Find research datasets worth reusing

opencc-by-4.0Apr 2017View details →

Derived Data supporting "On the Seasonal Cycles of Tropical Cyclone Potential Intensity" (Gilford et al. 2017, JoC)

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: Daniel M. Gilford, Susan Solomon, and Kerry Emanuel, 2017: On the Seasonal Cycles of Tropical Cyclone Potential Intensity. J. Climate, 30, 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>.

opencc-by-4.0Jun 2020View details →

Historical Tropical Cyclone Along-track Potential Intensity (and Derived Quantities) for Six Ocean Basins from Reanalyses

Supporting derived data for Shields et al. (2020, GRL). 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. All files are netcdf format, organized by basin, with suffixes on data variables to indicate reanalysis: <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> When using this data, please include the citation: <blockquote> 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. </blockquote> Potential intensities calculated with the Bister and Emanuel (2002) algorithm (pcmin.m) by Kerry Emanuel (revised by Daniel Gilford, Gilford et al. 2019), available freely at: ftp://texmex.mit.edu/pub/emanuel/TCMAX 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!). Please direct any questions or comments to daniel[dot]gilford[at]rutgers[dot]edu.

opencc-by-4.0Jun 2023View details →

Input data to replicate "The social cost of tropical cyclones"

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>. 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>: <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> 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>.

opencc-by-4.0Oct 2023View details →

DeepSurge storm surge predictions for HighResMIP tropical cyclones

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. 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. <h4>Dataset format</h4> 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:  <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> There are 6 variables in each file: <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>

opencc-by-4.0Aug 2020View details →

The datasets used in the manuscript named "Dynamical Seasonal Prediction of Tropical Cyclone Activity Using a Global Ensemble Prediction System FGOALS-f2 V1.0"

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"

opencc-by-4.0Nov 2021View details →

Supplemental Data for "Eyewall Asymmetries and Their Contributions to the Intensification of an Idealized Tropical Cyclone Translating in Uniform Flow"

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. 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/.

opencc-by-4.0Nov 2021View details →

United States tornado reports in landfalling tropical cyclones used in Paredes et al. (2021)

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. 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.

opencc-by-4.0Dec 2022View details →

Tropical cyclone and equatorial wave data in ERA5 (1980-2018)

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. See the Methods section in the article for details. Note: the TC data are produced by Kevin Hodges (Reading University) using the TRACK method (Hodges and Emerton, 2015; Hodges et al., 2017); the wave data are produced by Gui-Ying Yang (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>.   TC track data (ERA5 TC-matched.zip) 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*10-5 s-1) 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. 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.   Wave data (qvr_coefficient_YYYY_k3-40_p2-10_28plev_era5.nc) 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 β-plane. Six‐hourly horizontal winds and geopotential height in ERA5 are used here. This method identifies horizontal wind (u, v) and geopotential height (Z) structures associated with distinct equatorial waves. Potential equatorial waves are identified by projecting u, v and Z 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 y0=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. 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.    Reference  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. Monthly Weather Review, 143(12), 5091-5114. Hodges, K., Cobb, A., & Vidale, P. L. (2017). How well are tropical cyclones represented in reanalysis datasets?. Journal of Climate, 30(14), 5243-5264. Yang, G. Y., Hoskins, B., & Slingo, J. (2003). Convectively coupled equatorial waves: A new methodology for identifying wave structures in observational data. Journal of the atmospheric sciences, 60(14), 1637-1654. Feng, X., Yang, G.Y., Hodges, K., & Methven, J. (2023). Equatorial waves as useful precursors to tropical cyclone occurrence and intensification. Nature Communications.

opencc-by-4.0Mar 2023View details →

Wind and Waves in Tropical Cyclones (1985-2022)

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). 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). 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). 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.  Files given in NetCDF and MAT formats contain -  TC coordinates, heading velocity and direction, maximum wind speed and radius of maximum wind speed every 3 hours -  parameters of wind radial distributions for TC central and far zone every 3 hours -  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   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 .

