Our climate is affected by turbulent oceanic flows where energy is being continuously transferred across a wide range of scales -- quantitative understanding of these processes remains one of the big challenges in oceanography. My research resides at a synergetic overlap between geophysical fluid dynamics, physical oceanography, and climate dynamics. I am leading the Ocean Dynamics group at the University of Washington that works on mesoscale and submesoscale ocean turbulence, sea ice-ocean interactions, floe-scale sea ice modeling, laboratory experiments, remote sensing, and applications of Deep Learning to satellite oceanography and ocean turbulence. Our approach is to gain fundamental knowledge by reducing complex phenomena to their driving mechanisms and developing mathematical models to quantify and predict their evolution. Feel free to contact me about potential student and postdoctoral opportunities.
If you are an undergraduate student interested in data science and oceanography, check out our undergraduate summer program, Data Science in Oceanography.
I'm a high school student at Skyline High School in Sammamish, WA. I'm working on various science communication projects within the Ocean Dynamics research group at UW. My latest project involves sea ice modeling using the floe-scale model SubZero.
I'm an undergraduate student exploring the role of sea ice rheology in the inertial oscillations of sea ice floes observed with ice-tethered profiles in the Arctic Ocean.
I'm a graduate student exploring mesoscale and submesoscale ocean turbulence using deep learning. My research focuses on using deep learning to more accurately reconstruct gridded sea surface height maps from sparse along-track satellite altimeter observations. By synthesizing approaches from physics and deep learning, we hope to learn new lessons about the turbulent dynamics of the upper ocean.
I am an oceanographer and NOAA Climate & Global Change postdoctoral fellow with Georgy Manucharyan at UW and Andy Thompson at Caltech. My research uses a combination of observations, numerical models, and remote sensing to investigate the role of the ocean in the climate system. I am particularly interested in Southern Ocean dynamics, including ice-ocean interactions, air-sea fluxes, physical controls on biogeochemistry, large-scale circulation, and submesoscale variability.
I'm a postdoctoral scholar. My research utilizes applied mathematics to investigate the underlying physics of arctic sea ice. I am working on a sea ice model utilizing a discrete element method to resolve interactions and phenomena at the floe scale. Additionally, my research will implement asymptotic techniques to explore the coupling of interactions between the disparate length scales exhibited by sea ice.
Our group is using Deep Learning to synthesize different satellite observations and develop a global SSH product with unprecedented accuracy.
First AI-generated global SSH map. The future is here!
Satellite observations of sea surface height (SSH) are widely used to derive surface ocean currents on a global scale. However, due to gaps in SSH observations, it remains challenging to retrieve the dynamics of rapidly evolving upper-ocean currents. To overcome this limitation, we propose a Deep Learning framework that is based on pattern recognition extracted from SSH observations. Using synthetic data generated from a simplified model of ocean turbulence, we demonstrate that deep learning can accurately estimate both surface and sub-surface ocean currents, significantly outperforming the most commonly used techniques. By providing a proof of concept, our study highlights the strong potential of deep learning for estimating ocean currents from satellite observations.
Sea ice dynamics is a topic of continuing debate, particularly at scales of motion that are comparable to sea ice floes. We are developing from scratch a sea ice model that is based on an explicit representation of the floe lifecycle, including the fractures, welding, formation, etc. The goal is to have a model that could bridge the gap between the floe scale and the basic scale sea ice dynamics.
Exploring the role of small islands in sea ice propagation through Nares Strait.
(left) Simulation without the islands.  Simulation with the islands (right).
Localized and episodic fluxes due to submesoscale oceanic turbulence play an important role in larger scale dynamics and affect biogeochemical cycles via vertical advection of nutrients. We are exploring submesoscale variability in the Arctic Ocean, particularly in marginal and seasonal ice zones, where active mechanical and thermodynamical eddy interactions with overlying sea ice can affect its growth/melt rates.
Mesoscale and submesoscale eddies in marginal ice zones in the Arctic Ocean as simulated by the LLC4320 ocean model.
Submesoscale eddies revealed by sea ice patterns in marginal ice zones
Arctic Ocean stores a large amount of freshwater within the Beaufort Gyre. The freshwater accumulation was linked to changing atmospheric winds, which can cause a release of the accumulated fresh waters with global climate implications. Nonetheless, the basic gyre dynamics are not well understood and it is unclear how much freshwater can be held in under persistent forcing and how quickly it can be released. Combining theory with numerical simulations, we work to explore dominant physical processes governing the FWC dynamics, focusing on the interactions between eddies, mean flow, and sea ice. It is the interplay between these processes that defines the gyre stability and affects its variability.
Eddies are commonly observed in the Arctic
Ocean and can affect the melting of sea ice due to increased heat fluxes associated with enhanced boundary layer turbulence. However, the nature of mesoscale and submesoscale variability in the Arctic Ocean is not well understood. We use idealized models and high-resolution simulations along with in situ observations to explore the dynamics of outcropping density fronts and identify factors influencing the formation of coherent eddies.