Grad Student Grossi Uses Artificial Intelligence to Map Ocean Flows
June 11, 2019
Our knowledge about ocean transport comes primarily from ocean circulation models that use field observations and theoretical motion equations to simulate ocean dynamics. Ocean models can depict large-scale circulation features accurately, but resolutions high enough to capture all scales of motion entail significant computational time and cost and are challenging or even impossible for most modern supercomputers.
Matt Grossi is developing an alternative approach that uses an artificial neural network algorithm, a type of artificial intelligence, to predict ocean transport based on information it automatically learns from field observations. This type of machine learning is considerably less computationally expensive than conventional circulation models, and Matt believes the network’s ability to digest data for skilled ocean forecasts will have many real-world applications, such as predicting oil dispersion in specific locations.
Matt is a meteorology and physical oceanography Ph.D. student with the University of Miami’s Rosenstiel School of Marine and Atmospheric Science and a GoMRI Scholar with Consortium for Advanced Research on Transport of Hydrocarbons in the Environment III (CARTHE-III).
Matt credits his physical oceanography path to an eighth-grade field trip to Cape Cod, Massachusetts, where his class spent four days learning about the Cape’s geology, fauna, flora, and maritime history. A trip activity asked students to measure the speed and direction of the Cape Cod Canal surface current using a tape measure, a stopwatch, and oranges. “We hadn’t grown up near the ocean, so we had no idea that the relentless spring wind ripping through the canal could make the water appear to flow in the opposite direction of the strong tidal current,” said Matt. “Imagine how surprised we were when we tossed our oranges into the water, waited for them to float past our stopwatch, and observed them floating in the ‘wrong’ direction!”