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27 results for “Submesoscale”
Final results from McCoy et al., 'Global Observations of Submesoscale Coherent Vortices in the Ocean', submitted to Progress in Oceanography.
<p>Submesoscale coherent vortices (SCVs) are small-scale, subsurface eddies that are ubiquitous in the ocean. Observations suggest that they efficiently trap and transport water, nutrients, and other properties thousands of kilometers away from their formation regions. However, the weak sea-surface signature restricts SCV observations to mostly chance encounters with shipboard subsurface instrumentation. Thus, the global occurrence, properties, and generation frequency of SCVs remain poorly constrained. Here we present results from a new algorithm used to identify SCVs from Argo float data, applied to roughly 2 million profiles conducted globally from August 1997 to January 2020.</p> <p>After application of the SCV detection algorithm to the global Argo array, we identify 2501 casts piercing spicy-core SCVs (those with anomalously hot and salty water mass characteristics), and 1583 casts piercing minty-core SCVs (anomalously cold and fresh cores) over more than 20 years of available data. The Matlab file 'final_individual_scvs.mat' contains various data for each SCV identified.</p> <p>By grouping detections from consecutive Argo casts, we are also able to record 383 spicy-core SCV time-series and 169 minty-core SCV time-series. The Matlab file 'final_timeseries_scvs.mat' contains the data for these time-series. </p> <p>For a more detailed description of each Matlab file, please see 'README.rtf'. </p> <p>Reach out to Daniel McCoy (dmccoy801@gmail.com) or Daniele Bianchi (dbianchi@atmos.ucla.edu) for inquiries. </p>
A Submesoscale Eddy Identification Dataset Derived from GOCI I Chlorophyll–a Data based on Deep Learning
<p>This is an observational dataset on submesoscale eddies, which obtains from high–resolution chlorophyll–a distribution images from GOCI I. We employed a combination of digital image processing, filtering, YOLOv7–X, and small object detection techniques, along with specific chlorophyll image enhancement processing, to extract information on submesoscale eddies, including their time, polarity, geographical coordinates of the eddy center, eddy radius, coordinates of the upper left and lower right corners of the prediction box, area of the eddy's inner ellipse, and confidence score, which covers eight daily periods between 00:00 and 08:00 (UTC) from April 1, 2011, to March 31, 2021. We identified a total of 19,136 anticyclonic eddies and 93,897 cyclonic eddies at a confidence threshold of 0.2. The mean radius of anticyclonic eddies is 24.44 km (range 2.5 km to 44.25 km), while that of cyclonic eddies is 12.34 km (range 1.75 km to 44 km). The unprecedented hourly resolution dataset on submesoscale eddies provides information on their distribution, morphology, and energy dissipation, making it a significant contribution to understanding marine environments and ecosystems, as well as improving climate model predictions. The article doi associated with this dataset is https://doi.org/10.5194/essd-2024-188.</p>
Representation of submesoscale baroclinic instabilities in ROMS and MPAS-O
<h2>Idealized Model Outputs</h2> <p>This dataset archive contains a subset of the model outputs that are necessary for reproducing the analyses presented in the accompanying manuscript. The datasets correspond to either the MPAS-O or ROMS ocean models at resolutions ranging from 10 km to 100 m. Velocity and corresponding invariant (vorticity/divergence) variables and tracer variables are saved here with only their surface values. Additionally, all variables except those required to compute kinetic energy time series over the simulation period are only saved for a time window that corresponds to the shaded region in Figure 4 of the accompanying manuscript. Some grid metric variables necessary for MPAS-O calculations are also included within "MPAS-O_Initial_*.nc" files. There are two zipped tar directories (mpaso_winds.tar.gz and roms_winds.tar.gz) containing the outputs of all wind stress sensitivity tests needed to recreate the results shown in Figure 2. All datasets are saved in NetCDF format.</p>
Drifter deployment strategies to determine Lagrangian surface convergence in submesoscale flows
<p>Data used for the preparation of the manuscript "Drifter deployment strategies to determine Lagrangian surface convergence in submesoscale flows".</p>
