Idealized Mesoscale Dissipation

Simulation of mesoscale energy propagation and dissipation at a western boundary.

In this published work, I investigate the dissipation of oceanic mesoscale energy at western boundaries. I am interested in understanding how the westward-propagating mesoscale energy being dissipated in the global ocean given that eddies are ubiquitous but have nonlinearity of three-order-of magnitude in latitudes. Keypoints:

  • Bottom drag dominates viscous dissipation when eddies are nonlinear, and the open-ocean region dissipates most of the total kinetic energy.
  • Western boundary might be important for mesoscale dissipation only when energy generation is nearby since eddies are fully nonlinear in mid and mid-high latitudes.
  • We need to investigate other dissipation mechanisms in the presence of rough bottom topography.

Double gyre simulation of mesoscale energy dissipation.

Next, we use a double-gyre simulation to investigate more dissipation mechanisms, such as eddy killing by wind, eddy-mean interaction, and the effect of rough bottom topography.

Ocean Kinetic Energy Budget

Daily bottom eddy kinetic energy from GLORYS12, a global ocean reanalysis dataset.

This work aims to provide an accurate update of global bottom drag dissipation rate that is one of the pathways to dissipate oceanic mesoscale energy (Ferrari and Wunsch, 2009). Keypoints:

  • The first time using satellite and Argo datasets to estimate global near-seafloor EKE and drag dissipation rates.
  • Compute eddy vertical structures from GLORYS12 dataset (Jean-Michel et al., 2021) using my thickness-weighted EOF tool available at EOF_vertical.
  • Global bottom drag dissipation rate is $0.19\ \mathrm{TW}$, which is too small to dissipate the $1\ \mathrm{TW}$ of wind input to geostrophic flow. This work is under preparation for a GRL publication, and stay tuned for the preprint.

Machine Learning on Ocean Circulation

Predicted bottom flow speed from surface flow

My goal is to integrate machine learning and neural networks with high-speed computing to resolve and predict complex ocean physics that were previously intractable. In this mentoring project, Gabrielle Barnes (July 2026 - present) and Aisha Mardini (May 2025 - June 2026) aim to use GLORYS12 dataset to train the models for global bottom flow prediction. This work is supported by NSF ACCESS Program under my lead (EES250071: Using Machine Learning and Satellite Measurement to Estimate Global Bottom Drag Dissipation”).

Nearshore Research

sand coverage in Taoyuan coast Erotion in Taoyuan coast and reef exposure. Source: AGU 2019 poster presentation

I started up a team for monitoring tidal-zone sand coverage in Taoyuan coastlie, located in Taiwan. I developed an effective operating system for this research team which has successfully continued this project, in part due to the training practices I helped develop. This investigation workflow enabled the local government and contractors managing a billion-dollar coastal project to monitor nearshore dynamics and assess construction-related environmental impacts.

uav research UAV LiDAR research

During my undergraduate and master research, I contributed to the UAV LiDAR system development (Hunag et al., 2018). This autonomous system can measure waves, tides, wave energy dissipation (published in 2020 ICCE conference proceedings 10.9753/icce.v36v.waves.34, and presented in this video), and the roughness of the land surface at centimenter scale.

fishing weir investigation Archaeological landscape: fishing weir investigation. Source: COAST Lab

In addition to the nearshore physical dynamics research, I participated in the archaeological landscape investigation in Xinwu, Taiwan, which uses airborne imaging techniques and hydrographic measurements to understand the fishing weir sites and their relationship with tidal dynamics. As shown in the aerial image above, stone tidal weirs are built parallel to the shoreline and aligned perpendicular to local wave and tidal propagation to maximize fishing yields.