Dr. Jamie Dyer is a professor of meteorology/climatology with a primary research focus on hydrometeorological processes and their relation to surface-atmosphere interactions. Since arriving at MSU in 2005, Dr. Dyer has published in a variety of peer-reviewed journals such as the Journal of Geophysical Research, Weather and Forecasting, and the Journal of Hydrology, and has received funding from agencies including the US Army Research Laboratory, the US Geological Survey (USGS), and the National Oceanic and Atmospheric Administration (NOAA). Dr. Dyer’s current research interests are fairly broad, although they all have a strong focus on applied meteorology.
Dr. Dyer utilizes his research interests and experience to train and motivate students for success in the job market after graduation. He advises a number of MS and Ph.D.-level graduate students, and currently teaches courses in the following topics:
- Atmospheric thermodynamics
- Atmospheric dynamics (kinematics)
- Computer methods and techniques in meteorology
- Ph.D. (Geography), University of Georgia, 2005
- M.S. (Geography), University of Georgia, 2001
- B.S. (Physics), University of Georgia, 1999
- A.S., Young Harris Junior College, 1997
- Associate Professor, Department of Geosciences, Mississippi State University, 2011-present
- Visiting Professor, Faculty of Earth Sciences, Maria Curie-Skłodowska University, Lublin, Poland, 2015
- Assistant Professor, Department of Geosciences, Mississippi State University, 2005-2011.
- Hydrometeorologist (SCEP), Southeast River Forecast Center, NWS, Peachtree City, Georgia, 2001-2004.
- Numerical weather prediction (NWP) of convective initiation and precipitation formation along surface land use/cover boundaries
- Applications of multi-sensor (radar plus surface observation) precipitation estimates for analysis of warm-season rainfall patterns and extreme rainfall events
- Observations and assessment of planetary boundary layer processes using unmanned aerial vehicles (UAV)
- Visual analytics of numerical weather model uncertainty using parameterization and stochastic ensembles