OD44C:
Visualization, Statistics, and Model Validation of Big Data for Oceanography I Posters


Session ID#: 28299

Session Description:
Oceanographic research in today’s world often requires analysis of multiple datasets including ship-based in situ measurements, profiles by autonomous floats such as Argo and Deep Argo, satellite remote sensing data, as well as ocean model outputs and ocean state estimates that ingest these observations into global and regional models. These datasets are so large and varied that traditional data analysis approaches are inadequate to deal with the information surge in the oceanographic community. Innovative statistical analysis, computational techniques, and data visualization developed for Big Data Analytics are needed and can create new research opportunities. New software and computational methods can help with data collection, analysis, curation and visualization. These new techniques can advance our understanding and modeling of the global ocean circulation and its role in Earth’s climate variations. This session solicits creative studies of statistics, visualization, and modeling to address emerging challenges in oceanography and climate data analysis. The presentation topics may broadly include new computational platforms for big data, optimal data gridding, ocean model diagnostics and validation, pattern detection, machine and statistical learning, and other advanced data mining techniques in oceanographic research.
Primary Chair:  Donata Giglio, University of California San Diego, Scripps Institution of Oceanography, La Jolla, CA, United States
Co-chairs:  Barbara Ann Bailey, San Diego State University, Department of Mathematics and Statistics, San Diego, CA, United States and Samuel S.P. Shen, San Diego State University, Department of Mathematics and Statistics, San Diego, CA, United States
Moderators:  Barbara Ann Bailey1, Donata Giglio2 and Samuel S.P. Shen1, (1)San Diego State University, Department of Mathematics and Statistics, San Diego, CA, United States(2)University of California San Diego, Scripps Institution of Oceanography, La Jolla, CA, United States
Student Paper Review Liaisons:  Donata Giglio, University of California San Diego, Scripps Institution of Oceanography, La Jolla, CA, United States and Samuel S.P. Shen, San Diego State University, Department of Mathematics and Statistics, San Diego, CA, United States
Index Terms:

1942 Machine learning [INFORMATICS]
1986 Statistical methods: Inferential [INFORMATICS]
1994 Visualization and portrayal [INFORMATICS]
4299 General or miscellaneous [OCEANOGRAPHY: GENERAL]
Cross-Topics:
  • BN - Biogeochemistry and Nutrients
  • OM - Ocean Modeling
  • PO - Physical Oceanography: Other

Abstracts Submitted to this Session:

Research Workspace: A web-based collaborative data management, analysis, and publication platform (325286)
Jesse Lopez1, Robert Bochenek2, Ross Martin2, David Foster3, Shane StClair1, Chris Michael Turner4 and Brian Stone2, (1)Axiom Data Science, Portland, OR, United States, (2)Axiom Data Science, Anchorage, AK, United States, (3)Axiom Data Science, Providence, RI, United States, (4)Axiom Data Science, AK, United States
Enabling Interactive Synthesized Visualization, Exploration, and Access of In Situ and Satellite Ocean Datasets (307073)
Charles K Thompson1, Flynn Platt2, Joe T Roberts3, Vardis M Tsontos4, Chi Hin Lam5, Nga T Quach1 and Sean Cody Arms6, (1)Jet Propulsion Laboratory, Pasadena, CA, United States, (2)NASA Jet Propulsion Laboratory, Pasadena, United States, (3)Jet Propulsion Laboratory, Pasadena, United States, (4)Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States, (5)Large Pelagics Research Center/University of Massachusetts, Boston, United States, (6)University Corporation for Atmospheric Research, Unidata, Boulder, CO, United States
A New Website for Seamless Navigation of the Argo Data Set (Invited) (307333)
Tyler Tucker, University of Colorado, Department of Atmospheric and Oceanic sciences, Boulder, CO, United States, Megan Scanderbeg, Scripps Institution of Oceanography, La Jolla, United States and Donata Giglio, CU Boulder, Department of Atmospheric and Oceanic Sciences, Boulder, United States
Estimating Oxygen in the Southern Ocean using Argo Temperature and Salinity (320228)
Donata Giglio, University of California San Diego, Scripps Institution of Oceanography, La Jolla, CA, United States, Vyacheslav Lyubchich, University of Maryland Center for Environmental Science Chesapeake Biological Laboratory, Solomons, United States and Matthew R Mazloff, University of California San Diego, Scripps Institution of Oceanography, La Jolla, United States
Modeling Ocean Circulation with Bayesian Inversion and Markov Chain Monte Carlo (303722)
Leah Rumancik, Geophysical Fluid Dynamics Institute, Tallahassee, FL, United States and Kevin G Speer, Department Scientific Computing, Geophysical Fluid Dynamics Institute, Florida State University, Tallahassee, United States
Data-adaptive harmonic analysis and prediction of sea level change in North Atlantic region (323090)
Dmitri A Kondrashov, University of California Los Angeles, Atmos. Sci, Los Angeles, CA, United States
Dynamically Consistent Reconstruction and Visualization of Monthly Ocean Temperature with 1-Degree Resolution since January 1950 by the Spectral Optimal Gridding Method (307355)
Samuel Shanpu Shen1, Gregory P Behm2, Y Tony Song3, Tangdong Qu4, Julien Pierret5, Tyler Tucker5 and Scott Knapp5, (1)San Diego State University, Department of Mathematics and Statistics, San Diego, CA, United States, (2)Lockheed Martin Aeronautics, CA, United States, (3)Jet Propulsion Laboratory, Pasadena, CA, United States, (4)University of California Los Angeles, Los Angeles, CA, United States, (5)San Diego State University, CA, United States
Study of the Global Ocean Features Using the Gap-Filled VIIRS Ocean Color Products (317900)
Xiaoming Liu and Menghua Wang, NOAA College Park, College Park, MD, United States
Global distribution of decorrelation length, signal to noise ratio and variability of temperature, salinity and density from Core Argo (310159)
Shigeki Hosoda, WPI-AIMEC, Yokohama, Japan and Kanako Sato, Japan Agency for Marine-Earth Science and Technology, Research Institute for Global Change, Yokosuka, Japan
Analyses of the global bias between MODIS infrared and AMSR-E microwave SSTs (323953)
Brahim Boussidi, University of Rhode Island, Narragansett, RI, United States, Peter C Cornillon, Univ Rhode Island, Physical Oceanography, Narragansett, United States, Gavino Puggioni, University of Rhode Island, Computer Science and Statistics, Kingston, RI, United States, Prabhani Kuruppumullage Don, University of Rhode Island, Computer science and statistics, Kingston, RI, United States and Chelle L Gentemann, Earth and Space Research, Seattle, WA, United States
How Realistic Is the MITgcm llc 2160  (326318)
Peter C Cornillon, University of Rhode Island, Physical Oceanography, Narragansett, United States, Nicholas Piskurich, University of Notre Dame, Environmental Engineering, Notre Dame, IN, United States, Baylor Fox-Kemper, Brown University, Department of Earth, Environmental and Planetary Sciences, Providence, RI, United States and Brodie Pearson, Oregon State University, College of Earth, Ocean, and Atmospheric Sciences, Corvallis, United States

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