Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
256
datasets available to search
ShareScore release 0.7.1
Dataset results
256 results for “Cyclone”
Projected Snow Cover Reductions and Mid-latitude Cyclone Responses in the North American Great Plains, 1986 - 2005
Extratropical cyclones are responsible for major weather events and trends in the mid-latitudes and preferentially develop in regions of enhanced cyclogenesis and proceed along climatological storm tracks. It has been shown that terrestrial snow cover exerts considerable influence on atmospheric baroclinicity which is largely responsible for the aforementioned cyclogeneses and storm tracks. Research about the effect which terrestrial snow cover exerts on cyclones' intensities, trajectories, and precipitation characteristics is limited but indicates a robust relationship with these factors. Many examinations of climate model projections have generally shown a poleward shift in storm tracks by the late 21st century though none have determined the degree to which the coincident poleward shift in snow extent is responsible. A method of imposing 10th, 50th, and 90th percentile values of snow retreat between the late 20th and 21st centuries as projected by 14 models of the Coupled Model Intercomparison Project Phase Five (CMIP5) is used to alter 20 historical cold season cyclones which tracked over or adjacent to the North American Great Plains. Simulations by the Advanced Research version of the Weather Research and Forecast Model (WRF-ARW) are initialized at 0 to 4 days prior to cyclogenesis. Including control and sensitivity testing wherein snow is unaltered or removed entirely, each cyclone case is simulated 25 times for a total of 500 simulations.
Mediterranean Cyclone tracks between 1979-2018 (40 years) from a high-resolution perspective using ECMWF ERA5 dataset
<p>The present dataset presents the trajectories of the 13,157 cyclones identified within the Mediterranean Region (MR) between 1979 and 2018 (40 years). These cyclone tracks were obtained using the new Cyclone Detection and Tracking Method (CDTM) described in Aragão e Porcù (2021) to take advantage of the recent availability of a high-resolution reanalysis dataset of ECMWF ERA5. The CDTM uses hourly data of Geopotential Height at 1000 hPa with a spatial resolution of 0.25°x0.25°, and the analysis' domain covers the area within 15°W to 48° E and 21° N to 54°N. Additionally, trying to eliminate artificial low-pressure cores, short-living thermal-lows or too weak cyclones as much as possible, the present study only considered cyclones lasting more than 24h.<br> The dataset presents hourly information for all cyclones from the cyclogenesis time to the cyclolysis time. Each record presents: [1] Cyclone ID (integer, 8 digits), [2] Cyclone centre longitude position (°E, real, 8 digits, 3 decimal digits), [3] Cyclone centre latitude position (°N, real, 8 digits, 3 decimal digits), [4] Year (integer, 4 digits), [5] Month (integer, 2 digits), [6] Day (integer, 2 digits), [7] Hour (integer, 2 digits), [9] Cyclone centre Geopotential Height at 1000 hPa (m, real, 9 digits, 3 decimal digits).<br> The analyses presented in Aragão e Porcù (2021) revealed that the proposed CDTM is capable to capture almost the totality of the observed cyclones, as well as describing its respective area of cyclogenesis, trajectories, and durations. More than an adaptation to a high-resolution dataset, the method brings as its primary contribution a suitable set of parameters to systematically identify and track the cyclonic activities in the Mediterranean, where cyclones do not have sizeable horizontal pressure gradients and present a shorter lifetime compared to open-ocean cyclones.</p> <p>Cite this article</p> <p>Aragão, L., Porcù, F. Cyclonic activity in the Mediterranean region from a high-resolution perspective using ECMWF ERA5 dataset. <em>Clim Dyn</em> (2021). https://doi.org/10.1007/s00382-021-05963-x</p>
Dataset - Generating reliable estimates of tropical cyclone induced coastal hazards along the Bay of Bengal for current and future climates using synthetic tracks