opencc-by-4.0Jun 2023View details →

Extra-tropical cyclone statistics from OpenIFS aquaplanet simulations

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. 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.

opencc-by-4.0Jul 2023View details →

Recent Increases in Tropical Cyclone Rapid Intensification Events in Global Offshore Regions

The data and scripts in this repository can be used to support the main conclusion in the manuscript "Recent Increases in Tropical Cyclone Rapid Intensification Events in Global Offshore Regions" by Li et al., submitted to Nature Communications. The global distribution and annual variability of rapid intensification (RI) events of tropical cyclones (TCs) dervied from the open-source International Best Track Archive for Climate Stewardship (IBTrACS, https://www.ncei.noaa.gov/products/international-best-track-archive) are provided. The enviromental variables, including mid-level (600 hPa), vertical wind shear (200-850 hPa), and maximum potential intensity (MPI), were also calculated using the fifth generation of ECMWF reanalysis (ERA5) and Coupled Model Intercomparison Project Phase 6 (CMIP6) forced in different scenarios. The python script (coastal_RI_submit ipynb) can be used to reproduce figures in the article.

opencc-by-4.0Jul 2023View details →

Data associated with "Lightning and radar characteristics of tornadic cells in landfalling tropical cyclones"

These data include all tropical cyclone tornado reports from 2013–2020, as part of all data from 1995–2022, included in the Storm Prediction Center (SPC) Tropical Cyclone TORnado database (TCTOR; Edwards and Mosier, 2022) used in the following publication: Schenkel, B., K. Calhoun, T. Sandmael, M. Ake, Z. Fruits, B. Kassel, and I. Schick, 2023: Lightning and radar characteristics of tornadic cells in landfalling tropical cyclones. J. Geophys. Res.: Atmospheres, accepted. Each specific tropical cyclone tornado record has been extracted from the broader SPC tornado database, for all Atlantic and Gulf of Mexico tropical cyclones impacting the continental United States from 1995–2022. 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. These data will not be updated regularly. Citation for SPC TCTOR dataset: Edwards, R., & Mosier, R. M. (2022). Over a quarter century of TCTOR: Tropical cyclone tornadoes in the WSR-88D era [Dataset]. In Proc., 30th conf. on severe local storms (p. 171). Santa Fe, NM.

edi44/100

High-frequency water temperature and dissolved oxygen data and derived stability and metabolism metrics for nine lakes in northeastern North America for months before and after Tropical Cyclone Irene, Fall 2011

This dataset is used in the analysis published in the following manuscript: Klug, J.L., D.C. Richardson, H.A. Ewing, B.R.Hargreaves, N. R. Samal, D. Vachon, D.C. Pierson, A. E. Lindsey, D. O'Donnell, S.W. Effler, and K.C. Weathers. 2012. Ecosystem effects of a tropical cyclone on a network of lakes in northeastern North America. Environmental Science and Technology 46(21): 11693–11701. We include Quality Assurance Quality Controlled (QAQC) high-frequency dissolved oxygen, wind speed, and water temperature data from nine lakes and reservoirs in northeastern North America which were near the track of Tropical Cyclone Irene in August 2011. These data were collected using a set of in situ, automated monitoring systems associated with the Global Lake Ecological Observatory Network (GLEON) that record data at high frequency (10 min to 6 h). These sensor data were the basis for the derived measures of Schmidt stability, net ecosystem production, respiration, and gross primary production included in the dataset. We also include daily rainfall data collected at on-site or nearby weather stations. All data cover the period from 01 August through 15 October 2011.

