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7 results for “submesoscale eddy”
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>
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>
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>
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>
Microstructure turbulence, conductivity-temperature-depth, and current velocity measurements of a submesoscale eddy in Terra Nova Bay (2018-2019)
Open the record for dataset details and reuse information.
Submesoscale Eddy and Frontal Instabilities in the Kuroshio Interacting with a Cape South of Taiwan
<p>The dataset contains satellite images and model data used to produce the figures in the manuscript. All the data are in MATLAB file format.</p> <p> </p> <p>Fig2_(a)-(f).mat: satellite images shown in Figure 2.</p> <p>Fig3.mat: Correlations between the detected eddies.</p> <p>Fig4.mat: satellite image and drifter tracks shown in Figure 4.</p> <p>Fig5_b_c.mat: model temperature and current data in Figures 5.</p> <p>Fig5_d_e.mat: a time-latitude diagram of <em>Ro</em> and a time series of upstream zonal velocity in Figures 5.</p> <p>Fig7.mat: model temperature and current data in Figures 7.</p> <p>Fig9_(a)-(f).mat: satellite images, and model temperature and current data in Figure 9.</p> <p>Fig10: Monthly mean barotropic conversion terms field, and baroclinic conversion term in Figure 10.</p>
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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
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