OD44C:
Data Science for Modern Oceanography: Statistics, Machine Learning, Visualization, and More IV Posters
Session ID#: 92500
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):
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: 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
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:
Metrics and Citations for Data and Software (654979)
Jessica Hausman1, Lewis John McGibbney2, Suresh Vannan1, Sara Bond1 and Dudee Chiang3, (1)Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States, (2)NASA Jet Propulsion Laboratory, Pasadena, CA, United States, (3)Jet Propulsion Laboratory, United States
CF Conventions for netCDF: Support for Data Access, Analysis, and Visualization (655315)
Ethan Davis, University Corporation for Atmospheric Research, Boulder, United States, Guilherme P Castelao, Scripps Institution of Oceanography, La Jolla, CA, United States, David Hassell, National Centre for Atmospheric Science, Reading, United Kingdom, Jessica Hausman, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States, Aleksandar Jelenak, HDF Group, Champaign, IL, United States, Daniel Lee, EUMETSAT, Darmstadt, Germany and Kevin O'Brien, NOAA/PMEL UW Cooperative Institute for Climate, Ocean, and Ecosystem Studies, Seattle, United States
The CIGOM Data Management portal (650044)
Favio Medrano and Argelia Ronquillo, Center for Scientific Research and Higher Education at Ensenada, Ensenada, BJ, Mexico
Near real-time data management of multiplatform autonomous vehicles fleet and integration with multi-source heterogeneous data in a web-based visualization tool for data-sharing, dissemination, and operational purposes (650837)
Rubén Marrero1, Tania Morales1, Rayco Moran1, Eduardo Caudet1, Patricia Rivera1, Carlos Barrera2, Andres Cianca1 and Octavio Llinas1, (1)Oceanic Platform of the Canary Islands, Telde, Spain, (2)Oceanic Platform of the Canary Islands, Underwater Vehicles, Telde, Spain
Visualizing oceanic and atmospheric fields on a tile based web map: a new gridded product comparison tool in the Argovis web application (657737)
Tyler Tucker, University of Colorado, Department of Atmospheric and Oceanic sciences, Boulder, CO, United States, Donata Giglio, University of Colorado Boulder, Department of Atmospheric and Oceanic Sciences, Boulder, United States and Megan Scanderbeg, Scripps Institution of Oceanography, La Jolla, United States
PyEcholab: Processing Water Column Echosounder Data in the Cloud (648084)
Veronica Martinez, University of Colorado Boulder, Boulder, CO, United States; University of Colorado Boulder, CIRES, Boulder, CO, United States, Charles Anderson, Cooperative Institute for Research in Environmental Sciences, Boulder, CO, United States, Carrie Wall, University of Colorado at Boulder, NOAA NCEI, Boulder, United States, Rick Towler, NOAA Alaska Fisheries Science Center, Seattle, WA, United States, George Cutter, NOAA Southwest Fisheries Science Center, Antarctic Ecosystem Research Division, La Jolla, CA, United States and Josef Michael Jech, NOAA Northeast Fisheries Science Center, Woods Hole, United States
EchoFish - Visualizing water column sonar data (652191)
Carrie Wall1, Chris Slater2, Rudy Klucik2, Charles Anderson2 and Veronica Martinez3, (1)University of Colorado at Boulder, Cooperative Institute for Research in Environmental Sciences, Boulder, United States, (2)Cooperative Institute for Research in Environmental Sciences, Boulder, CO, United States, (3)Cooperative Institute for Research in Environmental Sciences, Boulder, United States
The NOAA/NCEI Data Assembly Center for NOAA/OMAO’s ship and aircraft fleet (636426)
Chris R Paver, NOAA National Environmental Satellite, Data, and Information Service, Silver Spring, MD, United States, Solomon Tadele, NOAA Office of Marine and Aviation Operations, Silver Spring, MD, United States and Fred Katz, NOAA National Environmental Satellite, Data, and Information Service, Silver Spring, United States
GOANA, a Global Ocean Atlas, Neutrally Averaged (649707)
Paul M Barker, University of New South Wales, School of Mathematics and Statistics, Sydney, NSW, Australia and Trevor J McDougall, University of New South Wales, Sydney, NSW, Australia
A Synthetic Ensemble of Global Ocean Chlorophyll Concentration (647946)
Geneviève Elsworth, NASA Goddard Space Flight Center, Global Modeling and Assimilation Office, Greenbelt, United States, Nicole S Lovenduski, University of Colorado, Department of Atmospheric and Oceanic Sciences, Boulder, CO, United States, Karen A McKinnon, University of California Los Angeles, Departments of Statistics, Institute of the Environment and Sustainability, Los Angeles, CA, United States and Riley Xavier Brady, University of Colorado at Boulder, Department of Atmospheric and Oceanic Sciences, Boulder, CO, United States
Using machine learning to improve operational wave forecasts (648627)
Jeff Hansen, University of Western Australia, Crawley, WA, Australia, Chen Wu, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China, Phil Watson, The University of Western Australia, Perth, WA, Australia and Diana Jane Greenslade, Bureau of Meteorology, Melbourne, Australia; Bureau of Meteorology, Melbourne, VIC, Australia
A 2DVAR Blending Method for CYGNSS Wind Speed Observations (645092)
Xiaochun Wang, University of California Los Angeles, Los Angeles, CA, United States, Zhijin Li, JPL, Pasadena, CA, United States, Yuchan Yi, The Ohio State University, Division of Geodetic Science, School of Earth Sciences, Columbus, OH, United States, C.K. Shum, The Ohio State University, Division of Geodetic Science, School of Earth Sciences, Columbus, United States and Joel T Johnson, Ohio State University Main Campus, Department of Electrical and Computer Enginneering, Columbus, United States
