Dr. Andrew Mercer is a meteorologist/climatologist whose primary research expertise centers on the application of machine learning/artificial intelligence methods to large-scale meteorological and climatological problems. He has published works in many different meteorological areas, including mountain windstorm forecasting, wind energy forecasting, warm-season precipitation quantification, tropical cyclone rapid intensification, large-scale severe weather outbreaks, seasonal climatological forecasts, groundwater and precipitation patterns, climatological downscaling, and hemispheric teleconnections. He also works closely with the Northern Gulf Institute as a research fellow and employs high performance computing when constructing numerical weather prediction model simulations of various meteorological phenomena. He currently maintains a postdoctoral researcher at NOAA’s Atlantic and Oceanic Meteorological Laboratory, with a second former postdoctoral researcher working on developing a basin-specific seasonal tropical cyclone forecast using statistical methods.

In addition to his numerous research foci, he is responsible for teaching Synoptic Meteorology, Physical Meteorology, Weather Analysis, and Dynamic Meteorology II for the department, as well as graduate and undergraduate statistical methods in atmospheric sciences. He currently serves as the Graduate Coordinator for the Department of Geosciences, as well as the team leader for Meteorology. He has also co-authored a Physical Geography lab manual used in courses around the nation.

Education

• Ph.D., University of Oklahoma, School of Meteorology, 2006-2008.
  • Dissertation: Discrimination of Tornadic and Non-tornadic Severe Weather Outbreaks
• M.S., University of Oklahoma, School of Meteorology, 2002-2005.
  • Thesis: Analysis of Three Synoptic Storm Tracks in the United States
• B.S., University of Oklahoma, School of Meteorology, 1998 - 2002

Experience

• Professor, Mississippi State University Department of Geosciences, 2021 - present
• Associate Professor, Mississippi State University Department of Geosciences, 2015 – 2020
• Assistant Professor, Mississippi State University Department of Geosciences, 2009 – 2015
• Lecturer, University of Oklahoma School of Meteorology, 2009
• Adjunct Instructor, Department of Science and Engineering, Oklahoma State University – Oklahoma City, 2008
• Research Assistant, Cooperative Institute of Mesoscale Meteorological Studies, 2003 – 2009
• Assistant Shift Supervisor, Weatherbank Inc., Edmond, OK, 2003 – 2008

Research Interests

• Synoptic Meteorology, Severe Weather Meteorology, Large-Scale Climate Informatics, Tropical Cyclone Intensification, Artificial Intelligence, Statistical Climatology, Numerical Weather Prediction

Teaching Areas

• GR 4733/6733 Synoptic Meteorology
• GR 4933/6933 Dynamic Meteorology II
• GR 4693/6693 Physical Meteorology and Climatology II
• GR 8453 Quantitative Analysis in Climatology

Honors/Professional Activities

• Graduate of the Office of Research and Economic Development Faculty Leadership Program, 2023-2024 Academic Year
• Geosystems Research Institute/Northern Gulf Institute Fellow

Graduate Students

• Victoria Maxwell – M.S.
• Mollee Starr – M.S.
• Nathan Mirly – M.S.
• Treven Knight – Ph.D.
• Jason Finley – Ph.D.

• Wiley, J., and A. Mercer, 2020:  An updated synoptic climatology of Lake Erie and Lake Ontario heavy lake-effect snow events.  Atmos., 11, 21 pp.
• Elcik, C., C. Fuhrmann, S. Sheridan, A. Mercer, and K. Sherman-Morris, 2020:  Relationship between synoptic weather type and emergency department visits for different types of pain across the triangle region of North Carolina.  Int. J. Biometeorology.
• Mercer, A., 2020:  Predictability of common atmospheric teleconnection indices using machine learning.  Proc. Comp. Sci., 168, 11-18.
• Sankar, M., P. Dash, Y. Lu, A. Mercer, G. Turnage, C. Shoemaker, and R. Moorhead, 2020:  Land use and land cover control on the spatial dispersal of dissolved organic matter across 41 lakes in Mississippi, USA.  Hydrobiologica, 1.
• Sankar, M., P. Dash, Y. Lu, V. Paul, A. Mercer, Z. Arslan, J. Varco, and J. Rodgers, 2019:  Dissolved organic matter and trace element variability in a blackwater-fed bay following precipitation.  Estuarine, Coastal and Shelf Science, 231, 16 pp.
• Mercer, A., and A. Bates, 2019:  Meteorological differences characterizing tornado outbreak forecasts of varying quality.  Atmos., 10, 16 pp.
• *MacDonald, C., and A. Mercer, 2019:  STUDENT PAPER:  Using Blue Waters to assess tornadic outbreak forecast capability by lead time.  J. Comp. Sci. Education, 11, 23-28.
• Guzman, S., J. Paz, M. Tagert, A. Mercer, and J. Pote, 2019:  Evaluation of seasonally classified inputs for the prediction of daily groundwater levels:  NARX Networks vs. support vector machines.  Env. Modeling & Assessment, 1-12.
• Sankar, M., P. Dash, Y. Lu, S. Singh, S. Chen, and A. Mercer, 2019:  Effect of photo-biodegradation and biodegradation on the biogeochemical cycling of dissolved organic matter across diverse surface water bodies.  J. Environ. Sciences, 77, 130-147.
• Mercer, A., A. Grimes, and K. Wood, 2018:  Multidimensional kernel principal component analysis of false alarms of rapidly intensifying Atlantic tropical cyclones.  Procedia Comp. Sci., 140, 359-366.
• Grimes, A., and A. Mercer, 2015:  Synoptic-scale precursors to tropical cyclone rapid intensification in the Atlantic Basin.  Adv. Meteor., 1.
• Chen, H., S. Zhang, W. Chen, H. Mei, J. Zhang, A. Mercer, R. Liang, and H. Qu, 2015:  Uncertainty-aware multidimensional ensemble data visualization and exploration.  IEEE Transactions Visualization and Computer Graphics, 1, 1.
• Mercer, A., and J. Dyer, 2014:  A new scheme for daily peak wind gust prediction using machine learning. Procedia Comp. Sci., 7, 128-133.
• Dixon, P., A. Mercer, W. Cooke, and K. Grala, 2014: Objective identification of tornado seasons and ideal spatial smoothing radii. Earth Interactions, 18, 1-15.
• Dyer, J., and A. Mercer, 2013: Assessment of spatial rainfall variability in the lower Mississippi River alluvial valley. J. Hydrometeorology, 14, 1826-1843.
• Mercer, A., J. Dyer, and S. Zhang, 2013: Warm-season thermodynamically-driven rainfall prediction with support vector machines. Procedia Comp. Sci., 36, 598-598.
• Richman, M. B., A. E. Mercer, L. M. Leslie, C. A. Doswell, and C. M. Shafer, 2013: High dimensional data compression using principal components. Open J. Statistics, 5, 356-366.