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Oceanography
Latest Past Events
Seminar: An Acoustic Ray Tracing Optimization Approach for Tomographic Assimilation
Holmes 247 2540 Dole Street, HonoluluPrajna Jandial Graduate Student Department of Ocean and Resources Engineering, University of Hawai’i at Mānoa, Honolulu, HI *In-person (HOLM 247) & via zoom* Zoom link Meeting ID: 963 5962 3640 Passcode: OREseminar https://hawaii.zoom.us/j/96359623640 Acoustic ray tracing is used in oceanographic tomographic assimilation, where the goal is to infer ocean states using acoustic travel times. Acoustic ray paths are highly sensitive to fluctuations in ocean conditions, which are dynamic and challenging to predict. Achieving more accurate assimilation of experimental data into models requires iterative recalculation of ray paths to ensure modeled and observed travel times have minimal discrepancies. To accelerate the
Seminar: Observing the ocean acoustically with the Kauai Beacon source
ORE ZoomKay (Kai) Gemba, Ph.D. Associate Professor, Department of Physics Naval Postgraduate School, Monterey, CA, USA *Zoom only* Meeting ID: 963 5962 3640 Passcode: OREseminar https://hawaii.zoom.us/j/96359623640 The 75 Hz center-frequency Kauai-Beacon Source is well-situated for observing the North Pacific Ocean acoustically, and ongoing efforts enable transmissions and analysis of broadband signals for this 18-month duration time-series. Previously, we demonstrated acoustic receiving along paths to Wake Island (∼3500 km), Monterey Bay, and OOI near Oregon (∼4000 km). Travel time observations to Wake Island compare well with HYCOM reanalysis data in 2023, but the model does not capture a continuous warming trend observed
Seminar: Innovative, Interdisciplinary Tools to Study Marine Animals in Our Changing Ocean
POST 723 1680 East-West Road, HonoluluCamille Pagniello, Ph.D. Assistant Researcher & SOEST Early Career Fellow Hawai’i Institute of Marine Biology University of Hawai‘i at Mānoa Although an abundance of ocean data has been gathered since the year 2000, the ocean remains under-sampled. In many instances, there is still a scarcity of data at any one location and time. However, the rise of small, and efficient microcontrollers and single-board computers has catalyzed the development of low-cost, open-source, “do-it-yourself” ocean observing technologies. Combined with advances in data science, signal processing and machine learning, there is a real opportunity to move beyond traditional and expensive oceanographic instruments and