<p>This data is complementary to the paper by Leijnse et al. 2022 "Generating reliable estimates of tropical cyclone induced coastal hazards along the Bay of Bengal for current and future climates using synthetic tracks" <br> https://doi.org/10.5194/nhess-2021-181</p> <p>This data is made available in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE</p> <p>For questions about the data ask: tim.leijnse@deltares.nl</p> <p>For more information about the tool to generate the used synthetic tracks TCWiSE see: <a href="https://www.deltares.nl/en/software/tcwise/">https://www.deltares.nl/en/software/tcwise/</a></p> <p> </p>
Derived Data supporting "On the Seasonal Cycles of Tropical Cyclone Potential Intensity" (Gilford et al. 2017, JoC)
<p>Derived monthly mean tropical cyclone potential intensities (and associated variables) using the Bister and Emanuel 2002 PI algorithm, ftp://texmex.mit.edu/pub/emanuel/TCMAX; from MERRA2 (averaged over 1980-2016) and ERA-I data (averaged over 1980-2013), on 2.5x2.5 degree grids and with the ERA-I land-sea mask already applied. This data supported the publication of Gilford et al. (2017, JoC). When using this data, please include the citation:</p> <p>Daniel M. Gilford, Susan Solomon, and Kerry Emanuel, 2017: On the Seasonal Cycles of Tropical Cyclone Potential Intensity. <em>J. Climate, </em><strong>30</strong>, 6085–6096. doi: <a href="http://journals.ametsoc.org/doi/10.1175/JCLI-D-16-0827.1">10.1175/JCLI-D-16-0827.1</a>.</p> <p> </p>
Historical Tropical Cyclone Along-track Potential Intensity (and Derived Quantities) for Six Ocean Basins from Reanalyses
<p>Supporting derived data for Shields et al. (2020, GRL).</p> <p>Derived tropical cyclone potential intensities and associated variables across the North Atlantic (NA), Eastern North Pacific (EP), North Indian (NI), South Indian (SI), South Pacific (SP), and Western North Pacific (WP) ocean basins, from MERRA2, ERA-I, and MERRA2 with SSTs replaced by HadISSTs. NA/WP basins also have potential and observed intensities calculated with NCEP/NCAR and ERA-20C reanalyses over 1950-2016 and 1950-2010, respectively.</p> <p>All files are netcdf format, organized by basin, with suffixes on data variables to indicate reanalysis:</p> <ul> <li>"_m": MERRA2 (Gelaro et al. 2017)</li> <li>"_h": MERRA2-HadISSTs (Rayner et al. 2003)</li> <li>"_e": ERA-I (Dee et al. 2011)</li> <li>"_n": NCEP/NCAR (Kalnay et al. 2016)</li> <li>"_c": ERA-20C (Stickler et al. 2014)</li> </ul> <p>When using this data, please include the citation:</p> <blockquote> <p><strong>Shannon Shields, Allison Wing, and Daniel M. Gilford, 2020: A Global Analysis of Interannual Variability of Potential and Actual Tropical Cyclone Intensities. Geophys. Res. Lett.</strong></p> </blockquote> <p>Potential intensities calculated with the Bister and Emanuel (2002) algorithm (<strong>pcmin.m</strong>) by Kerry Emanuel (revised by Daniel Gilford, Gilford et al. 2019), available freely at: ftp://texmex.mit.edu/pub/emanuel/TCMAX</p> <p>MERRA2, ERA-I, and MERRA2 with SSTs replaced by HadISSTs calculations were performed by Daniel Gilford; NCEP/NCAR and ERA-20C calculations were performed by Dr. Suzana Camargo (many thanks!).</p> <p>Please direct any questions or comments to daniel[dot]gilford[at]rutgers[dot]edu.</p>
Impact Areas and Dynamical Features associated with Mediterranean Cyclones (1980-2019)
<p>The dataset includes NetCDF files of Impact Areas and Dynamical Features associated with Mediterranean Cyclones (henceforth MedCyclones).</p> <p>MedCyclone tracks correspond to confidence-level 5 tracks from Flaounas et al. (2023), <a href="https://doi.org/10.5194/wcd-4-639-2023">https://doi.org/10.5194/wcd-4-639-2023</a>.