I am an Assistant Professor in the Department of Atmospheric and Oceanic Science at the University of Maryland. Prior to joining the University of Maryland I was a National Research Council postdoc fellow at the Hurricane Research Division of the NOAA Atlantic Oceanic and Meteorological Laboratory in Miami, FL. Before my position at HRD, I held an Advanced Study Program postdoc fellowship at the National Center for Atmospheric Research in Boulder, CO, where I worked jointly in the Mesoscale and Microscale Meteorology and Computational and Information Systems laboratories. I also worked at the NOAA National Severe Storms Laboratory in Norman, OK, under the support of the University of Oklahoma/NOAA Cooperative Institute for Mesoscale Meteorological Studies. I have a PhD in Meteorology from the Pennsylvania State University, and a BS in Meteorology and Applied Mathematics from Millersville University.
Environmental observing systems onboard satellites, aircrafts, and surface stations provide crucial measurements for characterizing current and past states of the Earth system (i.e., the atmosphere, hydrosphere, lithosphere, cryosphere, etc.). Data assimilation provides a means of combining these measurements with numerical models so that our physical knowledge of the system can be matched with evidence of the real world.
My research focuses on the development and application of advanced data assimilation techniques for geophysical research. Much of this work targets hazardous weather events that present major challenges for environmental prediction, such as tropical cyclones and severe convective storms. Despite the socioeconomic importance of these events, they are often associated with a high degree of forecast uncertainty. Therefore, the most honest prediction for hazardous weather—or any weather events for that matter—is a probabilistic one.
High-dimensional nonlinear systems such as Earth’s atmosphere, however, are incredibly challenging to represent probabilistically, even on very large supercomputers. This factor is just one of the many obstacles scientists face when trying to predict the weather. Limitations in numerical models, observation collection, and strategies for combining this information, also provide significant obstacles for achieving reliable probabilistic forecasts. For my research, I often work with meteorologists, modelers, observation collectors, and scientists in the uncertainty quantification community to help design state-of-the-art environmental prediction systems.
Current Projects and Support
Improving Convective-Scale Weather Prediction through Advanced Bayesian Filtering, Verification, and Uncertainty Quantification
NSF CAREER, Grant/Award Number: AGS1848363 (2019 – present)
This research examines fundamental challenges for the probabilistic prediction of severe convective storms using advanced Bayesian filtering methods based on particle filters. It targets long-term improvements for convective-scale weather prediction through advanced data assimilation, model verification, and multivariate probabilistic visualization techniques.
CISESS: Improving Hurricane Predictions Through Advanced Data Assimilation, Ensemble Forecasting, and Observing System Design
NOAA, Grant/Award Number NA19NES432000 (2019 – present)
This project supports the development of a proof-of-concept hurricane prediction system for testing design choices related to future modeling systems. Through this research, our group investigates obstacles for multi-scale weather prediction, while examining limitations of current operational modeling systems. This research also involves testing new developments for hurricane data assimilation methodology.
Accelerate the Development of the Hurricane Analysis and Forecasting System (HAFS)
NOAA, Grant/Award Number: NA20OAR4600281 (2020 – present)
This multi-institutional project aims to build the necessary data assimilation capabilities for the next-generation NOAA hurricane prediction system in the United States. My research group actively transitions advancements in data assimilation methodology into a modeling system that will soon be used operationally to produce timely warnings for landfalling hurricnanes. This research primarily targest methodology for improving the use of satellite and aircraft reconnaissance measurements.
CISESS: Advancing NOAA Earth System Modeling Efforts through Improvements in Model Physics and Sea Ice Data Assimilation
NOAA, Grant/Award Number NA19NES432000 (2021– present)
Research performed under this project will advance our current NOAA Global Forecast System (GFS) through improvments in sub-gridscale physical parameterization—namely cloud microphysics, planetary boundary layer physics, gravity-wave physics, and stochastic physics for probabilistic weather prediction. This project also supports the development of coupled sea-ice/ocean/atmospheric data assimilation capabilities, which target new advancements for seasonal environmental predictions. Both subgoals aim to develop different components of the NOAA Unified Forecast System (UFS).
Online uncertainty quantification for novel atmosphere measurements
NSF, Grant/Award Number: AGS2136969 (Beginning 2022)
This research investigates new ways to estimate uncertainty for novel environmental measurements. In this context, novel refers to measurements that provide unique information that is not easily validated with in situ measurements, such as radiance estimates from satellites. Other creative examples include pressure information from cell phone data, pictures of evolving cloud structure from cameras, and planetary boundary layer height inferred from dragonfly signatures on radar. The broad science goal is to target a fundamental limitation in earth system modeling, namely the errors assigned to observations used to constrain natural processes.