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Dataset for the ``Fast atmospheric response to a cold oceanic mesoscale patch in the north-western tropical Atlantic" publication
<p>The dataset presented here contains the files needed to produce the results presented in the publication "Fast atmospheric response to a SST mesoscale cold patch in the north-western subtropical Atlantic" submitted to the <em>Journal of Geophysical Research: Atmospheres</em>. The scripts that read and produce these files are publicly available at <a href="https://github.com/ClauClouds/SST-impact/">https://github.com/ClauClouds/SST-impact/</a> and can also be found in this repository (code_python.zip). This Zenodo data repository includes the following datasets:</p> <ul> <li> <p>Radiosonde data from 2-3 February 2020 (Stephan et al., 2021)</p> </li> <li> <p>Doppler lidar, and ARTHUS Raman lidar variables data from 2-3 February 2020,</p> </li> <li> <p>GOES-East (Geostationary Operational Environmental Satellite - East) Binary Cloud Mask (BCM) and Cloud Optical Depth (COD) products, provided at 2 km grid spacing every 10 minutes. They come from the GOES-R Advanced Baseline Imager (ABI) (Schmit et al., 2017), available at <a href="https://www.ncei.noaa.gov/products/satellite/goes-r-series.Data">https://www.ncei.noaa.gov/products/satellite/goes-r-series.Data</a> and they are provided for the 2-3 February 2020.</p> </li> <li> <p>Multi-scale Ultra-high Resolution (MUR) product (JPL MUR MEaSUREs Project, 2015,183 (Chin et al., 2017)) averaged between the 2nd and 3rdfor the 2nd of February 2020. The MUR product is an analysis product provided on a daily basis that combines different satellite (infrared at high and medium resolutions and microwave products) and in-situ data (Chin et al., 2017).</p> </li> <li> <p>W-band radar data post-processed for the purposes of the publication. The original W-band radar data used are publicly accessible at <a href="https://howto.eurec4a.eu/merian_cloudradar.html">https://howto.eurec4a.eu/merian_cloudradar.html</a> and can be downloaded via <a href="https://eurec4a.aeris-data.fr/">AERIS data portal</a>. See more details and specific DOI below.</p> </li> </ul> <p>The present dataset is structured as follows:</p> <ul> <li> <p>diurnal_cycle_removed_vars: files containing the time series of the variables without noise and diurnal cycle (filenames with extended dates 20200202 and 20200203)</p> </li> <li> <p>diurnal_cycle: files containing the diurnal cycle of each variable used in the publication</p> </li> <li> <p>binned_sst_vars: files containing variables binned in terms of SST, used to derive the plots in the paper.</p> </li> <li> <p>satellite_data: a folder containing all satellite data used in the publication</p> </li> </ul> <p>Additional data used in the publication, that are processed via the scripts contained in the link mentioned above, are available online at the following urls:</p> <ul> <li> <p>cloud radar observations can be directly obtained from the public dataset identifiable via DOI: <a href="https://doi.org/10.25326/235">https://doi.org/10.25326/235</a> (Acquistapace et al., 2022)</p> </li> <li> <p>ASCAT wind field data and corresponding MUR SST data are available from the NASA JPL PODAAC platform (<a href="https://podaac.jpl.nasa.gov/">https://podaac.jpl.nasa.gov/</a>)</p> </li> <li> <p>hourly ERA5 (Hersbach et al., 2020) gridded fields (available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=form, last accessed March 2022) of the following variables: SST, water vapor mixing ratio, air temperature, and horizontal wind components. </p> </li> </ul> <p><br> </p> <p>References;</p> <p>Acquistapace et al., 2022, ESSD, <a href="https://doi.org/10.25326/235">https://doi.org/10.25326/235</a>.</p> <p>Schmit, T. et al., 2017, QJRMS, <a href="https://doi.org/10.1175/BAMS-D-15-00230.1">https://doi.org/10.1175/BAMS-D-15-00230.1</a></p> <p>Hersbach et al., 2020, QJRMS, <a href="https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3803">https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3803</a></p> <p>Stephan et al., 2021, ESSD, <a href="https://doi.org/10.5194/essd-13-491-2021">https://doi.org/10.5194/essd-13-491-2021</a></p> <p>Chin, T. M. et al., (2017), RS, <a href="https://doi.org/10.1016/j.rse.2017.07.029">https://doi.org/10.1016/j.rse.2017.07.029</a></p>
Flow-topography interactions in the western tropical Atlantic boundary off Northeast Brazil.