</p> <p>Temporal frequency: 6h (00, 06, 12, 18 UTC)<br>Years: 1980 – 2019<br>Spatial resolution: 0.5 deg<br>Grid extension: 0-70N, 40W-65E</p> <p> </p> <h2>Dynamical Features</h2> <p>Files "dynfeats_bool_rmax2000_YYYY.nc" include the following list of variables, describing connected boolean objects:</p> <ul> <li><strong>r_500</strong>, <strong>r_1000</strong>: central areas of fixed 500 or 1000 km radius around MedCyclone centres;</li> <li><strong>WCB</strong>: warm conveyor belts related to MedCyclones (i.e., overlapping with r_500 in at least one grid point). Each <strong>WCB</strong> is eventually separated into inflow (<strong>WCBin</strong>, up to 800 hPa) and ascent (<strong>WCBout</strong>, between 800 and 400 hPa) regions. Ref. at <a href="https://doi.org/10.1175/JCLI-D-12-00720.1">https://doi.org/10.1175/JCLI-D-12-00720.1</a>, <a href="https://doi.org/10.5194/wcd-5-537-2024">https://doi.org/10.5194/wcd-5-537-2024</a>;</li> <li><strong>fronts</strong>: cold fronts related to MedCyclones (i.e., overlapping with r_500 in at least one grid point). Ref. at <a href="https://doi.org/10.5194/gmd-17-6137-2024">https://doi.org/10.5194/gmd-17-6137-2024</a>;</li> <li><strong>DI</strong>: dry instrusions related to MedCyclones (i.e., overlapping with r_1000 in at least one grid point). Ref. at <a href="https://doi.org/10.1175/JCLI-D-16-0782.1">https://doi.org/10.1175/JCLI-D-16-0782.1</a>;</li> <li><strong>r_1000_Nodynfeat</strong>: the central 1000 km area excluding regions of MedCyclone WCB, fronts and DI objects.</li> </ul> <p>The criteria for the identification of WCB, fronts and DI objects are described in Section 2.3 of Portal et al. (2024), <a href="https://doi.org/10.5194/wcd-5-1043-2024">https://doi.org/10.5194/wcd-5-1043-2024</a>.</p> <p>Additionally, we note that :<br>i. a weaker overlap constraint was used to associate DI objects to MedCyclones (r_1000 compared to r_500 for WCB and fronts objects) because of the relatively large distance of the DI airstream from the cyclone centre;<br>ii. in this dataset, all connected objects related to MedCyclones are cropped within a 2000 km area circle from the cyclone centre for two reasons. Firstly, the dynamical-feature related surface impacts usually weaken with the distance from the cyclone centre. Secondly, to cut connected objects composed by multiple overlapping features of the same kind - this often happens for fronts in summer because of their high detection density. Far from the cyclone centre, these objects are usually unrelated with the MedCyclone circulation.</p> <p> </p> <h2>Impact Areas</h2> <p>Files "IAs_bool_rmax2000_YYYY.nc" include boolean impact areas, combining a central area (r_1000 or r_500) and cyclone-related WCB, CF and DI objects. The three types of impact area are described in the following :</p> <ol> <li><strong>IA01</strong> is composed by a 1000 km radius circle around the cyclone centre (r_1000) extended by cyclone-related WCB, fronts and DI;</li> <li><strong>IA02</strong> is composed by a 500 km radius circle around the cyclone centre (r_500) extended by cyclone-related WCB, fronts and DI</li> <li><strong>IA03</strong> is composed by a 500 km radius circle around the cyclone centre (r_500) extended by cyclone-related WCB and fronts (DI is neglected).</li> </ol> <p>As discussed in Section 3.1 and Appendix A of Portal et al. (2024) (<a href="https://doi.org/10.5194/wcd-5-1043-2024">https://doi.org/10.5194/wcd-5-1043-2024</a>), IA01, composed by a central area of 1000 km, is adequate for intercepting long-range wind impacts associated with MedCyclones. IA02 and IA03, on the contrary, are better devised for detecting impacts expected at shorter distances from the cyclone centre, such as rainfall, thunderstorm and storm surges. In particular, IA03 neglects the DI region, which is normally of little interest for cyclone-related moist processes, involved in producing precipitation. Noetheless, DI remains relevant for the identification of strong cyclone-related winds.</p> <p> </p> <h3>Case Studies</h3> <p>A pdf file providing the visualisation and description of impact areas and dynamical features of all MedCyclones occurring in 1980 is available at the link <a href="https://boris.unibe.ch/192315/">https://boris.unibe.ch/192315/</a>. Note that in the examples the dynamical features are not cropped at 2000 km from the cyclone centres, as for the present dataset. </p> <p>Note that many of the "Annotations and Limitations" listed below derive from the attentive analysis of these study cases.