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22 results for “Tropical storm”
Labels for Emergency Response Imagery from Hurricane Barry, Delta, Dorian, Florence, Ida, Isaias, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon
<p>The csv files contain human-generated labels for Emergency Response Imagery collected by US National Oceanic and Atmospheric Administration (NOAA) after Hurricane Barry, Delta, Dorian, Florence, Ida, Isaias, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon. All authors contributed to labeling the imagery. All labeling was done with an open-source labeling tool (Rafique et al., 2020).</p> <p>All csv files provide the userID (the ID of the anonymous labeler), the NOAA flight, the NOAA image, and 6 labels — allWater (if the image was all water), devType (if the image had buildings/development), washoverType (if the image had washover deposits), dmgType (if the image showed damage to built environment), impactType (if the labeler could identify the coastal impact, using the Storm Impact Scale from Sallenger, 2000), and terrainType (the type of physical environment).</p> <p>Images labeled here correspond to multiple NOAA flights — all listed in the csv file for each jpeg image. These jpeg images can be downloaded directly from NOAA (https://storms.ngs.noaa.gov/) or using Moretz et al. (2020a, 2020b).</p> <p>There are three csv files:</p> <p>ReleaseData_10172022.csv has 10,237 labels for 4250 images. These labels were generated by coastal scientists. The csv also contains the Latitude and Longitude of the image center (from NOAA).</p> <p>ReleaseDataQuads.csv has 400 labels for 100 images. These labels were generated by coastal scientists. The images labeled in this set correspond to original NOAA images that have been split into quadrants. Splitting images was done with ImageMagick. The command used to split the images was:</p> <p>`magick mogrify -crop 2x2@ +repage -path ../quadrants *.jpg`</p> <p>The naming convention corresponds to the image quarter — the *-0.jpg is upper left, *-1.jpg is upper right, *-2.jpg is lower left, and *-3.jpg is the lower right.</p> <p>ReleaseDataNCE.csv has 400 labels for 100 images. These images were labeled by non-coastal scientists. Note that the 100 images were also labeled by coastal scientists — those labels can be found in ReleaseData_v3.csv.</p> <p>There is another companion dataset to this, with slightly different labels (Goldstein et al., 2020).</p> <p>A zip file of images is also provided for demonstration purposes (images.zip). These are resized copies made with imagemagick, with the longest dimension set at 2000 pixels ( `mogrify -resize 2000x2000`). For full size images, please download the jpegs directly from NOAA.</p>
DeepSurge storm surge predictions for HighResMIP tropical cyclones
<p>DeepSurge is a newly presented deep-learning approach to modeling the storm surge generated by a tropical cyclone (TC). This dataset is a collection of DeepSurge outputs for synthetic TCs in the North Atlantic generated by the HighResMIP project (Haarsma et al. 2016) for a simulated historical (1950-2014) and future (2015-2050) climate under the climate scenario SSP585.</p> <p>The data generation process and data analysis is detailed in an upcoming publication. The storm surge data presented here intentionally does not include the effects of sea level rise, rainfall, or other factors, in order to isolate the effects of changing TC climatology on future storm surge risk.</p> <h4>Dataset format</h4> <p>The data comes in the form of maximum surge levels at 2846 near-coastal locations for each synthetic TC. Each TC is defined by the corresponding track in the HighResMIP TempestExtremes dataset (Roberts 2019). The data is presented in NetCDF format, with two dimensions: </p> <ul> <li>'nodes', the number of near-coastal locations, always 2846.</li> <li>'tracks', the number of tracks in the simulation, which is different in each file.</li> </ul> <p>There are 6 variables in each file:</p> <ul> <li>'lons' and 'lats', the coordinates of the nodes in degrees North and East respectively.</li> <li>'track_valid' is a binary indicator (zero for false, one for true) indicating whether the TC occurs within the region of interest (HighResMIP tracks are global, but we only simulate those in the North Atlantic)</li> <li>'track_done' is another binary indicator for whether the track has been simulated. It should indicate true for all tracks for which 'track_valid' is true.</li> <li>'max_zeta' provides the predicted maximum surge height, in meters, for each storm at all 2846 nodes. This data is only valid in entries for which the corresponding 'track_done' and 'track_valid' indicators are true.</li> <li>'years' is the year in which each simulated TC occurs.</li> </ul>
Segmentation Labels for Emergency Response Imagery from Hurricane Barry, Delta, Dorian, Florence, Isaias, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon
