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== 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.

Supported by the 2020 – 2021 Results Fund, a Proof of Concept (POC) was developed for a predictive model to find  (dis-)similarities between in situ multidimensional profiles of oceanographic data of the Pacific Ocean. The in situ Conductivity-Temperature-Depth (CTD) profiles are classified using 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.  

Supported by the 2020 – 2021 Results Fund, a Proof of Concept (POC) was developed for a predictive model to find  (dis-)similarities between in situ multidimensional profiles of oceanographic data of the Pacific Ocean. The in situ Conductivity-Temperature-Depth (CTD) profiles are classified using the profile classification model <ref name=":0">https://pyxpcm.readthedocs.io/en/latest/index.html</ref>. The ocean profile classification model allows to automatically assemble ocean profiles in clusters according to their vertical structure similarities. The geospatial properties of these clusters can be used to address a large variety of oceanographic problems, E.g. front detection, water mass identification, natural region contouring (gyres, eddies), reference profile selection for QC validation, etc. The vertical structure of these clusters furthermore provides a highly synthetic representation of large ocean areas that can be used for dimensionality reduction and coherent intercomparisons of ocean data (re)-analysis or simulations <ref name=":0" />.   

 

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 ==