</p> <h3>Annotations and limitations</h3> <ul> <li>In the case of more than one MedCyclone centre per timestep, the dasaset does not distinguish the impact areas / dynamical features associated with each centre.</li> <li>Because of the automated criteria for associating WCB, fronts and DI objects to MedCyclones, at times objects close to the centre but unrelated to the MedCyclone's circulation, are considered to be cyclone-related and included in the impact area.</li> <li>Elaborating on the point above, at times fronts responsible for Mediterranean cyclogenesis (and not produced by the cyclonic circulation itself) are included in the MedCyclone impact area.</li> <li>When computing statistics over a long time interval (e.g., climatology), the effects of erroneous associations of dynamical features to MedCyclone impact areas are attenuated by the aggregation of large quantity of data. </li> <li>Over a long time interval (e.g., climatology) the choice of a 1000 km fixed-radius impact area provides similar statistics to IA01, although in the first case it is not possible to isolate the role played by the different features composing the MedCyclones.</li> </ul>
Input data to replicate "The social cost of tropical cyclones"
<p>Input data for the <a href="https://dx.doi.org/10.5281/zenodo.8056520">scripts</a> that replicate the results of <a href="https://doi.org/10.1038/s41467-023-43114-4">Krichene et al. 2023</a>.</p> <p>To run the <a href="https://dx.doi.org/10.5281/zenodo.8056520">scripts</a>, the following files from this repository need to be placed in the <code>./data/input/</code> subdirectory of the project folder containing the <a href="https://dx.doi.org/10.5281/zenodo.8056520">scripts</a>:</p> <ul> <li><code>GMT.nc</code>: Global mean temperature time series as used by the <a href="https://gitlab.pik-potsdam.de/tovogt/tc_emulator">tropical cyclone emulator</a>.</li> <li><code>GrowthClimateDataset.dta</code>: The <a href="https://purl.stanford.edu/wb587wt4560">input data</a> of <a href="https://dx.doi.org/10.1038/nature15725">Burke et al. 2015</a>.</li> <li><code>IHME_GLOBAL_GDP_ESTIMATES_1950_2015.csv</code>: Historical GDP per capita data from <a href="https://doi.org/10.1186/1478-7954-10-12">James et al. 2012</a> (downloaded from <a href="https://ghdx.healthdata.org/record/ihme-data/gross-domestic-product-gdp-estimates-country-1950-2015">IHME</a>).</li> <li><code>mean_temperature_gswp3-w5e5.csv</code>: Population-weighted average national temperature time series for the historical period.</li> <li><code>pulse_response_ricke_caldeira_2014.csv</code>: The global mean temperature response of an additional emission pulse according to <a href="https://dx.doi.org/10.1088/1748-9326/9/12/124002">Ricke & Caldeira 2014</a>.</li> <li><code>tcdata/TCE-DAT_historic-exposure_1950-2015.csv</code> and <code>tcdata/TotalPopulation.csv</code>: Historical (national) numbers of people affected by tropical cyclones according to <a href="https://doi.org/10.5880/pik.2017.011">TCE-DAT</a> with the corresponding total population counts.</li> <li><code>tcdata/emulator/</code>: Projected (national) shares of people affected by tropical cyclones according to the <a href="https://gitlab.pik-potsdam.de/tc_cost/tc_emulator">tropical cyclone emulator</a> as computed by the scripts in the <a href="https://gitlab.pik-potsdam.de/tc_cost/tc_people_affected">corresponding repository</a>.</li> <li><code>wid_all_data.zip</code>: A bulk data set from the <a href="https://wid.world/bulk_download/wid_all_data.zip">World Inequality Database</a>.</li> </ul> <p>For more information, see <a href="https://doi.org/10.1038/s41467-023-43114-4">Krichene et al. 2023</a> and the README file provided with the <a href="https://dx.doi.org/10.5281/zenodo.8056520">scripts</a>.</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>
The datasets used in the manuscript named "Dynamical Seasonal Prediction of Tropical Cyclone Activity Using a Global Ensemble Prediction System FGOALS-f2 V1.0"
<p>The hindcast and real-time prediction output of FGOALS-f2 V1.0 used in the study named "Dynamical Seasonal Prediction of Tropical Cyclone Activity Using a Global Ensemble Prediction System FGOALS-f2 V1.0"</p>
