Cooperative Institute for Research in Environmental Sciences at the University of Colorado Boulder

Stanley G. Benjamin

Research Interests

Benjamin is a senior scientist advisor for a group that focuses on developing the next-generation weather and earth-system models, data assimilation techniques and other research projects aimed at improving seamless prediction of extreme weather from short-range to seasonal time scales.

Current Research

  • Development of Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR), two experimental models developed in the Global Systems Laboratory which are now operational, helping weather forecasters around the country predict rapidly changing weather conditions including tornadoes, flash flooding and severe snowstorms.
  • Data assimilation (including storm-scale ensemble data assimilation) including observation impact studies and cloud/hydrometeor initialization.
  • Coupled atmospheric-ocean modeling toward improved prediction and predictability from tropical convection (MJO) and stratospheric sudden warming events.

Some of my branch’s current projects include:

  • Development of Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR), two hourly updated models developed in the Global Systems Laboratory which are now operational, and upcoming versions of similar models using the NOAA Unified Forecast System (UFS).
  • Development of global coupled models for improved medium-range to subseasonal forecasts.   Our group applies an advanced suite of physical parameterizations for global models (now, the NOAA Unified Forecast System – UFS, using the FV3 dynamic core) at medium-range and with coupling at subseasonal-to-seasonal scales.  
  • Development of improved representation of clouds and boundary layer and data assimilation to improve HRRR and RAP for applications for severe weather prediction and for the aviation/transportation community and for renewable energy, including ongoing wind and solar energy projects for the Department of Energy and NOAA, to improve energy management toward more efficient use of renewable energy.


map of great lakes, lake-effect snow

Improved short-range HRRR forecasts including for lake-effect snow is being accomplished by coupling atmospheric model with lake models.

Representation of subgrid-scale cloud processes is resulting in more accurate short-range (HRRR) forecasts but also for medium-range global forecasts.

Representation of subgrid-scale cloud processes is resulting in more accurate short-range (HRRR) forecasts but also for medium-range global forecasts.

Honors and Awards

  • U.S. Department of Commerce Gold Medals, 2015, 2006
  • NOAA Technology Transfer Award, 2017
  • Colorado Governor's Award for High-Impact Research, 2015
  • NOAA Research Employee of the Year, 2013
  • U.S. Department of Commerce Bronze Medals, 2010, 1998
  • NOAA Office of Atmospheric Research Outstanding Paper Awards, 2010, 2008, 2006
  • American Meteorological Society Fellow, 2004