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Line 28: + − − [[File:Ocean Profile Classification Model.png|alt=Data-driven approach for analyzing ocean data|thumb|478x478px|<small>'''Profile classification model: a data-driven approach for analyzing ocean data'''</small> <ref>Guillaume Maze et al, Coherent heat patterns revealed by unsupervised classification of Argo temperature profiles in the North Atlantic Ocean, Progress in Oceanography, Volume 151, 2017, Pages 275-292.</ref>]]
Using AI to advance oceanography (view source)
Revision as of 10:11, 22 December 2021
, 10:11, 22 December 2021no edit summary
== 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.
[[File:Ocean Profile Classification Model.png|alt=Data-driven approach for analyzing ocean data|thumb|469x469px|<small>'''Profile classification model: a data-driven approach for analyzing ocean data'''</small> <ref>Guillaume Maze et al, Coherent heat patterns revealed by unsupervised classification of Argo temperature profiles in the North Atlantic Ocean, Progress in Oceanography, Volume 151, 2017, Pages 275-292.</ref>]]
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" />.
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" />.