Supplemental Data for "Eyewall Asymmetries and Their Contributions to the Intensification of an Idealized Tropical Cyclone Translating in Uniform Flow"
<p>The repository contains a set of files required to reproduce the idealized tropical cyclone simulation analyzed in the manuscript entitled "Eyewall asymmetries and their contributions to the intensification of an idealized tropical cyclone translating in uniform flow", submitted to the Journal of the Atmospheric Sciences. See the README file for brief descriptions about the content of each file within this repository.</p> <p>The simulation was produced with the Cloud Model 1 (CM1) version 19.7, and CM1 can be downloaded at https://www2.mmm.ucar.edu/people/bryan/cm1/. </p>
United States tornado reports in landfalling tropical cyclones used in Paredes et al. (2021)
<p>These data include all tropical cyclone tornado reports used in Paredes et al. (2021) plus an additional year (e.g., 2020). These data will not be updated regularly. For the latest version, users should refer to https://www.spc.noaa.gov/misc/edwards/TCTOR/ or contact roger.edwards@noaa.gov.<br> <br> Each specific tropical cyclone tornado record has been extracted from the broader Storm Prediction Center tornado database, for all Atlantic and Gulf of Mexico tropical cyclones to affect the continental United States from 1995–2020. The tornado records were analyzed individually to determine their presence within the circulation envelope of either a classified or remnant tropical cyclone, without regard to fixed radii from tropical cyclone center, inland extent, temporal cutoffs before or after landfall, or other such arbitrary thresholds that may either exclude tropical cyclone events or include non-tropical cyclone tornadoes unnecessarily. Unlike other climatologies previously published in the literature, the chosen time period for this examination essentially covers only the full national deployment of the WSR-88D radar network in the United States. This permits consistent comparisons of a very large sample size of tropical cyclone tornado events (>1600) during the era of modernized National Weather Service warning and verification practices.</p>
gyroScatterEFF data: 590k element cyclone mesh
<p>Arrays and constants needed to run the gyroScatterEFF kernel on a single GPU without XGCm.<br> <br> Created with https://github.com/SCOREC/xgcm/tree/cws/extractGyroScatter @ <a href="https://github.com/SCOREC/xgcm/commit/5a8f0b47262c2466085e8fca983add502eef64b8">5a8f0b4</a>. <br> <br> The build on OLCF Summit used the following scripts: https://github.com/zhangchonglin/XGCm_build_scripts/tree/792c9378b6a12ca141811cbd788c6d6542e59938/Summit_gcc11.2.0_cuda11.5.2<br> <br> And the Cyclone 590k mesh case here: https://github.com/SCOREC/xgc1_data/tree/0d32f7f1354346a37df1fac517fa1f4fceff2015/Cyclone_ITG/Cyclone_ITG_deltaf_590kmesh <br> <br> At the time of dataset publication, the XGCm and xgc1_data repos were private.</p>
Arctic Cyclones and Climate Change Cases A-C
<p>Weather Research and Forecasting Model (v3.9.1.1) set up namelists and simulation output data used to analyze the effect of climate change on spring Arctic Cyclone characteristics for cyclone cases A-C. This dataset is made available to accompany the Nature Communications paper "<strong>The Influence of Recent and Future Climate Change on Spring Arctic Cyclones"</strong> by Parker et al. 2022 <a href="https://doi.org/10.1038/s41467-022-34126-7">https://doi.org/10.1038/s41467-022-34126-7</a> . See associated Zenodo datasets for other Cyclone cases in this work: 10.5281/zenodo.7131287; 10.5281/zenodo.7126117.</p>
Arctic Cyclones and Climate Change Cases G-I
<p>Weather Research and Forecasting Model (v3.9.1.1) simulation output data used to analyze the effect of climate change on spring Arctic Cyclone characteristics for cyclone cases G-I. This dataset is made available to accompany the Nature Communications paper "<strong>The Influence of Recent and Future Climate Change on Spring Arctic Cyclones"</strong> by Parker et al. 2022 <a href="https://doi.org/10.1038/s41467-022-34126-7">https://doi.org/10.1038/s41467-022-34126-7</a>. See associated Zenodo datasets for other Cyclone cases in this work: 10.5281/zenodo.7131287; 10.5281/zenodo.7131284.</p>
