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 spent some time 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, surface stations, etc. 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 studying geophysical problems. 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 dynamical meteorologists, modelers, observation collectors, and scientists in the uncertainty quantification community to help design state-of-the-art environmental prediction systems.