Contact Information
Biography
Prof. Nesbitt leads a research group in the Department of Atmospheric Sciences, where his research and teaching interests reside in observations and modeling of clouds and precipitation processes across the globe. He has participated in more than 20 field campaigns on 4 continents, and was the lead Principal Investigator of the NSF/NOAA/NASA RELAMPAGO (Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations) and Co-PI of the DOE Clouds, Aerosols, and Complex Terrain Interactions (CACTI) field campaign in Argentina and Brazil in 2018-19. He serves as a member of the American Meteorological Society Committee on Mesoscale Processes and the scientific planning team for the planned NASA Aerosols Clouds Convection Precipitation mission. He is a member of the University Senate, and a faculty affiliate of the Computational Science and Engineering program.
Research Interests
- precipitation processes and cloud dynamics
- mesoscale meteorology
- tropical meteorology
- radar and satellite meteorology
- data science in atmospheric sciences
Education
Meteorology, PhD, University of Utah
Meteorology, MS, Texas A&M University
Meteorology, BS with Honors, State University of New York College at Oswego
Awards and Honors
- Highly Meritorious Meteorology Senior Award, State University of New York College at Oswego, 1997
- Excellence in Graduate Research Award, University of Utah, 2003
- NASA Earth System Science Graduate Fellowship
- Editors’ Citation for Excellence in Refereeing for Journal of Geophysical Research – Atmospheres, American Geophysical Union, 2006, 2007, 2011
- NASA Robert H. Goddard Award, as member of the NASA Global Precipitation Measurement mission Ground Validation team, 2015
- NASA Group Achievement Award, 2010, 2015, 2020
- Award for Outstanding Service to the Radar Meteorology Committee, American Meteorological Society, 2016
- Department Graduate Teaching Award, 2020
- Department Service Award, 2020
- List of Teachers Ranked as Excellent: ATMS 403 Spring 2007, Fall 2007, Fall 2009, ATMS 406 Fall 2009, ATMS 505 Spring 2013, ATMS 571 Fall 2014, ATMS 597 Spring 2020
Courses Taught
- ATMS 207: Weather and Climate Data Science
- ATMS 305: Geophysical Data Analysis
- ATMS 315: Meteorological Instrumentation
- ATMS 406: Tropical Meteorology
- ATMS 597: Mesoscale modeling
External Links
Academic Service
- Member, Chair of the American Meteorological Society Scientific and Technical Committee on Radar Meteorology, 2008 - 2016
- Co-Chair, American Meteorological Society 35th Conference on Radar Meteorology, Pittsburgh, PA, 2011
- Editor, Journal of Applied Meteorology, 2010 - 2014
- Member of the American Meteorological Society Scientific and Technical Committee on Mesoscale Meteorology, 2019 - present
- Member, Universities Space Research Association Earth Sciences Council, 2016 - present
- Co-Chair, American Meteorological Society 19th Conference on Mesoscale Meteorology, Location TBD, 2021
- Chair, Department Diversity, Equity, and Inclusion committee, 2020-2021
Recent Publications
Bechis, H., Galligani, V., Alvarez Imaz, M., Cancelada, M., Simone, I., Piscitelli, F., Maldonado, P., Salio, P., & Nesbitt, S. W. (2022). A case study of a severe hailstorm in Mendoza, Argentina, during the RELAMPAGO-CACTI field campaign. Atmospheric Research, 271, [106127]. https://doi.org/10.1016/j.atmosres.2022.106127
Casanovas, C., Salio, P., Galligani, V., Dolan, B., & Nesbitt, S. W. (2021). Drop Size Distribution Variability in Central Argentina during RELAMPAGO-CACTI. Remote Sensing, 13(11), [2026]. https://doi.org/10.3390/rs13112026
Casaretto, G., Dillon, M. E., Salio, P., Skaba, Y. G., Nesbitt, S. W., Schumacher, R. S., García, C. M., & Catalini, C. (2021). High-resolution NWP forecast precipitation comparison over complex terrain of the Sierras de Córdoba during RELAMPAGO-CACTI. Weather and Forecasting, 37(1). https://doi.org/10.1175/WAF-D-21-0006.1
Chase, R. J., Nesbitt, S. W., & McFarquhar, G. M. (2021). A dual-frequency radar retrieval of two parameters of the snowfall particle size distribution using a neural network. Journal of Applied Meteorology and Climatology, 60(3), 341-359. https://doi.org/10.1175/JAMC-D-20-0177.1
Dillon, M. E., Maldonado, P., Corrales, P., García Skabar, Y., Ruiz, J., Sacco, M., Cutraro, F., Mingari, L., Matsudo, C., Vidal, L., Rugna, M., Hobouchian, M. P., Salio, P., Nesbitt, S., Saulo, C., Kalnay, E., & Miyoshi, T. (2021). A rapid refresh ensemble based data assimilation and forecast system for the RELAMPAGO field campaign. Atmospheric Research, 264, [105858]. https://doi.org/10.1016/j.atmosres.2021.105858