<p>The zip file here contains 1,179 pairs of human-generated segmentation labels and images from Emergency Response Imagery collected by US National Oceanic and Atmospheric Administration (NOAA) after Hurricane Barry, Delta, Dorian, Florence, Ida, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon. A total of 1,054 unique images were labeled. 946 images were annotated by a single labeler. 95 images were annotated by two labelers. 11 images were annotated by three labelers. 2 images were annotated by five labelers. All authors contributed to labeling, and all labeling was done with an open-source labeling tool (Buscombe et al., 2022).</p> <p>All pixels in each image are labeled with one of four classes: 0 (water), 1 (bare sand), 2 (vegetation - both sparse and dense), 4 (the built environment - buildings, roads, parking lots, boats, etc.)</p> <p>The csv file provided here is a list of each image file name (which includes the anonymized labeler ID), the name of the image without the labeler ID, the name of the corresponding NOAA jpg, the NOAA flight name, the storm name, the latitude and longitude of the image, and a column stating if the image has been labeled multiple times. </p> <p>Images labeled here correspond to multiple NOAA flights — all listed in the csv file for each jpeg image. These jpeg images can be downloaded directly from NOAA (https://storms.ngs.noaa.gov/) or using Moretz et al. (2020a, 2020b). The images included in this data release correspond to original NOAA images that have been resized and then split into quadrants (using ImageMagick). The naming convention corresponds to the image quarter — the *-0.jpg is upper left, *-1.jpg is upper right, *-2.jpg is lower left, and *-3.jpg is the lower right.</p> <p><br> The resize command used was:</p> <p><br> #to resize and then quarter<br> #Dir structure is:<br> # --Desktop<br> # |- originals<br> # |- resized<br> # |- quarters</p> <p>`cd originals`<br> `mogrify -resize 2000x2000 -path ../resized *.jpg`</p> <p>#then quarter them<br> `cd ..`<br> `cd resized`</p> <p>`mogrify -crop 2x2@ +repage -path ../quarters *.jpg`</p> <p>For full size images, please download the jpegs directly from NOAA.</p>
Sowing storms: how model timestep can control tropical cyclone frequency in a GCM
<p>Supplementary dataset to JAMES article "Sowing storms: how model timestep can control tropical cyclone frequency in a GCM" DOI:10.1029/2021MS002791</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>
Supplemental information for: Annual tropical-rainforest productivity through two decades: Complex responses to climatic factors, CO2 and storm damage
<p>The supplemental files in this deposition contribute additional information useful for understanding the analyses in the associated manuscript. Two files contain the annual (CARBONO Project measurement-year) data for productivity and for environmental conditions that were analyzed in the paper. The annual productivity metrics were derived from the CARBONO Project litterfall and tree-growth data that were provided and documented in prior Dryad data depositions. A third file in this deposition provides the daily data underlying the analyzed meteorological factors. The fourth file lists the standardized set of 66 test models that were used to test each of the five productivity metrics for environmental responses. The fifth file is an auxiliary table documenting tree mortality by year. The included README file documents the information in these files.</p>
Storm-scale and fine-scale boundary layer structures of tropical cyclones simulated with the WRF-LES framework
<p>The numerical simulations were carried out on the Tianhe Supercomputer, China. The TCBL data used in this study are uploaded here. Due to the large number, the original simulation data are available on request (liuqy@cma.gov.cn or liguangwu@fudan.edu.cn).</p>
Data from: Aridity and forest age mediate landscape scale patterns of tropical forest resistance to cyclonic storms
Open the record for dataset details and reuse information.
Supplemental information for: Annual tropical-rainforest productivity through two decades: Complex responses to climatic factors, CO2 and storm damage
Open the record for dataset details and reuse information.
Data from: Understanding the mechanisms of anti-tropical divergence in the seabird White-faced Storm-petrel (Procellariiformes: Pelagodroma marina) using a multi-locus approach
Analytical methods that apply coalescent theory to multilocus data have improved inferences of demographic parameters that are critical to understanding population divergence and speciation. In particular, at the early stages of speciation, it is important to implement models that accommodate conflicting gene trees, and benefit from the presence of shared polymorphisms. Here, we employ eleven nuclear loci and the mitochondrial control region to investigate the phylogeography and historical demography of the pelagic seabird White-faced Storm-petrel (Pelagodroma marina) by sampling subspecies across its antitropical distribution. Groups are all highly differentiated: global mitochondrial ΦST = 0.89 (P < 0.01) and global nuclear ΦST varies between 0.22 and 0.83 (all P < 0.01). The complete lineage sorting of the mitochondrial locus between hemispheres is corroborated by approximately half of the nuclear genealogies, suggesting a long-term antitropical divergence in isolation. Coalescent-based estimates of demographic parameters suggest that hemispheric divergence of P. marina occurred approximately 840 000 ya (95% HPD 582 000–1 170 000), in the absence of gene flow, and divergence within the Southern Hemisphere occurred 190 000 ya (95% HPD 96 000–600 000), both probably associated with the profound palaeo-oceanographic changes of the Pleistocene. A fledgling sampled in St Helena (tropical South Atlantic) suggests recent colonization from the Northern Hemisphere. Despite the great potential for long-distance dispersal, P. marina antitropical groups have been evolving as independent, allopatric lineages, and divergence is probably maintained by philopatry coupled with asynchronous reproductive phenology and local adaptation.