<p>Figures and other media files of the paper Flow-topography interactions in the western tropical Atlantic boundary 2off Northeast Brazil.</p>
Hydrogen peroxide in the upper tropical troposphere over the Atlantic Ocean and western Africa during the CAFE-Africa aircraft campaign
<p>We provide here the supporting dataset for our study on airborne measurements of oh hydrogen peroxide in the upper tropical troposphere over the Atlantic Ocean and western Africa during the CAFE-Africa aircraft campaign in 2018.</p> <p> </p>
Morphological cladogenesis and terminal dwarfing in extinct Late Miocene through Pliocene menardiform globorotalids: New complementary data to «Evolutionary prospection in the Neogene planktic foraminifer Globorotalia menardii and related forms from ODP Hole 925B (Céara Rise, western tropical Atlantic): evidence for gradual evolution superimposed by long distance dispersal ?, Swiss J. Palaeontology, 135:205-248»
<p>A complementary morphometric data set is provided to the study of Knappertsbusch (2016) about the shell evolution of menardiform globorotalids (Neogene planktic foraminifera) at ODP Hole 925B from Céara Rise in the the western tropical Atlantic. The new measurements confirm splitting of extinct <em>Globorotalia multicamerata</em> from the <em>G. menardii</em> stock via the intermediate form <em>G. limbata</em> between about 6 Ma to 5 Ma ago. After splitting both <em>G. limbata</em> and <em>G. multicamerata</em> show gradual divergence from <em>G. menardii</em> in several shell parameters illustrating morphological cladogenesis. Between 2.88 Ma and 2.59 Ma the same parameters show a concerted trend towards reduced values indicating pre-extinction dwarfing. A comparison with published literature data of Delta<sup>18</sup>O trends between species, that populated the mixed layer (<em>Globigerinoides sacculifer</em>) and the thermocline layer (<em>Neogloboquadrina dutertrei</em>) at this location during those times suggests, that both divergence and subsequent dwarfing trends were probably the results of changes in upper watermass stratification.</p> <p>The complementary data set is provided in six zipped archives APPENDIX A, B, C, D, E and F (zipped with free software 7-Zip 22.00 (x64), 2022-06-15 from 1999-2022 Igor Pawlow), together with a description of the data in file Report_925B_suppl_1.pdf.</p>
FIGURE 4 in New molecular phylogeny of Lucinidae: increased taxon base with focus on tropical Western Atlantic species (Mollusca: Bivalvia)
FIGURE 4. Single gene tree for Lucinidae based on cyt b sequences, using Bayesian inference as implemented by MrBayes. Support values are posterior probabilities (PP); branches with PP <50 % were collapsed. Western Atlantic species in red. See Table 1 for sample details. Monitilorinae and Lucininae expanded in Fig. 5.
FIGURE 1 in New molecular phylogeny of Lucinidae: increased taxon base with focus on tropical Western Atlantic species (Mollusca: Bivalvia)
FIGURE 1. Combined gene tree for Lucinidae based on sequences from three genes (18 S rRNA, 28 S rRNA and cyt b), using Bayesian inference as implemented by MrBayes. Support values are posterior probabilities (PP). Codakiinae and Lucininae expanded in Figs 2 and 3. See Table 1 for sample details. Western Atlantic species in red. Scale is number of substitutions per site. Locality codes used on trees. ABD—Abu Dhabi; ANG—Angola; BD—Bermuda; BOC—Bocas, Panama; BR—Broome, Australia; CAL—California; CB – Chesterfield Bank; COSRIC West Costa Rica; CR—Croatia; DMP—Dampier, Australia; DEV—Devon, UK; FK—Florida Keys; FR—France; GD—Guadeloupe; HK—Hong Kong; JPN – Japan; KK—Kungkraben Bay, Thailand; LH—Lord Howe Island; LI – Lizard Island, Australia; MAD—Madagascar; MADANG—Madang, Papua New Guinea; MAUR – Mauritius; MB—Moreton Bay, Australia; MEX—West Mexico; NC—New Caledonia; NIG—Nigeria; OK— Okinawa; PAN – Panglao, Philippines; PNG – Papua New Guinea; ROD—Rodrigues; RUK—Ryukyus, Japan; SAF—Safaga, Red Sea; SGP—Singapore; SOL—Solomon Sea; SYD—Sydney, Australia; TCB—Tin Can Bay, Australia; TIM—East Timor; TJ—Tjärnö, Sweden; TUN—Djerba, Tunisia; VAN—Vanuatu; VEN—Venezuela; UK—England.
