OD51A:
Data Science for Modern Oceanography: Statistics, Machine Learning, Visualization, and More I

Session ID#: 92492

Session Description:
Oceanographic research in today's world increasingly relies on analyzing multiple datasets, including ship-based measurements, profiles from autonomous instruments such as floats and gliders, satellite remote sensing data, as well as output from models and state estimates. These datasets are growing larger and more complex every day, and future advances in ocean observing and modeling will add significantly to the quantity and variety of "Big Data" across all disciplines of oceanography. Innovative statistical methods, computational techniques, and data visualizations will be needed in the coming decades to distill these data and to extract maximum scientific understanding. New developments in statistics and data science have the potential to transform our knowledge of the ocean across many spatial and temporal scales and can help address various emerging challenges in oceanographic data analysis. This session solicits studies on using the latest techniques from statistics, machine learning, and visualization to analyze datasets in oceanography and related areas of climate science, both those currently existing as well as those that will be available in the near future. Presentation topics may include computational methods for large datasets; software platforms and tools; model diagnostics, validation, and parameterization; spatio-temporal interpolation; uncertainty quantification; classification and regression techniques; pattern recognition; as well as other advanced data science topics.
Co-Sponsor(s):
  • OB - Ocean Biology and Biogeochemistry
  • OM - Ocean Modeling
  • PL - Physical Oceanography: Mesoscale and Larger
Index Terms:

1920 Emerging informatics technologies [INFORMATICS]
1942 Machine learning [INFORMATICS]
1986 Statistical methods: Inferential [INFORMATICS]
1994 Visualization and portrayal [INFORMATICS]
Primary Chair:  Alison R Gray, University of Washington, School of Oceanography, Seattle, United States
Co-chairs:  Mikael Kuusela, Carnegie Mellon University, Department of Statistics and Data Science, Pittsburgh, United States and Donata Giglio, University of Colorado Boulder, Department of Atmospheric and Oceanic Sciences, Boulder, United States
Primary Liaison:  Alison R Gray, University of Washington, School of Oceanography, Seattle, United States
Moderators:  Mikael Kuusela, Carnegie Mellon University, Department of Statistics and Data Science, Pittsburgh, United States and Donata Giglio, University of Colorado Boulder, Department of Atmospheric and Oceanic Sciences, Boulder, United States
Student Paper Review Liaisons:  Alison R Gray, University of Washington, School of Oceanography, Seattle, United States and Mikael Kuusela, Carnegie Mellon University, Department of Statistics and Data Science, Pittsburgh, United States

Abstracts Submitted to this Session:

OD51A-01
When data arrive as curves: an overview of Functional Data Analysis methods in oceanography (651523)
David Nerini, Aix-Marseille University, Mediterranean Institute of Oceanography, Marseille, France, Etienne Pauthenet, Sorbonne Université, LOCEAN‐IPSL, CNRS/IRD/MNHN, Paris, France, Pascal Monestiez, INRA Institut National de la Recherche Agronomique, Avignon, France, Christophe Guinet, Centre d’Etudes Biologiques de Chizé (CEBC), UMR 7372 Université de la Rochelle-CNRS, Villiers en Bois, France, Fabien Roquet, University of Gothenburg, Department of Marine Sciences, Gothenburg, Sweden, Madec Gurvan, LOCEAN-IPSL, CNRS/IRD/MNHN/Sorbonne Université, Paris, France, Frédéric Ménard, IRD, France, Christophe Menkes, IRD/LOCEAN, Nouméa, New Caledonia and Arnaud Bertrand, Institut de Recherche pour le Développement (IRD), France
OD51A-02
A Functional Data Approach to the Argo Project (654385)
Drew Yarger, Tailen Hsing and Stilian Stoev, University of Michigan, Statistics, Ann Arbor, United States
OD51A-03
The thermohaline modes of the global ocean (635903)
Etienne Pauthenet1, Fabien Roquet2, Gurvan Madec1, Jean-baptiste Sallee3 and David Nerini4, (1)Sorbonne Université, LOCEAN‐IPSL, CNRS/IRD/MNHN, Paris, France, (2)University of Gothenburg, Department of Marine Sciences, Gothenburg, Sweden, (3)LOCEAN-IPSL, CNRS/IRD/MNHN/Sorbonne Université, Paris, France, (4)Mediterranean Institute of Oceanography, Marseille, France
OD51A-04
A New Bottom Water Climatology Using a Stacked Random Forest and Objective Mapping Approach (641915)
Paige D Lavin, Cooperative Institute for Satellite Earth System Studies - University of Maryland, Earth System Science Interdisciplinary Center, College Park, United States and Gregory C Johnson, Pacific Marine Environmental Laboratory, Seattle, United States
OD51A-05
Detecting rainfall through prediction of precipitation forcing in the salinity balance equation (651985)
Oksana Chkrebtii, United States and Frederick Bingham, University of North Carolina at Wilmington, Wilmington, NC, United States
OD51A-06
Rethinking Prior Approaches – Bayesian Neural Networks for Information Retrieval from Ocean Color (657309)
Susanne Elizabeth Craig1, Erdem Karakoylu1 and Deric Gray2, (1)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (2)US Naval Research Laboratory, Washington, DC, United States
OD51A-07
Spatio-temporal changes in upper ocean heat content estimates: an internationally-coordinated intercomparison (644883)
Abhishek Savita, Institute for Marine and Antarctic Studies, University of Tasmania, Antarctic Climate and Ecosystems Cooperative Research Centre, Hobart, TAS, Australia, Catia M Domingues, National Oceanography Centre, Antarctic Climate and Ecosystems Co-operative Research Centre, United Kingdom, Tim Boyer, NOAA NCEI, Washington, United States, Simon A Good, Met Office, Exeter, United Kingdom, Viktor Vladimir Gouretski, University of Hamburg, Hamburg, Germany, Masayoshi Ishii, Japan Meteorological Agency, Tsukuba, Japan, Gregory C Johnson, Pacific Marine Environmental Laboratory, Seattle, United States, John M Lyman, JIMAR/PMEL, Seattle, WA, United States, Josh K Willis, Jet Propulsion Laboratory, Pasadena, CA, United States, Didier Monselesan, CSIRO Oceans and Atmosphere, Hobart, TAS, Australia, John Antonov, University Corporation for Atmospheric Research, Boulder, CO, United States, Susan Anne Wijffels, Woods Hole Oceanographic Institution, Woods Hole, MA, United States, Rebecca Cowley, CSIRO, Hobart, TAS, Australia, Simon James Marsland, CSIRO Ocean and Atmospheric Research Aspendale, Aspendale, VIC, Australia, Peter Dobrohotoff, CSIRO Ocean and Atmospheric Research Aspendale, Ocean and Atmosphere, Melbourne, VIC, Australia, Will R Hobbs, University of Tasmania, Institute for Marine and Antarctic Studies, Hobart, TAS, Australia and John Church, University of New South Wales, Climate Change Research Centre, Sydney, NSW, Australia
OD51A-08
Non-Gaussian Process Modeling of Argo Float Data (650308)
Jonas Wallin, Lund University, Department of statistics, Lund, Sweden
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