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About me

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Publications

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Research interests

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About me

I have a strong interest in the seasonal cycle of (sub)mesoscale turbulence in the ocean and how it interacts with the large-scale ocean circulation and bioproductivity particularly in the Southern Ocean. I approach the problems by analyzing big data outputs from general circulation models, idealized numerical simulations and satellite observations.

Education

PhD candidate - Physical Oceanography
Columbia University in the City of New York, USA
2014-2019 (projected)

Summer School - Turbulence Theory in Climate Dynamics
École de Physique des Houches, France
August 2017

B.E. - Ocean Engineering
The University of Tokyo, Japan
2010-2014

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Big-data Oceanography

Bioproductivity in the Southern Ocean
I am interested in the impact of eddy fluxes on the transport of momentum and tracers such as carbon and nutrients, and how this affect the bioproductivity in the Southern Ocean. The Southern Ocean is know as one of the high-nutrient low-chlorophyll zones, with the limiting nutrient being iron. This makes the biological pump of carbon in the region very sensitive to influx of iron, yet our insights into the pathways of iron are limited. My interest has been to quantify the relative impact of supply by the ocean dynamics. Below is a list of packages I have developed and/or contributed to for my analysis.

xrft
xrft is a Python package for taking the discrete Fourier transform (DFT) on xarray and dask arrays. It keeps the metadata of the original dataset and provides a clean work flow of DFT. Contributed to developing the functions for detrending the data and calculating the (isotropic) power/cross spectrum.

xomega
xomega is a Python package for inverting the generalized Omega equation given the right-hand side of the equation. It solved the inversion in Fourier space and provides an efficient work flow.

oceanmodes
oceanmodes is a Python package for linear quasigeostrophic normal mode analysis given the background state of velocity and density profile.

xregrid
xregrid is a Python package for aggregating and/or regridding data onto a orthogonal grid using the KDTree algorithm. xregrid is motivated by the fact that non-orthogonal grids are becoming increasingly common in general circulation models. In order to conduct physically meaningful analysis on the model outputs and compare them with satellite observations, however, we are in need of regridding the data onto orthogonal grids.

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