openCC (other)Aug 2019View details →

openNov 2023View details →

A Global Multi-Source Tropical Cyclone Precipitation (MSTCP) Dataset

Tropical cyclone precipitation (TCP) is a key diagnostic in the context of atmospheric science, hazard, risk and flood research. This dataset provides estimates of TCP from global datasets. The various TCP metrics reported were estimated through the analysis of the global Multi-Source Weighted-Ensemble Precipitation (MSWEP) precipitation product and the International Best Track Archive for Climate Stewardship (IBTrACS) version 4. There are two main files that comprise the dataset. The main dataset file includes information on the mean and maximum TCP found within 500 km of each storm centre as well as the rainfall area and radius of maximum rain. The second file includes the estimates of azimuthally averaged precipitation using a 10 km bin spacing which is useful for analyses of the storm-scale structure of precipitation.

opencc-by-4.0Nov 2023View details →

Tropical cyclone low-level wind speed, shear, and veer: sensitivity to the boundary layer parameterization in WRF

This repository contains namelists needed to reproduce the WRF(V4.4) simulations analyzed in "Tropical cyclone low-level wind speed, shear, and veer: sensitivity to the boundary layer parameterization in WRF"

dryad40/100

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

Extreme weather events perturb ecosystems and increasingly threaten biodiversity1. Ecologists emphasize the need to forecast and mitigate the impacts of these incidents, which requires knowledge of how risk is distributed among species and environments, but the scale and unpredictability of extreme events complicates assessment1–4. These challenges are compounded for large animals ('megafauna'), which play crucial ecological roles but are hard to study5. Traits such as body size, dispersal ability, and habitat affiliation are among the hypothesized determinants of animals' vulnerability to natural hazards1,6,7. However, it has rarely been possible to test these propositions or, more generally, to link short- and longer-term effects of weather-related disturbance8,9. Here, we show how large herbivores and carnivores in Mozambique responded to Intense Tropical Cyclone Idai, the deadliest storm on record in Africa, across scales ranging from individual decisions in the hours after landfall to community-level responses nearly 20 months later. Animals occupying low-elevation habitats exhibited strong spatial responses to rising floodwaters. Body size predicted species' subsequent numerical responses: small-bodied species exhibited the greatest population declines. We trace this sensitivity to limited mobility, which increased likelihood of death during the flood and constrained animals' capacity to withstand food shortages afterward. Our results identify potentially general trait-based mechanisms underlying animal responses to severe weather and may help to inform strategies for wildlife conservation in a volatile climate. <ol> <li>Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Pörtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, B. Rama (eds.)]. Cambridge University Press. Cambridge University Press, Cambridge, UK and New York, NY, USA, (2022).</li> <li>Smith, M. An ecological perspective on extreme climatic events: A synthetic definition and framework to guide future research. J. Ecol. 99, 656-663 (2011).</li> <li>Ummenhofer, C. C., & Meehl, G. A. Extreme weather and climate events with ecological relevance: a review, Phil. Trans. R. Soc. B. 372, 20160135 (2017).</li> <li>Jentsch, A., Kreyling, J., & Beierkuhnlein, C. A new generation of climate-change experiments: events, not trends. Front. Ecol. Environ. 5, 365-374 (2007).</li> <li>Pringle, R. M., et. al. Impacts of large herbivores on terrestrial ecosystems. Current Biology 33, R584-R610 (2023).</li> <li>Spiller, D. A., Losos, J. B., & Schoener, T. W. Impact of a catastrophic hurricane on island populations. Science 281, 695-697 (1998). </li> <li>Schoener, T. W., & Spiller, D. A. Nonsynchronous recovery of community characteristics in island spiders after a catastrophic hurricane. PNAS 103, 2220-2225 (2006).</li> <li>Pruitt, N., Little, A. G., Majumdar, S. J., Schoener, T. W., & Fisher, D. N. Call-to-Action: A global consortium for tropical cyclone ecology. TREE 34, 588-590 (2019).</li> <li>Lin, T. C., Hogan, J. A., & Chang, C. T. Tropical cyclone ecology: a scale-link perspective. TREE 35, 594-604 (2020).</li> </ol>

opencc-zeroDec 2022View details →

opencc-by-4.0Dec 2023View details →

North Atlantic synthetic tropical cyclone track, intensity, and rainfall dataset from RAFT

