Sharanya J Majumdar


(305) 421-4779
Doherty Marine Science Center 4600 Rickenbacker Cswy
Room 326
Miami, Florida 33149-1031


Associate Dean of Graduate Studies and Professor

Sharan Majumdar is a Professor of Atmospheric Sciences and Associate Dean of Graduate Studies at the University of Miami’s Rosenstiel School of Marine and Atmospheric Science. His main research interests lie in the broad area of predictability of tropical cyclones, including sensitivity diagnostics, ensemble prediction, optimizing current and future observing systems, and data assimilation.

Prof. Majumdar teaches a range of courses, including undergraduate-level courses in weather and climate, weather forecasting, and atmospheric dynamics and graduatelevel courses in introductory atmospheric science, hurricanes, and predictability. One of his favorite courses is the research-oriented course on predictability, where graduate students learn the fundamental concepts behind forecast error growth and data assimilation. Majumdar mentors Ph.D. students in both the Atmospheric Sciences (ATM) and Meteorology and Physical Oceanography (MPO) graduate programs.



1993M.A. Mathematics, University of Cambridge

Honors & Acknowledgements

As Associate Dean of Graduate Studies, Prof. Majumdar and his office oversee the welfare of all M.S. and Ph.D. students and the 6 graduate programs. He has also served as a Graduate Program Director and Chair of the RSMAS Graduate Academic Committee for 3 years, coordinating the development of the new graduate programs across RSMAS. He was recently inducted into the University of Miami’s “LeadershipU” academy on executive training. He is also a prominent working group member of various national and international organizations, including the World Meteorological Organization.
Prof. Majumdar’s research group uses several techniques to understand and improve the prediction of high-impact weather events, particularly tropical cyclones. They use numerical models, ensemble predictions, satellite and aircraft observations, and data assimilation schemes. They study how to best incorporate a variety of datasets into models using state-of-the-art data assimilation methods. Other studies involve the examination of how ensembles can improve our representation and understanding of uncertainty in forecasts.