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Using AI to advance oceanography (view source)
Revision as of 12:25, 17 December 2021
, 12:25, 17 December 2021→The Solution
== The Solution ==
== The Solution ==
Data-driven analysis approaches are better suited for such type of analysis. AI has the ability to sift through the data piles of ocean data to find the complex relation between the ocean observations.
Data-driven analysis approaches are better suited for such type of analysis. AI has the ability to sift through the data piles of ocean data to find the complex relation between the ocean observations.
To find (dis-)similarities between in situ multidimensional profiles of oceanographic data of the Pacific Ocean, in situ Conductivity-Temperature-Depth (CTD) profiles are classified using an AI approach, the profile classification model <ref>https://pyxpcm.readthedocs.io/en/latest/index.html</ref>. Two sets of experiments where conducted. The first experiment clusters a total of 3602 CTD profiles, where the final data point is less than 1,000 dbar, according to their temperature value only. Results have shown that the dataset of temperature profiles contains 9 groups of vertically coherent heat patterns, or classes. Each of the temperature profile classes reveals unique and physically coherent heat distributions along the vertical axis. When mapped in space, each of the 9 classes is found to define an oceanic region, even if no spatial information was used in the model determination. In the second experiment, both temperature and salinity observations are considered. Results have shown that depending on the season, up to 15 classes are observed, with occasional classes that correspond to sensor failures.
== References ==
== References ==