input file "routes.nc" provides the geographic transportation network as described in the study.</p> <p>The input data "impacts_*.nc" are necessary to drive simulation runs using the agent-based economic network model Acclimate with the geographic transport network module. The implementation of Acclimate with the geographic transportation module is available as open source on https://github.com/acclimate/acclimate/tree/transport-module with identifier 10.5281/zenodo.7788859.</p> <p>To the derive the input data, the open source file "IBTrACS.WP.v04r00.nc" from IBTrACS as well the Acclimate impact-generator (https://github.com/acclimate/impactgen) is needed. An explanation on the algorithm is given in the study "Resilience of international trade to typhoon-related supply disruptions".</p> <p>ii) Final output</p> <p>The model Acclimate computes, based on the input data, the raw output data. The raw output have in total a volume of over 3.3 Terabyte. We can provide this raw data upon request. However, due to storage space limitations, we will provide in this repository only the regionally and sectorally aggregated output data that were used for the graphs in the paper. These data are described "blocs_*.nc" or "shorts_*.nc"</p> <p> </p> <p>iii) python scripts</p> <p>Using the given python scripts ("*.py" files), the figures of the study "Resilience of international trade to typhoon-related supply disruptions" can be reproduced. The needed input files for the scripts are given under "input data" or "final output". All necessary self-build functions are given in "lib.py" or "regionfinder.py".</p> <p> </p> <p>If any question arise, please contact Dr. Kilian Kuhla under kilian.kuhla@pik-potsdam.de .</p>
Experimental Data for Natural Disaster Mobility Model and Typhoon Haiyan Scenario
<p>The experimental data set for running the <em>Typhoon Haiyan</em> scenario with the <em>Natural Disaster Mobility Model</em> presented in the paper:</p> <p>Milan Stute, Max Maass, Tom Schons, and Matthias Hollick, “<strong>Reverse Engineering Human Mobility in Large-scale Natural Disasters</strong>,” to appear in <em>ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM)</em>, November 2017, Miami Beach, USA.</p>
Data for Manuscript "Deep-Water Near-Inertial Waves and Turbulence on a Continental Slope in the South China Sea during Typhoon Mangkhut (2018)"
<p>Data for manuscript "Deep-Water Near-Inertial Waves and Turbulence on a Continental Slope in the South China Sea during Typhoon Mangkhut (2018)".</p>
Dataset for typhoon paper by Wang&Xiu(2021)
<p>Typhoon induced ocean surface change</p>
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.
Data of the Air-Sea Momentum Flux of the Coastal Marine Boundary Layer During Typhoons
<p>Contains data on the disturbance intensity of windspeed, friction velocity, wind speed and wind direction of Typhoon Chanthu, Hato and Koppu.</p>
Drivers of phytoplankton variability in and Near the Pearl River Estuarine, South China Sea during Typhoon Hato (2017): A numerical study
<p>Simulated and Observed data for JGR manuscript: Drivers of phytoplankton variability in and Near the Pearl River Estuarine, South China Sea during Typhoon Hato (2017): A numerical study. Please read the "A Data Interpretation - README.pdf" before loading the dataset.</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.
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.