Assistant Research Professor, Department of Atmospheric and Oceanic Science, University of Maryland, College Park

Visiting Scientist at the National Centers for Environmental Prediction (NCEP), NOAA Center for Weather and Climate Prediction, College Park, MD, USA

Visiting Scientist at the RIKEN Advanced Institute for Computational Science (AICS), Kobe, Japan
Prof. Stephen G. Penny
Harrisburg, PA, USA
2431 Computer & Space Science Building
Ph.D., Applied Mathematics and Scientific Computation
University of Maryland
M.S., Applied Mathematics and Scientific Computation
University of Maryland
B.S., Mathematics
James Madison University

Dr. Eugenia Kalnay, Dr. Jim Carton
Research Interests

My main research interests currently involve Ocean and Coupled-Model data assimilation (from research-level to full operational implementations) and fundamental methods in data assimilation.

  • Designing novel methods for data assimilation under non-optimal conditions, such as sparse observational data sets, short spin-up times.
  • Balancing ensemble size and model resolution using multi-resolution assimilation, reduced precision modeling, and hybrid Variational/Kalman Filter methods.
  • Studying the impact of approximating the assumptions of the Kalman Filter for earth system applications, and developing methods that rely on more general initial assumptions, such as with the use of stochastic differential equations.
  • Studying Non-Bayesian methods, methods that do not require assumptions of Gaussianity, linearity, or prior knowledge of distributions. Particle filters are one such approach.
  • Evaluating ocean DA behavior at multiple resolutions (from 1 degree, to 1/2-degree, 1/4-degree, and 1/12 degree).
  • Utilizing all available observation data, such as: vertical profiles of Temperature and Salinity, Lagrangian assimilation of drifter-based velocity data, as well as inclusion of satellite SST, SSH, and soon to be available SSS (see Aquarius).

  • Investigating the behavior of forced dynamical systems, generalizing the (uncoupled) ocean modeling environment.
  • Studying the connection of temporal decorrelation lengths with various analysis cycle lengths.
  • Assimilation of both slow and fast degrees of freedom.
  • Examine and correct model bias issues with appropriate modeling schemes, including stochatic parameterizations.
  • Design new metrics for assessing the quality of data assimilation results. Developing a suite of diagnostic/analysis tools to accelerate the verification and validation of assimilation results.
  • Developing new data assimilation techniques for the coupled Ocean/Atmosphere and full Earth system data assimilation.

Job Announcements

New (9/14/16) joint UMD/NWS postdoctoral position in Wave Modeling, Neural Networks, and Data Assimilation


Department of Atmospheric and Oceanic Science | 3424 CSS Building | University of Maryland, College Park, MD 20742