About

Biography

Jonathan Poterjoy is an Associate Professor and Graduate Program Director in the Department of Atmospheric and Oceanic Science at the University of Maryland, College Park, and an affiliate faculty member of the Applied Mathematics & Statistics, and Scientific Computation program. He is Editor of the American Meteorological Society journal Monthly Weather Review and an Associate Editor of the Quarterly Journal of the Royal Meteorological Society and the Journal of Advances in Modeling Earth Systems.

His research group develops data assimilation methods for Earth system prediction, connecting data assimilation theory with large-scale applications through ensemble Kalman filters, variational and hybrid methods, particle filters, and machine learning for state and parameter estimation. He introduced the local particle filter, a non-Gaussian ensemble method now available in NSF NCAR's Data Assimilation Research Testbed and being brought into the JEDI framework used by NOAA and NASA. The group applies these methods to global and mesoscale numerical weather prediction, hurricanes, and sea ice, working with scientists at NOAA, NASA, and NSF NCAR. He mentors graduate students and early-career scientists in his group and through the CADRE data assimilation consortium.

He received a Ph.D. in Meteorology from the Pennsylvania State University, specializing in ensemble data assimilation, and a B.S. in Meteorology and Applied Mathematics from Millersville University. Before joining Maryland in 2018, he was an Advanced Study Program postdoctoral fellow at the National Center for Atmospheric Research, a postdoctoral research associate with the Cooperative Institute for Mesoscale Meteorological Studies at NOAA's National Severe Storms Laboratory, and a National Research Council postdoctoral fellow at NOAA's Atlantic Oceanographic and Meteorological Laboratory. He received a National Science Foundation CAREER Award in 2019.

Areas of expertise

• Data assimilation
• Ensemble data assimilation and ensemble Kalman filters
• Particle filters and non-Gaussian (Bayesian) data assimilation
• Hybrid ensemble-variational data assimilation (EnVar, 4DEnVar, 4DVar)
• Numerical weather prediction: global, regional, and convection-allowing models
• Hurricane and tropical cyclone data assimilation and prediction
• Radar and satellite radiance data assimilation
• Sea ice and polar data assimilation
• Uncertainty quantification and observation error estimation
• Machine learning for data assimilation: kernel methods and generative models
• Observing system simulation experiments (OSSEs) and observing system design
• NOAA Unified Forecast System (UFS), GFS, and HAFS; JEDI; NSF NCAR DART; WRF