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J. Knaff and C. Slocum provided mentorship of Alvin Cheung, a rising senior in Meteorology and Atmospheric Science from The Penn State University. Alvin’s 10-week REU project was entitled “Detecting Tropical Cyclone Secondary Eyewalls with Microwave-Based Scheme.” The motivation for the work was to determine if a combination of tropical cyclone centered 85-91 GHz microwave imagery (MI), infrared window imagery-based diagnostics, environmental diagnostics, and storm intensity could be used to objectively classify the occurrence of secondary eyewall occurrence. To train the methodology, subjectively-determined secondary eyewall formation cases, provided by D. Herndon (CIMSS) and J. Kossin (NCEI), were used along with the recently developed TC PRIMED dataset. The latter combines microwave imagery from the GPM constellation, IR imagery, ERA-5-based environmental diagnostics, best track information, and Goddard Profiling Algorithm (GPROF) rain rates. Alvin developed diagnostics that determine the likely existence of concentric rings of cold MI temperatures, their magnitude and relative spacing across a host of cross-track and conically scanning MI sensors, and designed a classification algorithm to objectively identify secondary eyewall cases. The classification algorithm is based on the number of cold rings, distance between cold rings, the magnitude of the outer cold ring, the 24-h change in infrared brightness temperature, the 24-h change in IR-based tropical cyclone size, current intensity, and the 200 to 850 hPa vertical wind shear. The algorithm produced skillful discrimination of secondary eyewalls in an independent test with Peirce Skill Scores ranging from 0.41 to 0.73 depending on the threshold probability chosen. Below is an example of the algorithm performance for Hurricane Lorenzo (2019). Work and interaction with Alvin will continue as he will be expanding his work in his undergraduate thesis at PSU.