Kayli Matsuyoshi

Kayli Matsuyoshi

she/her

Physical Oceanography PhD student studying air-sea interactions

Posts by Collection

portfolio

publications

Evaluation of Ocean Currents Observed from Autonomous Surface Vehicles

Published in Journal of Atmospheric and Oceanic Technology, 2023

Wave Gliders are instrument platforms that efficiently collect measurments of the upper ocean and lower atmosphere on the air-sea interface. Various motions at this interface, such as the oscillations of multiple different surface gravity waves, impact the orientation of these autonomous surface vehicles in space. As a result, the measurements of upper ocean currents from the platform-mounted Acoustic Doppler Current Profilers (ADCPs) must account for fluctuations in the platform’s orientation. Correcting for the Wave Glider’s motion on the sea surface with additional GPS/IMU position and attitude records ensured that the spatiotemporal evolution of currents was accurately represented. See more at https://doi.org/10.1175/JTECH-D-23-0066.1.

Recommended citation: Hodges, B. A., Grare, L., Greenwood, B., Matsuyoshi, K., Pizzo, N., Statom, N. M., Farrar, J. T., & Lenain, L. (2023). Evaluation of Ocean Currents Observed from Autonomous Surface Vehicles. Journal of Atmospheric and Oceanic Technology, 40(10), 1121-1136.

Machine Learning for Daily Forecasts of Arctic Sea Ice Motion: An Attribution Assessment of Model Predictive Skill

Published in Artificial Intelligence for the Earth Systems, 2023

The presence of sea ice affects planetary energy budgets and high-latitude ecosystems, playing a significant role in Earth’s system. Ice-Tethered Profilers (ITPs) are instrumented buoys that enable the collection of sea ice trajectories in the Arctic Ocean. Using this data and reanalysis products, three different machine learning and statistical models are employed to forecast velocities of sea ice and their performances are evaluated. See more at https://doi.org/10.1175/AIES-D-23-0004.1.

Recommended citation: Hoffman, L., Mazloff, M. R., Gille, S. T., Giglio, D., Bitz, C. M., Heimbach, P., & Matsuyoshi, K. (2023). Machine Learning for Daily Forecasts of Arctic Sea Ice Motion: An Attribution Assessment of Model Predictive Skill. Artificial Intelligence for the Earth Systems, 2(4), Article 230004, 230004.

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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