Datasets of submesoscale circulations in the subtropical northeastern Pacific
<p>Dataset of results in Interannual variations of submesoscale circulations in the subtropical northeastern Pacific (Sasaki et al., 2022) based on a submesoscale permitting hindcast simulation. Please see the dataset list in README.txt.</p>
Submesoscale motions driven by down-front wind around an anticyclonic eddy observed by gliders
<p>This datasets was used in the study named 'Submesoscale motions driven by down-front wind around an anticyclonic eddy observed by gliders'. It contains glider data. Please let me know if you need any more information.</p>
Submesoscale motions at the edge of an abnormal anticyclonic eddy in the northwestern South China Sea
<p>This datasets was used in the study named 'Submesoscale motions at the edge of an abnormal anticyclonic eddy in the northwestern South China Sea'. It contains glider data. Please let me know if you need any more information.</p>
Data to reproduce paper: Submesoscale effects on changes to export production under global warming
<p>Data to reproduce paper: Submesoscale effects on changes to export production under global warming</p> <p>Paper is being submitted to the AGU journal Global Biogeochemical Cycles. A pre-print copy of the submitted paper will be available on essoar.org</p> <p>Matlab data files contain data to reproduce all figures in the paper and supplement</p> <p>Zipped files contain the input files for ROMS (Regional Ocean Modeling System) to re-run the simulations, when combined with the netcdf files for initial and boundary conditions. </p> <p>Pdf and image files are copies of the paper figures; supplement2_CodeDataList has a table matching the code and data for each figure. </p> <p>Matlab software is at github.com/gjayb/submesoBioROMS/ and will be archived here on Zenodo as well.</p> <p>The Bipit code for the biological model within ROMS is archived at <a href="http://doi.org/10.5281/zenodo.5716181">doi.org/10.5281/zenodo.5716181</a>, within tpos-roms-v.1.0.0\ROMS\Nonlinear\Biology </p> <p>This work was primarily funded by the National Science Foundation (NSF) under grants OCE-1658550 and OCE-1658541. This material is based on work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the NSF under Cooperative Agreement No. 1852977. Computing and data storage resources, including the Cheyenne supercomputer (doi:10.5065/D6RX99HX), were provided by the Computational and Information Systems Laboratory (CISL) at NCAR. We thank all the scientists, software engineers, and administrators who contributed to the development of CESM. <br> DBW acknowledges additional support from the National Oceanic and Atmospheric Administration (NA18OAR4310408) and the National Aeronautics and Space Administration (80NSSC19K1116). GJB acknowledges additional support from a Janney grant awarded by the Johns Hopkins University Applied Physics Laboratory. </p>
EICC_Submesoscale
<p>Data files and python scripts for 'Submesoscale processes associated with the East India Coastal Current' </p>
Submesoscale dynamics in the Bay of Bengal: Inversions and instabilities
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Data from: Crossing the line: tunas actively exploit submesoscale fronts to enhance foraging success
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Chen et al (2021) Mesoscale and Submesoscale Shelf-Ocean Exchanges Initialize an Advective Marine Heatwave
<p>data and software for Chen et al (2021) "Mesoscale and Submesoscale Shelf-Ocean Exchanges Initialize an Advective Marine Heatwave" in Journal of Geophysical Research: Oceans</p>
Data for: A persistent submesoscale frontal slick: A novel marker of the mesoscale flow field in a large lake (Lake Geneva)
<p><strong>This study provides the first evidence of the evolution of a frontal slick in Lake Geneva, a large, deep lake in western Europe. Our results rely on field measurement, remote sensing data, and three-dimensional numerical modeling. The data presented here comprise:</strong></p> <p><strong>(1) measurements from an autonomous catamaran, including GNSS data, near-surface temperature profiles, weather station, near-surface Acoustic Doppler Current Profilers (ADCP), and RGB images from two onboard cameras,</strong></p> <p><strong>(2) infrared images from a FLIR camera attached to a helium-filled balloon, </strong></p> <p><strong>(3) time-lapse images from a shore-based imaging package provided slanted (non-perpendicular) field of views of the lake surface water,</strong></p> <p><strong>(4) Excitation Emission Matrix (EEM) using a Fluorescence Spectrometer for the Fluorescent Dissolved Organic Matter (FDOM) inside slick/non-slick samples, and</strong></p> <p><strong>(5) results of a 3D numerical simulation provided details of the lake hydrodynamics during the field measurement campaign.</strong></p> <p><strong>The three-dimensional model used in this study is based on the MIT General Circulation Model (MITgcm,<a href="http://mitgcm.org/"> http://mitgcm.org/</a>,<a href="https://doi.org/10.1029/96JC02775"> https://doi.org/10.1029/96JC02775</a>).</strong></p>