Arctic Cyclones and Climate Change Cases D-F
<p>Weather Research and Forecasting Model (v3.9.1.1) simulation output data used to analyze the effect of climate change on spring Arctic Cyclone characteristics for cyclone cases D-F. This dataset is made available to accompany the Nature Communications paper "<strong>The Influence of Recent and Future Climate Change on Spring Arctic Cyclones"</strong> by Parker et al. 2022 <a href="https://doi.org/10.1038/s41467-022-34126-7">https://doi.org/10.1038/s41467-022-34126-7</a>. See associated Zenodo datasets for other Cyclone cases in this work: 10.5281/zenodo.7131284; 10.5281/zenodo.7126117</p>
Data set of detected atmospheric rivers, cyclones, and fronts within the region of 75°N – 82.5°N, 0°E – 30°E and at Ny-Ålesund (Svalbard) for 2017 – 2021
<p>This data set contains times when atmospheric rivers, cyclones, or fronts have been detected within the broader region of 75°N – 82.5°N, 0°E – 30°E and specifically at Ny-Ålesund, Svalbard (78.92308 °N, 11.92108 °E) for the years 2017 to 2021. To this end, the detection methods, as described in Lauer et al. (2023), have been applied to the hourly-resolved ERA5 reanalysis (Hersbach et al., 2020) data. </p> <p>Data set overview</p> <p>Each file contains the times (year, month, day, hour in UTC) when the corresponding weather system, i.e. atmospheric river, cyclone and front, has been detected within the region of 75°N – 82.5°N, 0°E – 30°E. The last column indicates if the weather system was located also over Ny-Ålesund Svalbard (78.92308 °N, 11.92108 °E). </p>
Tropical cyclone and equatorial wave data in ERA5 (1980-2018)
<p><strong>The dataset consists of global tropical cyclone (TC) track and equatorial wave data derived from ERA5 (1980-2018). The dataset was originally used to produce the paper "Equatorial waves as useful precursors to tropical cyclone occurrence and intensification" published in Nature Communications in 2023. </strong>See the Methods section in the article for details.</p> <p><strong>Note</strong>: the TC data are produced by <strong>Kevin Hodges</strong> (Reading University) using the TRACK method (Hodges and Emerton, 2015; Hodges et al., 2017); the wave data are produced by <strong>Gui-Ying Yang</strong> (Reading University) based on the method in Yang et al., 2003. ERA5 data is generated by ECMWF and distributed by the C3S CDS. ERA5 data used in this paper are archived from <a href="https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset">https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset</a>.</p> <p> </p> <p><strong>TC track data </strong>(ERA5 TC-matched.zip)</p> <p>TCs are identified and tracked by cyclonic vorticity centres in the ECMWF fifth generation climate reanalysis (ERA5) from six‐hourly atmospheric data, using the TRACK method (Hodges and Emerton, 2015; Hodges et al., 2017). The TRACK scheme used in this paper includes the following processes. First, the vertical average of the relative vorticity between 850 and 600 hPa is obtained. This is then spatially filtered using spherical harmonics to T63 resolution; the large‐scale background with total wavenumbers n ≤ 5 is removed. Vorticity maxima in the Northern Hemisphere and minima in the Southern Hemisphere are determined on the T63 grid and then used as starting points to obtain the off‐grid locations using B‐spline interpolation and maximisation methods. Then, in the first instance, all positive vorticity centres that exceed 0.5 Cyclonic Vorticity Unit (CVU, with 1.0 CVU=1.0*10<sup>-5</sup> s<sup>-1</sup>) in the range 0°–60°N (negative vorticity centres that are below -0.5 CVU in the range 0°–60°S) are identified through the data time series. The tracking is performed by first initialising a set of tracks using a nearest neighbour method and then refining them by minimising a cost function for track smoothness subject to adaptive constraints on track smoothness and displacement distance in a time step. After the tracking is complete, the full T63 vorticity maxima at levels from 850 hPa up to 200 hPa (850, 700, 600, 500, 400, 300 and 200 hPa) are added to the tracks using a recursive search within a 5° radius (geodesic) of the tracked centre. This is used to test for the existence of a coherent vertical structure and a warm core.