It is still beyond human beings' ability to fully comprehend the dynamical and radiative processes behind the climate change. With satellite remote sensing data, we are somewhat provided a chance to take a sneak peek of what a long-term, consistent and robust climate data record would look like. Satellites have kept sending back data from the space for decades, and these data tell stories about this blue planet we all dwell in.
"There's something, something about this place."
- Lady Gaga
As a PhD student at the department of earth and atmospheric sciences of Indiana University in Bloomington, I work with Dr. Staten on the topic of climate variability and clouds using satellite data from legacy instruments such as AVHRR and HIRS. We use a unique method to combine AVHRR cloud type radiance and HIRS spectral data together, in order to create a spectrally resolved dataset. An example of the data we're looking at is shown below. My previous research includes retrieval of optical thickness of maritime stratocumulus with satellite data, sensitivity study of properties that could influence the retrieval of smoke aerosol, radiative transfer models and processing and visualization of satellite data from multiple sensors (AVHRR, MODIS, VIIRS, OMI, CALIPSO, etc) flown on A-train satellites.
I enjoy using different programming tools to visualize my data. Previously I used IDL solely for both computational and visulizational purpose. Now I am gradually migrating my codes and scripts to Python to fully utilize the advantage of xarray to process netCDF files.
Correlation between Cirrus Cloud Frequency and MEI This map shows the correlation between cirrus cloud frequency (CF) measured by AVHRR on-board MetOp A satellite for the time period from 2009 to 2016 and multi-variate ENSO index (MEI). A clear positive correlation of cirrus CF in the tropical region indicates a increase of cirrus associated with El Nino episodes.
I am an associate instructor (AI) for
For Python workshop day 2, the Jupyter notes can be found here