YoungHyun Koo
2023 Visiting Fellow Post-Doc

- PhD in Environmental Science and Engineering, University of Texas at San Antonio, 2023
- MS in Energy Systems Engineering, Seoul National University, 2019
- BS in Energy Resources Engineering, Seoul National University, 2017
Research Interests
I am a postdoctoral fellow collaborating with the National Snow and Ice Data Center (NSIDC) through CIRES. My research interest is the application of geographic information systems (GIS) and remote sensing to environmental monitoring in polar regions, including sea ice, icebergs, and ice sheets. Additionally, I am interested in using machine learning and deep learning techniques for such applications.
Current Research
In the Southern Ocean, the dynamic drift and deformation of sea ice play an important role in the distribution of sea ice mass balance, as well as the thermodynamic freezing and melting. However, the detailed mechanisms of the formation of pressure ridges or leads have not been fully understood due to the lack of high-resolution sea ice height measurement data. Recently, NASA’s ICESat-2 altimeter made it possible to capture the characteristics of individual sea ice deformation features and their regional statistics. Moreover, various satellite products, including optical images, passive microwave, and synthetic aperture radar (SAR), provide important sea ice information on both large and small scales.
Younghyun Koo will work with Dr. Walter Meier to combine these multiple satellite data and examine how atmospheric/oceanic forces and sea ice kinematics impact the formation of sea ice dynamic features. Using this remote sensing big data, he will also develop deep learning models to predict how the dynamic conditions of the Antarctic sea ice will change as the global climate changes.
Research Categories
Atmosphere, Cryosphere, OceansVisiting Fellow
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2026 -Machine-learning Models to Predict Dynamic Sea Ice Conditions
The two-year post-doctoral project is a study to find the statistical correlation between large-scale/small-scale sea ice kinetics and dynamic sea ice features, including ridges or leads. Based on this finding of statistical correlation, the post-doc will develop machine learning models to predict the dynamic sea ice conditions in the Southern Ocean. Consequently, this study will provide a crucial clue to understanding how polar ocean and sea ice conditions will change in the future in response to the dramatic climate change.Sponsors
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About CECA
CECA connects and creates a supportive environment for graduate students and postdocs who come from various academic units to do research in CIRES.