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.
Ph.D. - Physical Oceanography
Columbia University in the City of New York, USA
Summer School - Turbulence Theory in Climate Dynamics
École de Physique des Houches, France
B.E. - Ocean Engineering
The University of Tokyo, Japan
Balwada, D., W. Chen, J. C. Ohlmann, T. Uchida, R. Abernathey. Velocity Structure Functions in California’s Coastal Seas from Surface Drifters. 2019.
Uchida, T., R. Abernathey, G. McKinley, S. Smith, D. Balwada and M. Levy. Seasonality of eddy iron fluxes in the Southern Ocean and its impact on primary production. NHOM-Brest: Workshop on Non-Hydrostatic Ocean Modeling. October 2018. Brest, France.
Uchida, T., R. Abernathey and S. Smith. The global seasonal cycle of mixed layer instability in a GCM. 21st Conference on Atmospheric and Oceanic Fluid Dynamics 19th Conference on Middle Atmosphere. June 2017. Portland, USA.
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 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 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 is a Python package for linear quasigeostrophic normal mode analysis given the background state of velocity and density profile.
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.