Bridging observations, theory and numerical simulation of the ocean using machine learning
Progress within physical oceanography has been concurrent with the increasing
sophistication of tools available for its study. The incorporation of machine learning (ML) …
sophistication of tools available for its study. The incorporation of machine learning (ML) …
Finale: impact of the ORCHESTRA/ENCORE programmes on Southern Ocean heat and carbon understanding
AJS Meijers, MP Meredith… - … of the Royal …, 2023 - royalsocietypublishing.org
The 5-year Ocean Regulation of Climate by Heat and Carbon Sequestration and Transports
(ORCHESTRA) programme and its 1-year extension ENCORE (ENCORE is the National …
(ORCHESTRA) programme and its 1-year extension ENCORE (ENCORE is the National …
Three types of Antarctic Intermediate Water revealed by a machine learning approach
X Xia, Y Hong, Y Du, P Xiu - Geophysical Research Letters, 2022 - Wiley Online Library
The subduction and export of Antarctic Intermediate Water (AAIW) is important for the heat,
freshwater, carbon, and nutrient budgets of the world's oceans. Three types of AAIW are …
freshwater, carbon, and nutrient budgets of the world's oceans. Three types of AAIW are …
A multivariate functional-data mixture model for spatio-temporal data: inference and cokriging
In this paper, we introduce a model for multivariate, spatio-temporal functional data.
Specifically, this work proposes a mixture model that is used to perform spatio-temporal …
Specifically, this work proposes a mixture model that is used to perform spatio-temporal …
Machine learning‐based clustering of oceanic Lagrangian particles: Identification of the main pathways of the Labrador Current
Modeled geospatial Lagrangian trajectories are widely used in Earth Science, including in
oceanography, atmospheric science and marine biology. The typically large size of these …
oceanography, atmospheric science and marine biology. The typically large size of these …
[HTML][HTML] Characterizing vertical upper ocean temperature structures in the European Arctic through unsupervised machine learning
EE Thomas, M Müller - Ocean Modelling, 2022 - Elsevier
In-situ observations of subsurface ocean temperatures are, in many regions, inconsistently
distributed in time and space. These spatio-temporal inconsistencies in the observational …
distributed in time and space. These spatio-temporal inconsistencies in the observational …
Detection of coherent thermohaline structures over the global ocean using clustering
E Romero, E Portela, L Tenorio-Fernandez… - Deep Sea Research …, 2024 - Elsevier
The classification of the ocean in water masses with similar physical and/or biogeochemical
characteristics provides an ideal framework for an efficient monitoring of the change in …
characteristics provides an ideal framework for an efficient monitoring of the change in …
Unsupervised classification of the northwestern European seas based on satellite altimetry data
From generating metrics representative of a wide region to saving costs by reducing the
density of an observational network, the reasons to split the ocean into distinct regions are …
density of an observational network, the reasons to split the ocean into distinct regions are …
Sea Ice‐Driven Variability in the Pacific Subantarctic Mode Water Formation Regions
Abstract Subantarctic Mode Water (SAMW) forms north of the Subantarctic Front, in regions
of deep winter mixed layers, and is important to the absorption and storage of anthropogenic …
of deep winter mixed layers, and is important to the absorption and storage of anthropogenic …
On the choice of training data for machine learning of geostrophic mesoscale turbulence
Data plays a central role in data‐driven methods, but is not often the subject of focus in
investigations of machine learning algorithms as applied to Earth System Modeling related …
investigations of machine learning algorithms as applied to Earth System Modeling related …