Figure 7 in Revalidation of Leucetta floridana (Haeckel, 1872) (Porifera, Calcarea): a widespread species in the tropical western Atlantic
Figure 7. Box-and-whisker plot (maximum and minimum size; upper and lower quartiles and median) of spicule size of Leucetta spp. A, triactine I length; B, triactine I width; C, triactine II length; D, triactine II width; E, tetractine I length; F, tetractine I width; G, tetractine II length; H, tetractine II width. Leucetta floridana (Caribbean), Z Leucetta floridana (Brazil), Leucetta sp., Leucetta microraphis.
Figure 5 in Revalidation of Leucetta floridana (Haeckel, 1872) (Porifera, Calcarea): a widespread species in the tropical western Atlantic
Figure 5. Leucetta floridana from the Caribbean (UFRJPOR 5360). A, L. floridana in situ (photo: S. Zea). B, transversal section of the cortex and choanosome; C, transversal section of the choanosome and atrium; D, triactine I; E, triactine II and the small triactines I; F, tetractine I; G, detail of the apical actine of tetractines I protruding into the atrium; H, tetractine II and several triactines I and tetractines I. Scale bars: A = 1 cm; B–H = 100 Mm.
Figure 1 in Revalidation of Leucetta floridana (Haeckel, 1872) (Porifera, Calcarea): a widespread species in the tropical western Atlantic
Figure 1. Sampling sites. Caribbean: 1 – Bocas del Toro (BDT), 2 – San Andrés Island (SAN), 3 – Urabá (URA); Brazil: 4 – Ceará (CEA), 5 – Rio Grande do Norte (RGN), 6 – Rocas Atoll (RAT), 7 – Fernando de Noronha Archipelago (FNO), 8 – Abrolhos Archipelago (ABR); Pacific: 9 – Australia (GBR), 10 – New Caledonia (NCA).
Figure 6 in Revalidation of Leucetta floridana (Haeckel, 1872) (Porifera, Calcarea): a widespread species in the tropical western Atlantic
Figure 6. Leucetta sp. from Brazil. A, live specimen (photo: G. Muricy); B, triactine I; C, tetractines I; D, triactine II and tetractine II. Scale bars: A = 1 cm, B–D = 100 Mm.
Figure 3. Leucetta microraphis from New Caledonia. A in Revalidation of Leucetta floridana (Haeckel, 1872) (Porifera, Calcarea): a widespread species in the tropical western Atlantic
Figure 3. Leucetta microraphis from New Caledonia. A, preserved specimen; B, triactine I; C, triactine II and several triactines I and tetractines I; D, tetractine I. Scale bars: A = 1 cm; B–D = 100 Mm.
Tracking sargassum pathways across the Tropical Atlantic
<p>This dataset includes the date, time, address, latitude, longitude, speed, and altitude data collected by GPS trackers deployed in sargassum mats. For more information on the methods and data see Fidai et al. (2023) 'Tracking and detecting sargassum pathways across the Tropical Atlantic'. </p>
North Atlantic synthetic tropical cyclone track, intensity, and rainfall dataset from RAFT
<p>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.</p> <p>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:</p> <ul> <li><strong>Basin ID</strong>: Identifies the basin (1 for North Atlantic)</li> <li><strong>Storm ID</strong>: Unique identification number for each TC, starting from 0</li> <li><strong>Year</strong>: Year of the environmental conditions used for modeling</li> <li><strong>Jday</strong>: Julian day of the year, ranging from 0 to 365</li> <li><strong>Longitude (lon)</strong>: Geographical longitude in degrees</li> <li><strong>Latitude (lat)</strong>: Geographical latitude in degrees</li> <li><strong>Maximum Wind Speed (vmax)</strong>: Measured in knots</li> <li><strong>Minimum Pressure (mslp)</strong>: Measured in hectopascals (hPa)</li> <li><strong>Radius of Maximum Wind (rmax)</strong>: Measured in nautical miles (nmi)</li> </ul> <p>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:</p> <ul> <li><strong>Total Accumulated Rainfall (p_accum)</strong></li> <li><strong>Frictional Precipitation Component (p_accum_f)</strong></li> <li><strong>Topographic Precipitation Component (p_accum_h)</strong></li> <li><strong>Shear-related Precipitation Component (p_accum_s)</strong></li> <li><strong>Vortex Stretching Precipitation Component (p_accum_t)</strong></li> </ul> <p>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.</p> <p>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').</p> <p>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.</p> <p> </p> <p><strong>How to cite:</strong></p> <p>Xu, W., Balaguru, K., Judi, D.R. <em>et al.</em> A North Atlantic synthetic tropical cyclone track, intensity, and rainfall dataset. <em>Sci Data</em> <strong>11</strong>, 130 (2024). https://doi.org/10.1038/s41597-024-02952-7</p>