Tropical Cyclones in Global Storm-Resolving Models
This dataset contains information about the tropical cyclones simulated by nine global storm-resolving models (global storm-resolving models are global models with a horizontal resolution of < 5 km). The dataset contains two distinct data sources: (1) track files, which contain information about TC position, intensity, and size. (2) gridded data files with radius-height composites of the TC dynamic and thermodynamic structure. There is one track file and one gridded data file for each model. The overall dataset is part of the global storm-resolving model intercomparison project "DYAMOND" (Stevens et al. 2019, https://link.springer.com/article/10.1186/s40645-019-0304-z), and has been used in a study that is currently under review.
Roles of Wind-Driven Currents and Surface Waves in Sediment Resuspension and Transport During a Tropical Storm
<p>Roles of Wind-Driven Currents and Surface Waves in Sediment Resuspension and Transport During a Tropical Storm</p>
Supporting Data for "The Vertical Structure of Tropical Temperature Change in Global Storm-Resolving Model Simulations of Climate Change"
<p>Code and netcdf files of processed X-SHiELD and CMIP6 simulations to reproduce the figures of the revised submission of Timothy M. Merlis, Ilai Guendelman, Kai-Yuan Cheng, Lucas Harris, Yan-Ting Chen, Christopher S. Bretherton, Maximilien Bolot, Linjiong Zhou, Alex Kaltenbaugh, Spencer K. Clark, and Stephan Fueglistaler (2024): "The Vertical Structure of Tropical Temperature Change in Global Storm-Resolving Model Simulations of Climate Change".</p> <p> </p>
Data from: Understanding the mechanisms of anti-tropical divergence in the seabird White-faced Storm-petrel (Procellariiformes: Pelagodroma marina) using a multi-locus approach
Open the record for dataset details and reuse information.
Flight data for Tropical Storm Maliksi
<div> <p>Probe data and dropsonde data collected for Tropical Storm Maliksi</p> </div>
The dynamics of tropical storm Mulan-induced destruction in 3D structure and dissipation in kinetic energy of an anticyclonic eddy in the northern South China Sea
<p>dataset for The dynamics of tropical storm Mulan-induced destruction in 3D structure and dissipation in kinetic energy of an anticyclonic eddy in the northern South China Sea</p>
Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone-driven Storm Tides and Inundation: Model Data and Analysis Code
<p>This repository contains model data and analysis codes related to the manuscript entitled "Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone-driven Storm Tides and Inundation", as follows:</p> <ol> <li>Model data are maximum water surface elevations of ensemble 48-hr forecast ADCIRC model simulations for three historical US landfalling hurricanes: 2017 Irma, 2018 Florence, and 2020 Laura. These are located in the "NameYYYY_Results.tar" archive files as "maxele.63.nc" files. Also included in the tar files are the hurricane forecast track files in Automated Tropical Cyclone Forecasting (ATCF) system format (*.22) and the error variable parameters (*.json) for each forecast. </li> <li>Model data of best-track runs for the 2017 Irma, 2018 Florence, and 2020 Laura hurricanes, and astronomical tide-only runs for the corresponding time periods are located in the "NameYYYY_besttrack+tides.tar" archive files. Both the maximum water surface elevations "maxele.63.nc" and the time series of water surface elevations "fort.63.nc" are included. </li> <li>ADCIRC input mesh (*.14) and mesh property files (*.13) are included in "ADCIRC_mesh_files.zip".</li> <li>Joint Karhunen-Loeve Polynomial Chaos (KL-PC) analysis python scripts with and without considering inundation are located in "klpc_analysis_scripts.zip". Requires <a href="https://github.com/noaa-ocs-modeling/EnsemblePerturbation">EnsemblePerturbation</a> python toolbox. </li> <li>Python scripts for analyzing and plotting the KL-PC results (Figures 6-14 and Table 1 in the manuscript) are located in "results_plotting_scripts.zip". Requires <a href="https://github.com/noaa-ocs-modeling/EnsemblePerturbation">EnsemblePerturbation</a> python toolbox. </li> </ol>
GRIP Hurricane and Tropical Storm Forecasts V1
The GRIP Hurricane and Tropical Storm Forecasts dataset consists of tropical cyclone model forecast tracks archived during the NASA Genesis and Rapid Intensification Processes (GRIP) field campaign. GRIP was one of three hurricane field campaigns conducted during the 2010 Atlantic/Pacific hurricane season. This tri-agency effort included NASA GRIP, the NSF Pre-Depression Investigation of Cloud-systems in the Tropics (PREDICT) and the NOAA Intensity Forecasting Experiment 2010 (IFEX10). The hurricane and tropical storm forecasts data files are available from August 12 through November 14, 2010 in ASCII text format with browse files in KML format, viewable in Google Earth. The ASCII text files contain 5-day model “consensus” forecasts and the KML browse files contain model forecasts ranging from 5-days to 10-days.
