Sharanya J Majumdar

Associate Dean, Graduate Studies

(305) 421-4779
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Associate Dean of Graduate Studies and Professor, Department of Atmospheric Science - Rosenstiel School of Marine and Atmospheric Science

Predictability of tropical cyclones, ensemble prediction, optimizing current and future observing systems, data assimilation. 

Prof. Majumdar earned his B.A. and Ph.D. degrees in Mathematics from Cambridge University.  A chance meeting on a train in Australia ultimately led him into the atmospheric sciences as a postdoc at Penn State and then the faculty at the University of Miami, where he has been since 2002.  In addition to conducting research with his staff and students, he teaches undergraduate courses in tropical meteorology, weather forecasting and atmospheric dynamics, and graduate courses in atmospheric science, hurricanes, and predictability.



1992B.A. (Hons.) in Mathematics University of Cambridge
1997Ph.D. Applied 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.