F I G U R E 5 in Length-weight relationships of 55 mesopelagic fishes from the eastern tropical North Atlantic: Across- and within-species variation (body shape, growth stanza, condition factor)
F I G U R E 5 Log–log plot of the relative condition factor (Krel) vs. standard length (cm) calculated from length–weight relationships (LWRs) of the species (a) Argyropelecus affinis, (b) Argyropelecus sladeni, (c) Ceratoscopelus warmingii, (d) Diaphus dumerilii, (e) Electrona risso, (f) Lampanyctus nobilis, (g) Lepidophanes guentheri, (h) Notoscopelus resplendens and (i) Scopelogadus mizolepis (Table 3). Geographic regions are indicated by linetype, symbol and colour (EQ–C, dotted line, dark-blue square; EQ–N, two-dashed line, turquoise triangle; LO–E, solid line, red circle; LO–W, dashed line, violet diamond). If present, vertical dashed grey line indicates breakpoint in the LWR estimated by segmented regression analysis (cf. Table 2)
F I G U R E 1 in Length-weight relationships of 55 mesopelagic fishes from the eastern tropical North Atlantic: Across- and within-species variation (body shape, growth stanza, condition factor)
F I G U R E 1 Stations in the eastern low-oxygen (LO–E), western low-oxygen (LO–W), northern equatorial (EQ–N) and central equatorial (EQ–C) regions of the eastern tropical North Atlantic sampled in this study
F I G U R E 4 in Length-weight relationships of 55 mesopelagic fishes from the eastern tropical North Atlantic: Across- and within-species variation (body shape, growth stanza, condition factor)
F I G U R E 4 Distribution of form factor a3.0 for 55 mesopelagic species related to (a) body shape, (b) taxonomic family and (c) species. Form factor calculated from Equation 2 using across-species slope of S = 1.358 based on 1223 fish species presented in equation 17 in Froese (2006)
F I G U R E 3 in Length-weight relationships of 55 mesopelagic fishes from the eastern tropical North Atlantic: Across- and within-species variation (body shape, growth stanza, condition factor)
F I G U R E 3 Scatter plot of mean log a (SL) over mean b for 55 mesopelagic species with information on body shape. Body shape:, elongated;, fusiform;, short-deep
F I G U R E 2 in Length-weight relationships of 55 mesopelagic fishes from the eastern tropical North Atlantic: Across- and within-species variation (body shape, growth stanza, condition factor)
F I G U R E 2 Frequency distribution of (a) mean log a (binwidth 0.2) and (b) mean exponent b (binwidth 0.1) based on 55 records (measured in centimetres and grams) of mesopelagic species of the eastern tropical North Atlantic during cruise WH383
Fig. 1 in Dynamics of fish assemblages on a continuous rocky reef and adjacent unconsolidated habitats at Fernando de Noronha Archipelago, tropical western Atlantic
Fig. 1. Map of the Fernando de Noronha Archipelago showing the study area (Porto Beach) and permanent sampling stations.
Fig. 3 in Dynamics of fish assemblages on a continuous rocky reef and adjacent unconsolidated habitats at Fernando de Noronha Archipelago, tropical western Atlantic
Fig. 3. Canonical plotting of microhabitat characteristics (arrows) and fish species (points). Rug.: rugosity; Crev.: number of crevices; S. height: substratum height; C. algae: percent cover of encrusting coralline algae; Macr.: percent cover of Macroalgae; Turf: percent cover of turf algae; L. coral: percent cover of live coral; Other: percent cover of other organisms; B. rock: percent cover of bare rock; Sand: percent cover of sand and limestone; IHC: index of habitat complexity; Species names are abbreviated as the first three letters of genus and first three letters of specific epithet (see Table 4 for full scientific names).
ScienceDex guides
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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.