The Risk Analysis Framework for Tropical Cyclones (RAFT)'s comprehensive and unified simulation of 40,000 synthetic North Atlantic tropical cyclone (TC) events are presented in this dataset. RAFT meticulously models these events based on large-scale environmental conditions, providing a valuable tool for in-depth TC impact analysis. The dataset encompasses detailed 6-hourly track information, along-track intensity metrics (including maximum wind speed and minimum pressure), the radius of maximum winds, and cumulative precipitation for each event. The primary dataset is encapsulated in a NetCDF4 file, "<a href="../records/10392725/files/RAFT.NA.v20231016.nc?download=1">RAFT.NA.v20231016.nc</a>", which contains a complete array of variables pertinent to the 40,000 synthetic TCs. These variables, detailed in Table 1 of the accompanying paper and summarized below, offer a comprehensive view of each TC event: <ul> <li>Basin ID: Identifies the basin (1 for North Atlantic)</li> <li>Storm ID: Unique identification number for each TC, starting from 0</li> <li>Year: Year of the environmental conditions used for modeling</li> <li>Jday: Julian day of the year, ranging from 0 to 365</li> <li>Longitude (lon): Geographical longitude in degrees</li> <li>Latitude (lat): Geographical latitude in degrees</li> <li>Maximum Wind Speed (vmax): Measured in knots</li> <li>Minimum Pressure (mslp): Measured in hectopascals (hPa)</li> <li>Radius of Maximum Wind (rmax): Measured in nautical miles (nmi)</li> </ul> Additionally, the dataset offers individualized accumulated rainfall data for each TC event, stored in NetCDF4 files named according to the convention "modeled_rainfall_ERA5_syn_{i}.h5", where "{i}" is the synthetic storm's ID. "ERA5" signifies the reanalysis input source, and "syn" indicates a synthetic track. This component of the dataset includes the following variables, all measured in total millimeters of precipitation: <ul> <li>Total Accumulated Rainfall (p_accum)</li> <li>Frictional Precipitation Component (p_accum_f)</li> <li>Topographic Precipitation Component (p_accum_h)</li> <li>Shear-related Precipitation Component (p_accum_s)</li> <li>Vortex Stretching Precipitation Component (p_accum_t)</li> </ul> The rainfall dataset is curated to focus on TC events within 600 km of the U.S. coast, reducing the number of rainfall events to 17,010 from the original 40,000, thereby enhancing its relevance and manageability. For user convenience, these events are compressed into grouped archives named "RAFT_accum_rainfall_{index}.tar.gz", where each "{index}" represents the index of the zipfile, containing up to 2,000 files for efficient data retrieval. The accumulated rainfall data is provided on a regular spatial grid, detailed in "<a href="../records/10392725/files/RAFT_rainfall_latlon_grid.h5?download=1">RAFT_rainfall_latlon_grid.h5</a>", which outlines the grid coordinates ('lat' and 'lon'). For comprehensive usage guidelines and further insights into this dataset, users are encouraged to refer to the associated paper. This dataset is not only a significant resource for researchers and analysts in the field of meteorology but also serves as a pivotal tool for understanding and predicting the impacts of tropical cyclones.   How to cite: Xu, W., Balaguru, K., Judi, D.R. et al. A North Atlantic synthetic tropical cyclone track, intensity, and rainfall dataset. Sci Data 11, 130 (2024). https://doi.org/10.1038/s41597-024-02952-7

opencc-by-4.0Dec 2021View details →

Contributions of Anomalous Large-Scale Circulations to the Absence of Tropical Cyclones over the Western North Pacific in July 2020

The datasets are for the article 'Contributions of Anomalous Large-Scale Circulations to the Absence of Tropical Cyclones over the Western North Pacific in July 2020', including the WRF Initial conditions (horizontal wind at 850 hPa, geopotential height at 850 and 200 hPa) of sensitivity experiments (CTRL, W_WNPSH, W_SAH, W_TUTT, and S_SASM) in July 2020, and the formation records of the simulated TC formation in all experiments.