Peter Norris, Research Scientist III

Peter Norris


NASA Goddard
Bldg 33, Room C221
Greenbelt, MD 20771

Phone: 301-614-6252
Fax: 301-614-6297


Cloud parameterization (esp. statistical subgrid-scale parameterizations), cloud microphysics, cloud data assimilation


Dr. Peter M. Norris received a BS and MS from the University of Auckland, New Zealand, in 1986 and 1989, and a Ph.D. in Oceanography in 1996 from Scripps Institution of Oceanography (SIO), University of California, San Diego, where his major research interest was numerical modeling of the stratocumulus-topped marine boundary layer. He continued this research in 1996 as a Research Oceanographer in the Marine Meteorology Research Group at SIO, and then in 1997-1998 took up a New Zealand Science and Technology Postdoctoral Fellowship at the National Institute for Water and Atmospheric Research in New Zealand, where he worked on marine boundary-layer modeling. In 1999 he joined the GSFC Center for Excellence in Space Data and Information Science (CESDIS) as a Staff Scientist working on cloud parameterization behavior in a high-resolution global belt version of NCEP's Eta model. In 2000 he joined UMBC/GEST as an Assistant Research Scientist, working at the NASA Data Assimilation Office (DAO), now the Global Modeling and Assimilation Office (GMAO), on cloud modeling and assimilation. In May 2011, he joined Goddard Earth Sciences Technology and Research (GESTAR). He has also participated in numerous atmospheric field experiments, including the FIRE Cirrus II, Kansas, 1991, FIRE ASTEX, Azores, 1992, and TOGA-COARE, Solomon Islands, 1993, taking roles in data collection, aircraft flight direction, and data analysis. Dr. Norris' current research focuses on methods to improve cloud properties in numerical weather prediction and global climate models, and in particular GMAO's GEOS system. This includes improved cloud parameterizations, using subcolumn statistical approaches (with probability density and copula functions, see Norris et al., 2008, QJRMS, v. 134), and cloud data assimilation of high-resolution satellite cloud data via Bayesian cloud parameter estimation (see Norris and da Silva, 2016, QJRMS, v. 142, Parts 1 & 2).