I have a B.S. in Physics with a minor in Mathematics from
Pennsylvania State University . After my undergrad,
I spent several years working odd jobs, including in high-end kitchens and environmental chemical testing
labs, before joining my PhD program in 2019.
On the weekends, you can find me hiking, biking, trail running, or otherwise enjoying the outdoors.
I have a passion for cooking, and wish to travel and taste cuisines from all around the world.
I love electronic music, hip hop, and jazz fusion, and enjoy craft beers and dry red wines.
The next time you're in a virtual meeting with me, look out for my cats
Maya and Maggi, who love interrupting their papa's work.
Download CV (coming soon)
Research
Tropical cyclones are particularly difficult to model due to a number of factors, including the
complex physical processes between ocean and atmosphere, which are not wholly understood
and therefore difficult to model; the scale of these storm systems; as well as the unpredictable
nature of rapid intensification.
These challenges are compounded by the nature of tropical cyclone formation and evolution
over remote areas of the Atlantic and Pacific oceans, for which we have very few in-situ
measurements. As a result, numerical weather models rely heavily on high resolution microwave
and infrared radiance measurements, which are collected via satellite instruments, such as the
advanced microwave sounding unit aboard NOAA polar-orbiting satellites.
However, a significant challenge remains: observation error. Satellite instruments do not directly
observe atmospheric variables, instead collecting data on electromagnetic energy emitted from
Earth's surface and atmosphere from which useful variables such as temperature and humidity
are derived from.This derivation requires a radiative transfer model, and is highly complex and
can introduce complicated observation errors. Furthermore, observation errors can arise from
other sources, such as improper calibration of the instrument itself.
Track Error Verification, Control (a) vs Online Bias Correction (b), for AL20, Hurricane Teddy. The blue lines represent forecasts generated early in the model time span, transitioning to red as forecast time progresses. Black represents the best track estimate.
As a result, radiance measurements must be corrected of biases prior to assimilation into
numerical weather models. Since these biases depend on specific weather conditions, we can
produce statistical models that are constructed based on our physical knowledge of these bias
sources. In this way, we are able to leverage our imperfect knowledge of atmospheric conditions
to form a relatively accurate estimate of bias.
That being said, Knisely and Poterjoy (2023)
show that the effectiveness of these bias correction methods
varies widely depending on the weather model they are trained on. It also
highlights the importance of uninterrupted basin-wide data assimilation methodologies,
as opposed to more limited, ad-hoc model initialization steps common in operational
hurricane models.
This work serves as both a proof of concept and a motivation for ongoing research exploring
continuous basin-wide data assimilation with high resolution ensembles with HAFS. With this
research, we investigate new
ensemble data assimilation techniques that aim to transition HAFS into a fully probabilistic
prediction system. Ultimately, we seek to understand how these methods can improve tropical
cyclone forecasting, as well as how they may synergize with the bias correction methodology
outlined by Knisely and Poterjoy (2023).
Teaching
I have been a TA for AOSC 200 (Weather and Climate) for Dr. Tim Canty and taught AOSC 201 (Weather and Climate Lab)
for three semesters (Fall 2019, Spring 2020, Fall 2020).
