![]() ![]() This could include weather in other areas of the world, or new processes that they are learning about, such as wind/wave correlations, sea breezes (land/air interactions), or heat capacity (air/sea temp relationships).Īs part of our 2020 Virtual REU, I created the following notebook to demonstrate some basic data analysis techniques using a few years of data from NDBC Station 44025. With larger datasets like NDBC, which has stations all over the world, students can compare the patterns they’ve identified and are familiar with, with patterns they may not be as familiar with. ![]() With 10-years of data plotted at once, you can quickly see what the mean and variability look like over the course of the year, as well as the impact from the occasional extreme event (read: storm). Here is just one example of showing the annual cycle of Sea Surface Temperature in the Mid Atlantic at NDBC Station 44025 (my favorite station – everyone should have one ). the weather near them), while they are also learning new data analysis techniques and developing their programming skills. That’s why I love the NDBC dataset, because it makes weather data easily accessible. This allows students to visualize data and look for patterns they are hopefully familiar with (i.e. Of course, as oceanographers, weather data is far more relevant to our research goals, but it’s also useful to start with more accessible weather or “ocean weather” related examples, as those will be more familiar with students, before diving into more niche oceanographic datasets. Which is why you’ll often find weather-related data used in data analysis courses. ![]()
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