</p> <p>A matching process is applied to match the ERA5 tracks against the observed tracks, the Best Track from IBTrACS. An ERA5 track is matched to an IBTrACS track if the mean spatial separation is ≤5° over the corresponding paired track points and it is the track with the smallest separation. This process ensures that the ERA5 tracks are those storms actually ‘observed’ in IBTrACS (but they may have different lifecycle). This means that the ERA5 storm tracks have an extended lifecycle consistent, allowing the analysis of the “pre-TC” features. ERA5 “pre-TCG” is the first point of the above identified TC track in ERA5, which is at an earlier stage than the genesis in IBTrACS (normally the first track point reaching the tropical storm intensity). On average, the ERA5 pre-TCG events are 4.6 days earlier in time, and 2.7 CVU weaker in relative vorticity of the vortex, than the TCG events at the observed TCG time.</p> <p> </p> <p><strong>Wave data (</strong>qvr_coefficient_YYYY_k3-40_p2-10_28plev_era5.nc<strong>)</strong></p> <p>In this dataset, dynamical equatorial waves are derived by projecting global wind and geopotential height data onto an orthogonal basis defined by the horizontal equatorial wave structures obtained from the theory of disturbances to a resting atmosphere on the equatorial <em>β</em>-plane. Six‐hourly horizontal winds and geopotential height in ERA5 are used here. This method identifies horizontal wind (<em>u, v</em>) and geopotential height (<em>Z</em>) structures associated with distinct equatorial waves. Potential equatorial waves are identified by projecting <em>u</em>, <em>v</em> and <em>Z</em> in the tropics (24°S–24°N) at each pressure level onto the different equatorial wave modes, using their sinusoidal structure in the zonal direction and parabolic cylinder functions in the meridional direction. These basis functions used for the wave projection are orthogonal, meaning that the wave structures here are orthogonal since they are pre-described as a series of the basis functions. In the parabolic cylinder functions, the meridional trapping scale is y<sub>0</sub>=6°. Before the projection, a broad-band spectral filter, with wavenumber 3 to 40 and period 2 to 10 days, is applied to separate eastward and westward moving waves.</p> <p>This ERA5 wave dataset contains three equatorial wave modes: westward-moving mixed Rossby-gravity (WMRG) and meridional mode number n=1 and 2 Rossby (R1 and R2) waves. The dataset spans 39 years from 1980 to 2018 covering all seasons, with a 6-hourly interval at 1° resolution on 28 pressure levels from 1000 to 70 hPa. </p> <p> </p> <p>Reference </p> <p>Hodges, K. I., & Emerton, R. (2015). The prediction of Northern Hemisphere tropical cyclone extended life cycles by the ECMWF ensemble and deterministic prediction systems. Part I: Tropical cyclone stage. <em>Monthly Weather Review</em>, 143(12), 5091-5114.</p> <p>Hodges, K., Cobb, A., & Vidale, P. L. (2017). How well are tropical cyclones represented in reanalysis datasets?. <em>Journal of Climate</em>, 30(14), 5243-5264.</p> <p>Yang, G. Y., Hoskins, B., & Slingo, J. (2003). Convectively coupled equatorial waves: A new methodology for identifying wave structures in observational data. <em>Journal of the atmospheric sciences</em>, 60(14), 1637-1654.</p> <p>Feng, X., Yang, G.Y., Hodges, K., & Methven, J. (2023). Equatorial waves as useful precursors to tropical cyclone occurrence and intensification. <em>Nature Communications</em>.</p>
Sprenger et al. (2017) cyclone data
