Past Research

Note: I am still working on filling in content here. The current research page has gotten priority at the moment!

This page is some additional information about topics of research I have explored in the past.

TL;DR: I started my research career studying ozone transport in the Amazon, went on to study radar retrievals of snow and then focused on machine learning methods meteorological tasks.

Machine Learning for Meteorology (Postdoc)

2021 - 2023

Machine learning within Meteorology journals has been growing at an exponetial rate.

After dabbling with machine learning for my PhD, I went on to do machine learning as the focus of my research as a postdoc at the University of Oklahoma (remotely) working with Dr. Amy McGovern. My first goal while at OU was to write two plain language

Global Precipitation Measurement mission snowfall (PhD)

2018 - 2021

The GPM satellite preparing for launch (source)

During my masters a paper came out by Gail Skofronick-Jackson showing the Global Precipitation Measurement (GPM) mission’s estimates of snowfall globally was vastly underpredicting what CloudSat was measuring. Seeing this, my advisers and I thought it would be good to investigate why GPM’s snowfall was so much smaller than CloudSat. My PhD then can be grouped into 3 parts (and 3 papers):

1) Why is the current radar retrieval of snowfall deficient?
2) Can we create an alternate retrieval that improves snowfall estimates?
3) How well does the new alternate retrieval work compared to CloudSat?

Triple Frequency Radar Observations (MS)

2016 - 2018

APR-2 courtesy of NASA

For my masters work, I was hired to work on analyzing NASA field campaign data that was collected for the Global Precipitation Measurement Mission. One insturment that was used during these campaigns was the Airborne Precipitation Radar (pictured above). One especially novel thing from these field camapigns was that this radar observed clouds at 3 wavelengths, all collocated. These 3 wavelengths were Ku-, Ka- and W-band. Having 3 different wavelengths helps constrain our retrievals of things like snow amounts, snow particle size etc.

This was the first time that an airborne platform used these 3 frequencies together, and previous ground based efforts showed the 3 frequencies could help determine snow habit, i.e., aggregate vs rimed particle. What I did was explore how the new airborne data compared to the ground based work, and also include some coincident in-situ observations from a second aircraft.

Figure 2 from my first paper showing an example of the triple-frequency obs and the in situ data

What we found in my first peer-review publication (Chase et al. 2018) that these new airborne observations compare well to the previous ground based efforts. More specifically, the dual-frequency ratio space (differences between radar frequencies) can seperate particle effective size and effective density (i.e., seperate aggregates vs graupel).

Ozone transport by storms in the Amazon (BS)

2014 - 2015

image from GO AMAZON 2014

My first topic of research was with the transport of ozone within storms over the Amazon rainforest. This was when I participated in a Research Experience for Undergraduates at Penn State University where I worked with Dr. Jose Fuentes.

The general idea was that the rainforest is a natural sink for ozone, so ozone levels often were near 0 ppbv. When a storm forms and with it a downdraft, this downdraft would mix ozone from the free troposphere into the boundry layer. The largest example is shown in the plot above (from my poster). This example shows an increase of ozone from 10 to 50 ppbv.

This work culminated in posters at the 2014 and 2015 AMS annual meetings.