Opportunities & My Interests

Student opportunities in Fall 2024

I would like to welcome incoming graduate student Yuhong Ye into my lab beginning Fall 204. I would also like to welcome Weichen Liu, a new postdoctoral research associate! Please contact me regarding forthcoming opportunities to join our lab!

5-year interests

Environmental change is already creating huge ripples across financial markets and wreaking havoc to ecosystems and human systems. Further, climate change is disproportionately affecting disadvantaged communities. Studying the atmospherics of climate change is hampered by the low resolution of Global Climate Models (GCMs), the principle tools used to project future changes in the earth system, due to their coarse grid spacings (> 100 km). Their outputs thus cannot currently provide precipitation and temperature projections on decision-relevant scales (i.e., resolutions on which local-scale terrain modulates the weather, climate, and its change).


I use Regional Climate Models (RCMs) to address the inadequate grid spacing problem in a process referred to as dynamical downscaling. In this process, I embed a RCM within a GCM across a limited area of the planet to focus the computer power across a select region. Inside this area, coastlines and topography, as well as their overlying atmospheric and land-surface phenomena can be more realistically resolved (grid spacings < 10 km). This process however is an art form of sorts, so I spend a great deal of my time (with collaborators ) to impove this process for applications towards quantifying climate risk.


Over the next 5 years, I aim to apply this technique to develop an ensemble of dynamically downscaled GCMs across the conterminous United States (CONUS) in coordination with partners at Wyoming, UCLA, NCAR, and the Department of Energy, tackling issues of technique and quality relating to:


I am also working with colleages to enlarge our ensemble for select variables of community interest (e.g., precipitation and temperature)  using artificial intelligence. Large ensembles of downscaled GCM projections are needed in order to meaningfully distinguish the random extreme weather events from those emerging due to climate change.