OD34B:
Data Science for Modern Oceanography: Statistics, Machine Learning, Visualization, and More III Posters
Session ID#: 85203
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:
Statistics for Mapping Ocean Heat Content with Argo Floats: Modeling and Uncertainty Quantification (648701)
Mikael Kuusela, Carnegie Mellon University, Department of Statistics and Data Science, Pittsburgh, United States, Donata Giglio, University of Colorado Boulder, Department of Atmospheric and Oceanic Sciences, Boulder, United States, Anirban Mondal, Case Western Reserve University, Department of Mathematics, Applied Mathematics and Statistics, Cleveland, OH, United States and Michael Stein, University of Chicago, Department of Statistics, Chicago, IL, United States; Rutgers University, Department of Statistics, Piscataway, NJ, United States
Disentangling Complex Flows by Blurring the Data (656135)
Hussein Aluie, University of Rochester, Rochester, United States, Matthew W Hecht, Los Alamos Nat'l Lab, Los Alamos, NM, United States, Mathew E Maltrud, Los Alamos National Laboratory, Los Alamos, NM, United States, Shikhar Rai, Woods Hole Oceanographic Institution, Woods Hole, MA, United States, Mahmoud Sadek, University of Rochester, Mechanical Engineering, Rochester, NY, United States, Benjamin Aaron Storer, University of Waterloo, Applied Mathematics, Waterloo, ON, Canada and Geoffery K Vallis, University of Exeter, Exeter, United Kingdom
Data-driven stochastic parameterization of multi-scale flow interactions in ocean models (655541)
Dmitri A Kondrashov, University of California Los Angeles, Atmos. Sci, Los Angeles, CA, United States, Eugene Ryzhov, Imperial College London, Mathematics, United Kingdom, Pavel S Berloff, Imperial College London, London, SW7, United Kingdom and Agarwai Niraj, Imperial College London, United Kingdom
Reconstructing Subsurface Ocean Temperature and Salinity by the Reduced Space Objective Analysis With Dynamical Constraints (656720)
Alexey Kaplan, Lamont Doherty Earth Observatory, Palisades, NY, United States, Yochanan Kushnir, Columbia Univ, Palisades, NY, United States and Mark A Cane, Lamont-Doherty Earth Obs, Palisades, NY, United States
Characterization of Physical and Biogeochemical Variability around the Kerguelen Plateau and across the Southern Ocean Frontal Zones: A Machine Learning Approach (646784)
Isabella Rosso1, Matthew R Mazloff1, Lynne D Talley2, Sarah G Purkey3, Natalie M Freeman4 and Guillaume Maze5, (1)Scripps Institution of Oceanography, UC San Diego, La Jolla, CA, United States, (2)Scripps Institution of Oceanography, UC San Diego, UC San Diego, La Jolla, CA, United States, (3)Scripps Institution of Oceanography, University of California San Diego, San Diego, United States, (4)Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, United States, (5)IFREMER, Laboratory for Ocean Physics and Satellite remote sensing, Plouzané, France
Extracting nutrient - ocean state relationships from oceanic biogeochemistry simulations for macroalgae mariculture (651867)
Zhendong Cao1, Phillip J. Wolfram Jr1, Mathew E Maltrud1 and Riley Xavier Brady2, (1)Los Alamos National Laboratory, Los Alamos, NM, United States, (2)University of Colorado at Boulder, Department of Atmospheric and Oceanic Sciences, Boulder, CO, United States
Machine Learning (ML) enabled forecasts derived from Carbon, Silicate, and Nitrogen Ecosystem (CoSiNE) model generated climatology (646326)
Christopher Wood1, Richard W Gould Jr2, Bradley Penta3, Sergio DeRada3, Sean McCarthy2 and Gregory P Behm4, (1)United States, (2)US Naval Research Laboratory, Stennis Space Center, MS, United States, (3)Naval Research Laboratory, Stennis Space Center, MS, United States, (4)High Performance Computing Modernization Program, Productivity Enhancement, Technology Transfer, and Training (PETTT), Vicksburg, MS, United States
Responses of Marine Phytoplankton Communities to Environmental Changes: New Insights From a Niche Classification Scheme (640080)
Wupeng Xiao1, Edward A. Laws2, Yuyuan Xie3, Lei Wang4, Xin Liu1, Jixin Chen5, Bingzhang Chen6 and Bangqin Huang1, (1)Xiamen University, State Key Laboratory of Marine Environmental Science, Xiamen, China, (2)Louisiana State University, School of the Coast & Environment, Baton Rouge, LA, United States, (3)Xiamen University, China, (4)Xiamen University, State Key Lab of Marine Environmental Science, Xiamen, China, (5)Xiamen University, State Key Laboratory of Marine Environmental Sciences, Xiamen, China, (6)Japan Agency for Marine-Earth Science and Technology, RCGC, Yokohama, Japan
Predicting Shape of Dives of Southern Elephant Seals Using Regression Tree Models (645496)
Morgan Godard, Aix-Marseille University, Mediterranean Institute of Oceanography, Marseille, France, Claude Manté, MIO-AMU CNRS, Marseille, France, Christophe Guinet, Centre d’Etudes Biologiques de Chizé (CEBC), UMR 7372 Université de la Rochelle-CNRS, Villiers en Bois, France and David Nerini, Mediterranean Institute of Oceanography, Marseille, France
Deep Learning Algorithm Forecasts Shellfish Toxicity at Site Scales in Coastal Maine (637972)
Isabella Grasso1, Stephen D Archer2, Craig Burnell3, Kohl Kanwit4, Benjamin Tupper2 and Nicholas Record5,6, (1)Clarkson University, Department of Mathematics, Potsdam, NY, United States, (2)Bigelow Laboratory for Ocean Sciences, East Boothbay, ME, United States, (3)Bigelow Laboratory for Ocean Sciences, United States, (4)Maine Department of Marine Resources, United States, (5)Bigelow Lab for Ocean Sciences, Tandy Center for Ocean Forecasting, East Boothbay, United States, (6)Bigelow Laboratory for Ocean Sciences, East Boothbay, United States
