Into the Deep End: Four Projects Using Python in the Atmospheric Sciences for Undergraduate Interns at Argonne National Laboratory

Research skills, specifically independent learning, are not taught well at an undergraduate level. It is vital that students gain skills by doing as well as traditional instruction delivery measures. To this end the Department of Energy’s Summer Undergraduate Learning Internship (SULI) takes talented students from around the nation and pairs them up with scientists at the Office of Science’s National Laboratories. This presentation will entertain the audience with the experiences of four of those students at Argonne National Laboratory. They took on four distinct projects: Linking storms seen in NEXRAD data to GOES16 cloud top temperatures, radar based storm nowcasting for Chicago, structural storm analysis using X-Band radar and comparing seasonal and intraseasonal climatologies of rainfall over Darwin Australia to act as an observational target for DoE’s flagship climate model, the Energy Exascale Earth System Model (or E3SM). The presentation will highlight specific Python tools and methods used including Dask, Py-ART, Cartopy and Pandas. More importantly it will detail how the projects were constructed and how the students developed independent learning skills over the course of the two month research experience.