Climate Science Hackathon Winner: Team CAT
Preston Hinkle (School of Physical Sciences, Department of Physics and Astronomy):
Preston is a 5th year physics PhD student working in the Siwy lab, where he studies ion and particle transport in
nano- and microscale systems. His current research project involves studying cell deformations in confined
microfluidic flows. Although an avid experimentalist, Preston is most comfortable when writing code to run his
experiments or for data analysis. Preston has been an active participant in UCI Data Science Initiative activities
since its inception, and is currently an instructor for the Predictive Modeling with Python workshop. In 2016
Preston was named one of the Initiative's Summer Fellows for his proposal to develop an open-source software
library for analyzing his experimental data.
Crystal Yang (Physical Sciences, Chemistry):
Crystal is a 5 th year graduate student in chemistry working in the Siwy lab. On a day to day basis, she is trying
to save the world by using nanopores to desalinate water. In her free time, she enjoys rock climbing, hiking,
and OCR racing. In her other free time, she writes scripts and GUIs based on python to automate her research,
and enjoys diving into datasets to make aesthetically pleasing visualizations and interesting predictions.
Anna Kwa (Department of Physics & Astronomy, School of Physical Sciences):
I am a fifth-year Ph.D. student working with Prof. Manoj Kaplinghat and the astro-particle group in the
Department of Physics & Astronomy at the University of California, Irvine. I completed my undergraduate
degrees in Physics and Astronomy at the Ohio State University. My doctoral research focuses on using
astrophysical and cosmological observations to probe the particle nature of dark matter.
The NSIDC Reconstruction of Arctic sea ice is a massive data set of sea ice concentration measurements taken from
1850 to 2014. The data set consists of monthly sea ice measurements taken at regularly spaced longitude-latitude
coordinates centered on the North Pole, and is compiled from a wide variety of sources, from shipping reports to satellite
microwave data. The data is useful because it allows researchers to study both the seasonal variation and long term
trends in sea ice over the entire Arctic or in specific regions. However, due to the large size of the data set and the wide
range of times and locations contained within it, visualizing it can be difficult. In order to address this problem, we
developed a graphical user interface (GUI) program written in Python and PyQt that enables dynamic and interactive
visualization of the NSIDC data set. The program allows the user to quickly load and view a given month of
measurements from the data set, and pan and zoom to focus on specific regions. Additionally, the user can select
regions of interest to view a time-series of the average ice concentration local to that region at its annual high and low
points. The program could be deployed as an educational outreach tool that allows users to discover for themselves how
the Arctic's sea ice is changing in time. The program could also be used by researchers to rapidly discover trends in
Arctic sea ice that warrant a more in-depth study.