Dataset for the manuscript, "Submesoscale Nitrate Upwelling by Cyclonic Eddies in the Upstream Kuroshio Current"
<p>Data and codes for the manuscript, entitled with "Submesoscale Nitrate Upwelling by Cyclonic Eddies in the Upstream Kuroshio Current" by Gloria Silvana Duran Gomez and Takeyoshi Nagai</p>
Baroclinic instability induced mesoscale and submesoscale processes in the river plumes: A laboratory investigation on a rotating tank
<p>This dataset studied the baroclinic instability (BI) induced mesoscale and submesoscale processes by conducting laboratory rotating flume experiments. We acquired the high-resolution velocity data by Parrticle Image Velocity (PIV). The mesoscale and submesoscale vortices were identified and tracked, and the variations of the instabilities and kinetic energy of the plume system under different inflow conditions and slopes were summarized. Number '1' to '33' mean cases number; 'gs', 'ss', and 'ns' stand for 'gentle slope', 'steep slope', and 'no slope'; 'T20' to 'T60' correspond to the rotation period from 20 to 60 s; 'g4' to 'g10' correspond to the reduced gravity between the buoyant plume and environmental fluid from 4 to 10 cm/s^2. 'vor_char' means the vortices characteristics including the time series of the vortex center and vortex contour.</p>
Observations of island wakes at high Rossby numbers: Evolutions of submesoscale vortices and free shear layer
<p>The dataset contains shipboard and moored current and temperature measurements used to produce the figures in a manuscript entitled as "Observations of island wakes at high Rossby numbers: Evolutions of submesoscale vortices and free shear layer". All the data are in MATLAB file format.</p> <p>Fig3_Experiment2.mat: current velocity and temperature data shown in Figure 3 in ten surveys.</p> <p>Fig4_MOORING.mat: Moored zonal velocity data at W1 and W2 shown in Figure 4.</p> <p>Fig6_L6.mat: meridional current data, Ro and shear-squared shown in Figure 6.</p> <p>Fig7_Experiment1_T1.mat: temperature and current data in Figures 7a-c.</p> <p>Fig7_case05.mat: temperature and current data in Figures 7d and 7g.</p> <p>Fig7_case08.mat: temperature and current data in Figures 7e and 7h.</p> <p>Fig7_case11.mat: temperature and current data in Figure 7f and 7i.</p> <p>Fig8_(a)-(e): temperature and current data in Figure 8.</p> <p> </p> <p> </p>
Dataset used in the manuscript "The importance of topographically-induced submesoscale processes on cross-shelf transport"
<p>Dataset used in the manuscript "The importance of topographically-induced submesoscale processes on cross-shelf transport", submitted to JGR-Oceans on December 5, 2019 (under review).</p>
SST data from JAXA for "Importance of Strains in Kinetic Energy Conversion for Submesoscale Processes from an Anisotropic Perspective"
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Source data for "Submesoscales are a significant turbulence source in global ocean surface boundary layer"
<p>The files here provide the data and codes for reproducing figures from the paper titled "Submesoscales are a significant turbulence source in global ocean surface boundary layer" by Dong et al.</p> <p>It should be clarified that Fig.1 is originally generated by Python and then produced in Illustrator, and Fig.5 is completely produced by Illustrator. All other figures are produced by MATLAB.</p>
Characteristics of Mesoscale to Submesoscale Eddies in the Labrador Sea: Insights from Ship Observations
<p>Numerical simulation output from NATL60 model in the Labrador Sea: depth-averaged (15-100 m) horizontal velocity vector (u, v) for three snapshots (15 May 2013, 15 June 2013, 15 August 2013). The data was subject to an eddy tracking algorithm (Angular Momentum Eddy Detection and tracking Algorithm; AMEDA; https://doi.org/10.1175/JTECH-D-17-0010.1)</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.