OD53B:
Visualization, Statistics, and Model Validation of Big Data for Oceanography II


Session ID#: 36975

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:

Portable Immersive 3D Visualization of Large Gridded Datasets (323547)
Albert J Hermann, NOAA Pacific Marine Environmental Laboratory, Seattle, WA, United States
Pangeo: An Open-Source Platform for Big Data Climate Science (321028)
Ryan P Abernathey, Lamont -Doherty Earth Observatory of Columbia University, Palisades, NY, United States
Combining Argo floats, altimeter data and numerical model output to visualize 4-D eddy processes in the Southern Ocean (324743)
Uriel Zajaczkovski, WHOI, Physical Oceanography, Woods Hole, MA, United States, Sarah T Gille, Scripps Institution of Oceanography, La Jolla, CA, United States and Matthew R Mazloff, SIO, La Jolla, CA, United States
Bottom Mixed Layer Property Maps Using Machine Learning and Objective Mapping (306981)
Paige D Lavin, University of Washington, Oceanography, Seattle, WA, United States and Gregory C Johnson, NOAA Pacific Marine Environmental Laboratory, Seattle, WA, United States
Locally stationary spatio-temporal interpolation of Argo float data (303394)
Mikael Kuusela, Statistical and Applied Mathematical Sciences Institute, Durham, NC, United States; University of North Carolina at Chapel Hill, Department of Statistics and Operations Research, Chapel Hill, NC, United States and Michael Stein, University of Chicago, Chicago, IL, United States
Deep Bering Sea Circulation and Variability, 2001–2016, from Argo Data (303322)
Gregory C Johnson and Phyllis J Stabeno, NOAA Pacific Marine Environmental Laboratory, Seattle, WA, United States

See more of: Ocean Data Management