(I think I talk enough about my most interesting projects in other areas of this application, so I will not repeat them here)
Before joining the University of Michigan, I spent all of my research career studying meteorology, specifically severe and tornadic thunderstorms. A majority of my research was done at at the storm-scale, or on a scale of 50-100m, and that is the resolution at which I modeled and observed these atmospheric phenomena. My dissertation involved creating a high-resolution ensemble of idealized supercells (thunderstorms with a rotating updraft) and using ensemble sensitivity analysis to identify why some storms produced tornadoes, and why some did not, even though they were initialized with virtually the same environement. While I am still very interested in severe thunderstorm dynamics (and storm chasing was always a thrill!), my heart was drawn back to the Great Lakes Region, which home for me and my family. After getting a Masters and Ph.D. at Texas Tech University, I accepted a postdoctoral position with University of Michigan's Cooperative Institute for Great Lakes Research (CIGLR), where I configured a Regional Climate Model (RCM) for optimum performance in the Great Lakes Region (GLR). During my two years as a postdoc, I learned a lot about the importance of modeling the atmosphere and the Great Lakes in a coupled-capacity at long time scales. The lakes and the atmosphere interact with feedbacks that go both ways, and it is becoming apparent that a climate model that does not include a lake model will be unreliable for future projections of climate in our region. In December of 2023, I was hired to be an assistant research scientist for CIGLR, where I began my appointment with SEAS and continue to conduct atmospheric research for the Great Lakes Region.
My goals are two-fold: I want to help make all Great Lakes-related climate and weather research data centralized and easily (and freely) accessible to scientists, researchers, and decision-makers in the Great Lakes region, and I want to contribute to that by creating datasets of significant past and future events that can be references to better understand the range of weather and climate extremes impacting the region.
AI/ML has been making its way into atmospheric sciences and meteorology for quite a while. In fact, AI/ML methods are now being incorporated into tools used by operational forecasters to better predict the probability of extreme hazards throughout the country. I am excited about AI in my data science because I can learn from previous studies and applications of AI on near-term and past weather to look forward and use it to better understand future climate uncertainties. That, and I am excited to collaborate with my colleagues who are experts in AI/ML so we can create valuable research be combining our areas of expertise.
– I spent every summer of my graduate education operating a mobile radar with the goal of observing supercell thunderstorms and tornadoes, which meant being a part of a large, interdisciplinary armada of vehicles on the road in the open land of the Great Plains for over a month at a time. It was thrilling, but more often it was hot, sweaty, and punctuated with gas-station dinners.
– I believe some of our most valuable work is the least glamorous. Specifically, it is very important to compare models, gridded reanalyses, and even gridded observed datasets, to other in situ observations of our atmosphere. In the study of atmospheric sciences, gridded models and analyses are often treated as "truth", when the reality is that every dataset has its errors and biases.
– I am a mother of two young girls, and they absolutely refuse to sit still. I am exhausted and proud at all times.