When Emily de Jong arrived at Caltech for her Ph.D. in 2019, she’d done internships in the oil industry and thought that she might pursue research modeling fluids in energy or the environment. Then she met environmental engineering professor Tapio Schneider.
She learned about Schneider’s work on Earth system modeling and, with Department of Energy Computational Science Graduate Fellowship (DOE CSGF) support, decided to explore how aerosols and cloud particles behave in the atmosphere. Her research path had quickly hopped from chemical to mechanical to environmental engineering, she says. “It seemed like an opportunity that I couldn’t and shouldn’t pass up.”
The fundamentals of studying cloud microphysics weren’t far from her original plan, but the atmospheric context was different. And it’s led her to grapple with difficult and messy questions in climate modeling. Ultimately she dove deep into one question: When do cloud droplets become rain droplets? “If you think about it logically,” she says, “it’s at the moment that they fall out of the sky.”
Models don’t keep track of droplets in that way. Instead, researchers create artificial parameters that can introduce uncertainty into climate models. So de Jong has worked on how to mathematically represent the size of droplets and how they collide and coalesce as they precipitate. “That requires thinking a little bit more creatively about what quantities we actually keep track of in our climate systems or in our large eddy simulations or weather models in order to accurately model those physics,” de Jong says.
She had completed her undergraduate degree in chemical engineering at Princeton University, where she took computer science courses alongside her major. For her first research experience, she contacted Princeton engineering professor Emily Carter, who simulates molecules and materials for energy applications. “That was definitely my first exposure to high-performance computing,” de Jong says. “And since then, I’ve always been interested in more of the theory that goes into how you actually design systems of physical equations to function on those HPC processes.”
When she started working in Schneider’s group, de Jong was excited by the idea of contributing to building a new climate model and thinking about the math involved. Schneider leads an interdisciplinary team of engineers, scientists, and applied and computational mathematicians at Caltech called the Climate Modeling Alliance (CliMA), whose goal is to refine climate predictions.
Later, de Jong grappled with fundamental climate questions, such as how carbon dioxide or aerosol emissions are changing climate.
More recently, she’s also examined the relationship between lightning and aerosols, which can yield insights about these same aerosol-emission questions. To wrap up her graduate work, she’s integrating a new mathematical representation of cloud microphysics into the full CliMA model and using it to run a large-scale atmospheric simulation.
During two DOE CSGF practicums at the National Renewable Energy Laboratory, de Jong used data and weather models to study low-level jets — atypical wind patterns — off the U.S. mid-Atlantic coast to better understand how they form. She’s also simulated how those features could interact with future wind turbines and affect their performance.
De Jong will defend her Ph.D. in June, and this fall she’ll continue her work on cloud microphysics while incorporating AI tools as a Lawrence Fellow at Lawrence Livermore National Laboratory.
Outside of her research, de Jong enjoys cycling, rock climbing and mountaineering. Her day-to-day work modeling microphysics and understanding weather has shaped her perspective as she bicycles up above a cloud layer or climbs a mountain. “Having some understanding of what’s going on there, why that happens, what’s going to happen to that cloud as the day goes on,” she says, “almost gives me a greater sense of control to have an understanding of what’s going on, even if it doesn’t actually help me get off the mountain any more safely.”
This article was adapted from an episode of the Science in Parallel podcast.
A Caltech fellowship recipient works on the physics underlying turbulence, or the chaotic gain of… Read More
A recent program alum interweaves large and small scales in wind-energy and ocean models. Read More
At UC Berkeley, a fellow applies machine learning to sharpen microscopy. Read More
A Cornell University fellowship recipient works on methods for ensuring software functions as expected. Read More
A UCSD engineering professor and former DOE CSGF recipient combines curiosity and diverse research experiences… Read More
A UT Austin-based fellow blends physics and advanced computing to reveal cosmic rays’ role in… Read More