Professor, Graduate Coordinator, Meteorology
200B Hilbun Hall
Mississippi State, MS 39762
Dr. Andrew Mercer is a meteorologist/climatologist whose primary areas of research focus include statistical and artificial intelligence (AI) applications to large-scale meteorological and climatological problems. He has used AI methods with many different meteorological applications, including mountain windstorm forecasting, peak wind speed forecasts, warm-season precipitation quantification, tropical cyclone intensification, large-scale severe weather outbreaks, seasonal climatological forecasts, groundwater and precipitation patterns, climatological downscaling, and hemispheric teleconnections. His research interests continue to focus in the areas of artificial intelligence and statistics, with a secondary focus area on numerical weather prediction and applications of high-performance computing to meteorological and climatological problems. He has also co-authored a Physical Geography lab manual and taught a variety of meteorological and statistical courses for the department.
- 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
- Associate Professor, Mississippi State University Department of Geosciences, 2015 – present
- 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
- Synoptic Meteorology, Severe Weather Meteorology, Large-Scale Climate Informatics, Artificial Intelligence, Statistical Climatology, Numerical Weather Prediction
- GR 4733/6733 Synoptic Meteorology
- GR 4633/6633 Statistical Climatology
- GR 4693/6693 Physical Meteorology and Climatology II
- GR 8453 Quantitative Analysis in Climatology
- Associate Editor – Journal of Operational Meteorology – National Weather Association
- Associate Editor – Monthly Weather Review – American Meteorology Society
- Geosystems Research Institute/Northern Gulf Institute Fellow
- Justin Bowles – M.S.
- Maximilian Magness – M.S.
- Jacob Wiley – 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. View More
- 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.