  An unsupervised clustering using a Gaussian mixture model is applied to Argo
profiles distributed over the entire ocean. We employ as the coordinate
components in feature space the path signature, which is a central notion in
rough path theory. This allows us to better identify the oceanic condition at
each horizontal point with distinct clusters, than by using conventional
temperature and salinity coordinate. To the best of my knowledge, it is the
first attempt at clustering almost all of the existing Argo profiles with the
full use of measured sequences of temperature, salinity, and pressure. We will
also discuss why the path signature is relevant to representing the property of
a profile.
