Artificial Intelligence

Artificial Intelligence is the application of statistics and machine learning to the development of new insights and prediction systems. While long a mainstay of data analysis in the atmospheric sciences, artificial intelligence techniques have only recently entered the mainstream in meteorological weather prediction. They offer advantages of speed, and in many cases accuracy, over human forecasters.

Selected Publications

Selected Publications

• Hilliker, J.L., G.S. Young, and J.M. Fritch, 2007: An objective, statistical forecast system for short-term probabilistic forecasts of thunderstorms. In press, National Weather Digest. --- Abstract

• Haupt, S.E., G.S. Young, and C.T. Allen, 2007: A genetic algorithm method to assimilate sensor data for a toxic contaminant release, In press, Journal of Computers. --- Abstract

• Root, B., P. Knight, G.S. Young, S. Greybush, R. Grumm, R. Holmes, and J. Ross, 2007: A fingerprinting technique for major weather events. J. Appl. Meteor. Climate, 46, 1053-1066. --- Abstract

• Allen, C.T., G.S. Young, and S.E. Haupt, 2007: Improving Pollutant Source Characterization by Optimizing Meteorological Data with a Genetic Algorithm. Atmospheric Environment, 41, 2283-2289. --- Abstract

• Allen, C.T., S.E. Haupt, and G.S. Young, 2007: Source Characterization with a Genetic Algorithm-Coupled Dispersion-Backward Model Incorporating SCIPUFF, J. Appl. Meteor. Climate, 46, 273-287. --- Abstract

• Haupt, S.E., G.S. Young, C.T. Allen, 2006: Validation of a Receptor–Dispersion Model Coupled with a Genetic Algorithm Using Synthetic Data, J. Appl. Meteor. Climate, 45, 476-490. --- Abstract

• Haupt, S.E., C.T. Allen, and G.S. Young, 2006: A Genetic Algorithm Method to Assimilate Sensor Data for Homeland Defense Applications, SMCals06: 2006 IEEE Mountain Workshop on Adaptive and Learning Systems, Logan, UT, July 24-26.

• Haupt, S.E., C.T. Allen, and G.S. Young, 2006: A Genetic Algorithm Method for Sensor Data Assimilation and Source Characterization, World Congress on Computational Intelligence, Vancouver, Canada, July 16-20.

• Allen, C.T., Haupt, S.E., and G.S. Young, 2006: Application of a genetic algorithm-coupled receptor-dispersion model to the Dipole Pride 26 Experiments. Proceedings of 14th Joint Conference on Applications of Air Pollution Meteorology. Paper 4.2.
  

• Haupt, S.E., G.S. Young, and L.J. Peltier, 2005: On using data assimilation and sensor fusion techniques for dispersion modeling. 12 pp. ARL technical memorandum TR 05-011.

• Allen, C.T., S.E. Haupt, and G.S. Young, 2005: Validation of a Genetic Algorithm-Coupled Receptor/Dispersion Model Incorporating SCIPUFF, 9th Annual George Mason University Transport and Dispersion Modeling Conference, Fairfax, VA. July 18-20.

• Haupt, S.E. and G.S. Young, 2005: Validation of Receptor/Dispersion Model Coupled with a Genetic Algorithm, AMS 4th Conference on Artificial Intelligence Applications to Environmental Science, San Diego, CA, January 10-13, paper 1.2.

• Knight, P., J. Ross, B. Root, G.S. Young, R. Grumm, 2005: Fingerprinting significant weather events. Proc. Fourth Conference on Artificial Intelligence, San Diego, CA, January 9-13.

• Young, G.S. and S.E. Haupt, 2005: Teaching Artificial Intelligence to Meteorology Undergraduates, AMS 4th Conference on Artificial Intelligence Applications to Environmental Science, San Diego, CA, January 10-13, paper P1.1.