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5 results for “Thermocline”
Observations of Diurnal Coastal-Trapped Waves with a Thermocline-Intensified Velocity Field
<p>Repository for the data presented in the JPO 2019 article: "Observations of Diurnal Coastal-Trapped Waves with a Thermocline-Intensified Velocity Field". Observations were compared against output from Ken Brink's model. The model is available from: <a href="https://darchive.mblwhoilibrary.org/handle/1912/10527">https://darchive.mblwhoilibrary.org/handle/1912/10527</a>.</p>
Data to reproduce figures in "Tropical thermocline helps power Pacific equatorial upwelling"
<p>A set of netcdf include results of the energetics in the Pacific STC region. </p> <p>A jupyter notebook uses all those dataset to reproduce the main plots in the paper. Code used to compute the energetics can be found within the 'Tailleux' class inside this module: https://github.com/inciente/EastPac/blob/main/KE_tools.py</p> <p>Please feel free to reach out if you're trying to use the data, or apply the energetics framework to your own simulations.</p>
McMurdo Dry Valleys Lake Vanda thermocline temperature time series
This data set includes the deployment of anchored thermistors Lake Vanda, Wright Valley, as part of an Antarctica New Zealand project. Thermistors were positioned at the sediment-water interface and 10 cm above the lake bottom within the thermocline at 24 m depth. This dataset is funded through the New Zealand Foundation for Research, Science and Technology grant # CO1X0306 and the NASA grant NNX13AI60G, with USAP event G-063. This project is not funded through the MCM LTER program. MCM LTER hosts this synergistic project.
Imaging thermocline microstructure in 2D with swaths traced by wave-pumped χpods: dataset and code
<p><a href="https://doi.org/10.1029/2024JC022134">Associated paper</a> published in <em>J. Geophys. Res. Oceans</em></p> <h2>Dataset summary</h2> <p>Location: 0°N, 140°W<br>Depth: 120 m<br>Period: 16-Sep-2014 to 19-Oct-2015</p> <p>This data archive contains two types of data files:<br>data_yymmdd.mat<br>grid_yymmdd.mat<br>Each file contains 24 hours of data. There are 394 of each type.</p> <p>Arrays in data_yymmdd.mat are single precision (except the 'time' array) to keep file sizes small.</p> <h2>Contents of the data files</h2> <p>Each Matlab file contains a single struct. These structs include readmes, which are reproduced in the full PDF readme (chipod_swaths_readme.pdf).</p> <p>Files are grouped into months and zipped (yymm.zip) to ease downloading.</p> <h2>Reading the data file with Python</h2> <p>Example code to read the files into Python as dictionaries is given in the full PDF readme (chipod_swaths_readme.pdf).</p> <h2>Matlab code to produce the processed data</h2> <p>The code to read in raw chipod data and process them is primarily contained in the file 'swaths_paper_data_preparation.m'. This file calls three other files ('raw_load_chipod.m', 'deglitch.m', and 'bin.m'). All of these files are provided for completeness, but we are only archiving the processed outputs (not the raw voltage signals). Please email if more information is needed.</p> <h2>Matlab code for the convolutional neural network</h2> <p>See 'chipod_swaths_convolutional_neural_network.m'.</p>
Movements of the Thermocline lead to high variability in benthic mixing in the nearshore of a large lake
<p>2013 data</p>
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