Hurricane and Severe Storm Sentinel (HS3) Naval Research Laboratory (NRL) Tropics Satellite Data V1
The Hurricane and Severe Storm Sentinel (HS3) Naval Research Laboratory (NRL) Tropics Satellite Data contains browse only data files, including brightness temperature, rain rate, and Red, Green, Blue (RGB) composite imagery, for the Hurricane and Severe Storm Sentinel (HS3) field campaign. Goals for the HS3 field campaign included assessing the relative roles of large-scale environmental and storm-scale internal processes, addressing the controversial role of the Saharan Air Layer (SAL) in tropical storm formation and intensification, and the role of deep convection in the inner-core region of storms. These browse only data files are available for dates between April 22, 2013 and September 30, 2014 in JPG format.
STORM tropical cyclone wind speed return periods as global GeoTIFFs
<p>Global tropical cyclone wind speed return period maps.</p> <p>This dataset is derived with minimal processing from the following datasets created by Bloemendaal et al, which are released with a CC0 license:</p> <p>[1] Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2023): STORM tropical cyclone wind speed return periods. Version 4. 4TU.ResearchData. Dataset. <a href="https://doi.org/10.4121/12705164.v4">https://doi.org/10.4121/12705164.v4</a></p> <p>[2] Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan) et. al. (2023): STORM climate change tropical cyclone wind speed return periods. Version 4. 4TU.ResearchData. Dataset. <a href="https://doi.org/10.4121/14510817.v4">https://doi.org/10.4121/14510817.v4</a></p> <p>Datasets containing tropical cyclone maximum wind speed (in m/s) return periods, generated using the STORM datasets (see <a href="https://www.nature.com/articles/s41597-020-0381-2">https://www.nature.com/articles/s41597-020-0381-2</a>) and STORM climate change datasets (see <a href="https://figshare.com/s/397aff8631a7da2843fc">https://figshare.com/s/397aff8631a7da2843fc</a>). Return periods were empirically calculated using Weibull's plotting formula. The STORM_FIXED_RETURN_PERIOD dataset contains maximum wind speeds for a fixed set of return periods at 10 km resolution in every basin and for every climate model used here (see below).</p> <p>The GeoTIFFs provided in the datasets linked above have been mosaicked into single files with global extent for each climate model/return period using the following code: </p> <p><a href="https://github.com/nismod/open-gira/blob/88dc522dd267020b16927e1b51ba46c4df4da277/workflow/tropical-cyclone/STORM.smk">https://github.com/nismod/open-gira/blob/88dc522dd267020b16927e1b51ba46c4df4da277/workflow/tropical-cyclone/STORM.smk</a></p> <p>Files are named on the pattern: <code>STORM_FIXED_RETURN_PERIODS_{STORM_MODEL}_{STORM_RP}_YR_RP.tif</code></p> <p>STORM_MODEL is be one of constant, CMCC-CM2-VHR4, CNRM-CM6-1-HR, EC-Earth3P-HR or HadGEM3-GC31-HM. The "constant" files are for the present day, baseline climate scenario as explained in dataset [1]. The other files are for 2050, RCP8.5 under different models as explained in the paper linked from dataset [2].</p> <p>STORM_RP is one of 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000 or 10000.</p> <h3>Updates</h3> <p>2024-04-05: recreated GeoTIFFs to align precisely with source pixels, updated references to v4, uploaded GeoTIFFs within ZIP to meet Zenodo file limits.</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.