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Oceans Six.html
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Oceans Six.html
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<!DOCTYPE html>
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<title>Oceans' Six</title>
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<header>
<h1>Oceans' Six</h1>
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<div class = "parallax"> </div>
<h3>Using 25 years of sea level anomaly data to study mesoscale eddies in Bermuda</h3>
<p>Sea level anomaly data from 1993 to 2018 was used to identify and study the formation of mesoscale eddies near Bermuda in the Sargasso Sea. These ocean eddies, swirling whirlpools of water between 10 and 500 km across, transport millions of cubic meters of nutrients and water through the ocean and affect the growth rate of phytoplankton. An algorithm was developed to identify these mesoscale eddies from satellite-derived sea level anomaly data. With Python packages including Matplotlib and Pandas, the algorithm calculates contours in sea level anomaly and analyzes them for inclusion in the eddy dataset. This technique can be used on the entire dataset to identify eddies and quantify associated statistics such as size, duration, direction and type. Visual analysis shows that most eddies near Bermuda travel westward, that there are more cyclonic than anticyclonic eddies, and that eddy formation is seasonally dependent.</p>
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<h3>Languages, libraries, and packages we used:</h3>
<ul>
<li>The progamming language we used for this project was Python.</li>
<li>The libraries and packages we used were matplotlib, numpy, netcdf4, and daytime.</li>
</ul>
<p></p>
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<h3>Some challenges we encountered:</h3>
<ul>
<li>A difficulty was plotting. The x, y, z-axes were difficult to understand.</li>
<li>Figuring out how to make data readable and understandable.</li>
<li>Data from 1950 to today, 9000 days, creating a function to convert into a usable format(month, day and year).</li>
<li>Learning dot format.</li>
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<h3>Some problem solving techniques we used:</h3>
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<h3>Sample results & graphs:</h3>
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