<p>These data were generated by Sprenger et al., as detailed in their paper:</p> <p>Sprenger, M., Fragkoulidis, G., Binder, H., Croci-Maspoli, M., Graf, P., Grams, C. M., Knippertz, P., Madonna, E., Schemm, S., Škerlak, B., & Wernli, H. (2017). Global Climatologies of Eulerian and Lagrangian Flow Features based on ERA-Interim, Bulletin of the American Meteorological Society, 98(8), 1739-1748. https://doi.org/10.1175/BAMS-D-15-00299.1</p> <p>, and are being uploaded with their permission to comply with Geophysical Research Letter's FAIR data policy for a separate paper, submitted by Linh Vu and Robin Clancy (uploader).</p> <p>Fields included are: date latitude, longitude, thetamin (minumum potential temperature), amplitude, circulation. Radius data are not present in this file, and so only a fill value is present. Individual cyclones are split by a line that indicates how many 6-hourly records follow for that cyclone.</p>
Wind and Waves in Tropical Cyclones (1985-2022)
<p>Dataset contains information on wind speed and wave height in Tropical Cyclones (TCs) obtained from the Best Track Data during the period from 1985 to 2022 and along-track altimeter measurements from 17 satellites in 1985-2018 (IMOS archive, Ribal and Young, 2019) and in 2020-2022 (CMEMS archive).</p> <p>For each TC, in which the maximum wind speed exceeded 30 m/s (1905 cases), files are created to combine altimetry data on the significant wave height and wind speed in the cyclone area (+-7 degrees from TC center) and information on each cyclone trajectory and its main characteristics (maximum wind speed, radius of maximum winds, heading velocity vector).</p> <p>To describe the radial distribution of wind speed, standard data on distances from the cyclone center to points with wind speeds of 34, 50, and 64 knots are approximated with the analytical function suggested by Holland (1980).</p> <p>For each cyclone, graphical files are provided to illustrate the evolution of every TC parameters, the quality of the wind prifile approximation, the location of altimeter tracks, and the along-track values of significant wave height and wind speed. </p> <p>Files given in NetCDF and MAT formats contain</p> <p>- TC coordinates, heading velocity and direction, maximum wind speed and radius of maximum wind speed every 3 hours</p> <p>- parameters of wind radial distributions for TC central and far zone every 3 hours</p> <p>- altimetry data: time and along-track significant wave height and wind speed in TC region (+-7 degrees from TC center) from satellites GEOSAT, ERS-1, TOPEX, ERS-2, GFO, ENVISAT, JASON-1, JASON-2, JASON-3, SARAL/AltiKa, CryoSat-2, HY-2A, HY-2B, CFOSAT, Sentinel-3A, Sentinel-3B, Sentinel-6А</p> <p> </p> <p>To form the dataset, the NOAA archive with data on tropical cyclones (https://www.ncei.noaa.gov/data/international-best-track-archive-for-climate-stewardship-ibtracs/v04r00/access/netcdf/, DOI :10.48670/moi-00178) and archives CMEMS (https://resources.marine.copernicus.eu/products, DOI:10.48670/moi-00178) and IMOS (https://catalogue-imos. aodn.org.au/geonetwork/srv/rus/catalog.search#/metadata/c6d5c7b4-323e-4979-9364-cdfa59684163, DOI:10.26198/5c184f4a5cd2e) with altimetry data were used .</p> <p> </p>
Extra-tropical cyclone statistics from OpenIFS aquaplanet simulations
<p>This data set is derived from three idealised modelling experiments that were performed with the global numerical weather prediction model, OpenIFS. Three aqua-planet experiments were performed that differed only in their sea surface temperatures. There was a control simulation, case where the sea surface temperatures were warmed everywhere by 4K and a case where the polar sea surface temperatures were increased by 5K. In all three experiments, the extra-tropical cyclones were identified. The data presented in this data set is the maximum vorticity and precipitation of all of these extra-tropical cyclones. This data set is used by Sinclair and Catto (2023) in their study.</p> <p>Sinclair, V. A. and Catto, J. L.: The relationship between extra-tropical cyclone intensity and precipitation in idealised current and future climates, Weather Clim. Dynam. Discuss. [preprint], https://doi.org/10.5194/wcd-2022-62, in review, 2022.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.