P1.1 Teaching Artificial Intelligence to Meteorology Undergraduates

Advanced Artificial Intelligence (AI) forecast systems have shown superiority to traditional regression models in a number of research projects (e.g. Burrows 1997, Dean and Fiedler 2002). Yet operational use of AI-based forecast systems remains limited in comparison with older regression-based statistical forecast systems. Likewise, development of AI forecast systems by operational forecasters remains uncommon. Given the potential of AI in operational forecasting why has adoption not been more widespread? A major factor restricting the adoption of AI systems in operational meteorology is the lack of a pool of forecasters trained in the areas required to use the new systems wisely. The supply of forecasters and researchers trained in the theory and tools required to develop new AI-based forecast systems is even more limited. In order to develop a large enough pool of appropriately trained operational meteorologists one must teach AI at the undergraduate level. The goal of this project was to develop an undergraduate course that would prepare students to be both users and developers of AI-based forecast systems. Successful users of such systems must understand the strengths and limitations of the various AI methodologies and understand verification well enough to be able to test for themselves the skill of AI systems versus their own forecasts. System developers require even greater knowledge, but not nearly as much as those researchers developing new AI methodologies. As the development tools improve, system development is opened up to a much wider group of meteorologists. To use these tools wisely, the students must know enough theory to select the set of methods that are likely to work well on a particular problem. Likewise, to fit system development into an already busy schedule, operational meteorologists require both training and hands-on experience in a tool set that lets them develop AI forecast systems efficiently and within the limits of their knowledge. Teaching AI to undergraduate meteorologists poses a number of challenges. In particular, undergraduate meteorologists are not fluent with all of the tools used by AI researchers. In mathematics they have generally had calculus and differential equations but often lack linear algebra. In statistics they understand quantitative data description and linear regression but have little ____________________________________________