Preparing the Next Generation of Meteorological Data Scientists: Redesigning Curricula for Student Success

An increasing number of atmospheric science and meteorology graduates are forgoing traditional careers in academia and public sectors (e.g., National Weather Service) in favor of private-sector opportunities. The private sector offers enriching career options for our graduates as small and large companies seek out students with atmospheric science knowledge and technical skills. In part, this change is due to the evolving landscape of weather information being leveraged by more people and applications. Observations and weather model information are being routinely leveraged to create specialized products for clients and customers, and companies see the value in having atmospheric science and meteorological expertise. However, while our traditional meteorology curricula have maintained a rigorous structure to teaching dynamics, chemistry, radiation, and the like, they have been slow to adapt to the other needs of private sector positions, namely skills in data analysis, data science, and programming.

At the University of Oklahoma (OU), we have made substantial revisions to our undergraduate and graduate curriculum to take into consideration the changing landscape of atmospheric science careers. In particular, the increasing dependence on computer programming and data analysis skills from employers, either within graduate schools or the private sector, has motivated the restructuring of our undergraduate, meteorology-specific programming class. “Introduction to Programming for Meteorology” has been revised and modernized to levy Python-based notebooks for in-class lectures, demonstrations, and flipped-classroom education, and the class provides ample opportunities to apply new programming knowledge to real-world curated datasets. (e.g., mobile radar scans, high-resolution model forecasts). The class is now required for Freshman undergraduates in the meteorology program, providing a foundation for programming skill development throughout the undergraduate curriculum.

Additionally, new courses are in development (or have been recently taught for the first time) that introduce and explore artificial intelligence and machine learning (AI/ML) in the context of environmental science. Employers are increasingly interested in students with data science backgrounds, including AI/ML, and these efforts are further supporting the career transition we are witnessing across the weather enterprise, including exposing students to innovative and cross-cutting research tools. This presentation will introduce these curricula advancements at OU, including what has worked and what has not, and offer publicly available resources that can be adapted for other meteorology courses.