Skip to main content
US Flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Stephen Weygandt
325 Broadway
Boulder, CO 80305-3328


Steve Weygandt, Chief, Assimilation, Verification and Innovation Division (AVID)

Stephen S. Weygandt, is the Chief of the Assimilation, Verification, and Innovation Division (AVID), who has helped to direct the development of data assimilation systems that provide initial conditions for the Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) models and has helped transition multiple versions of these models to NOAA operations since 2012.  He is currently helping to direct the development of the Rapid Refresh Forecast System (RRFS) that will replace the RAP and the HRRR as the NOAA regional model application of the Unified Forecast System. 

Dr. Weygandt also serves as the GSL lead for the Model Development & Enhancement Research (MDE) Team, as part of the FAA Aviation Weather Research Program (AWRP) that supports regional model development for aviation applications.  In this role, he leads GSL MDE collaboration with several other AWRP Teams and is active in the aviation weather community. The RAP and HRRR provide short-range weather guidance to many different users and are used as input for automated products addressing weather hazards such as convection, icing, ceiling and visibility, and turbulence.  Dr. Weygandt has also helped lead GSL efforts in regional assimilation of satellite data, including direct broadcast radiance data and geostationary lightning mapper data.

Dr. Weygandt joined NOAA in 2000 and his work has focused on improving rapidly updated local forecasts of hazardous weather phenomena and working with users to best utilize automated weather guidance products, including ensemble-based probabilistic guidance.  Dr. Weygandt has B.S. and M.S. degrees in meteorology from Penn State and a Ph.D. in meteorology from the University of Oklahoma.

Research Interests
  • Radar, satellite, and lightning data assimilation
  • Ensemble data assimilation and forecasting
  • Ensemble post-processing and use of probability guidance
  • Rapid-update model guidance for aviation and severe weather applications
  • Model verification and observing system impact assessment 

  • Ph.D. Meteorology, University of Oklahoma, 1998
  • M.S. Meteorology, The Pennsylvania State University, 1991
  • B.S. Meteorology, The Pennsylvania State University, 1984

Honors and Awards
  • 2015  U.S. Department of Commerce Gold Medal (group award)
  • 2015  Governor’s Award for High Impact Research (group award)
  • 2012  NOAA Research Employee of the Year (group award)
  • 2010  U.S. Department of Commerce Bronze Medal (group award)
  • 2006  NOAA Research Paper of the Year Award (group award)

Benjamin, S.G., E. P. James, M. Hu, C. R. Alexander, T. T. Ladwig, and John M. Brown, S. S. Weygandt, D. D. Turner, P. Minnis, W. L. Smith, A. Heidinger, 2020: Stratiform Cloud-Hydrometeor Assimilation for HRRR and RAP Model Short-Range Weather Prediction. Mon. Wea. Rev., conditionally accepted. 

Zhu, K. M. Xue, Y. Pan, M. Hu, S. Benjamin, S. Weygandt, H. Lin,  2019:  The Impact of Satellite Radiance Data Assimilation within a Frequently Updated Regional Forecast System Using GSI-based Ensemble Kalman Filter.  Adv. Atmos. Sci.,
Hu, M., S.G. Benjamin, T. Ladwig, S.S. Weygandt, 2017:   GSI 3-dimensional ensemble-variational hybrid data assimilation using a global ensemble for the regional Rapid Refresh model.  Mon. Wea. Rev. October

Lin, H., S.S. Weygandt, S.G. Benjamin, and M. Hu, 2017:  Satellite radiance data assimilation within the hourly updated Rapid Refresh.     Wea. Forecasting, 32, 1273-1287.   August

Lin, H., S.S. Weygandt, A. Lim, M. Hu, J.M. Brown, and S.G. Benjamin, 2017:  Radiance preprocessing for assimilation in the hourly updating Rapid Refresh mesoscale model: A study using AIRS data.  Wea. Forecasting. October

Benjamin, S. G., S.S. Weygandt, M. Hu, C.A. Alexander, T.G. Smirnova, J.B. Olson, J.M. Brown, E. James, D.C. Dowell, G.A. Grell, H. Lin, S.E. Peckham, T.L. Smith, W.R. Moninger, G.S. Manikin, 2016, A North American hourly assimilation and model forecast cycle: The Rapid Refresh.  Mon. Wea. Rev., 144, 1669-1694. 

Zhu, K., Y. Pan, M. Xue, X. Wang, J.S. Whitaker, S.G. Benjamin, S.S. Weygandt, M. Hu, 2013: A regional GSI-based ensemble Kalman filter data assimilation system for the Rapid Refresh configuration: Testing at reduced resolution.  Mon. Wea. Rev., 141, 4118-4139.

Stensrud, D., M. Xue, L. Wicker, K. Kelleher, M. Foster, J. Shaefer, R. Schneider, S. Benjamin, S. Weygandt, J. Ferree, and J. Tuell,  2009: Convective-scale Warn-On-Forecast System.  Bull. Amer. Meteor. Soc., 90, 1487-1499.

Wurman, J., Y. Richardson, C. Alexander, S. Weygandt, and P.F. Zhang, 2007: Dual-Doppler analysis of winds and vorticity budget terms near a tornado.  Mon. Wea. Rev., 135, 2392-2405.

Wurman, J., Y. Richardson, C. Alexander, S. Weygandt, P.F. Zhang,  2007: Dual-Doppler and Single-Doppler analysis of a tornadic storm undergoing mergers and repeated tornadogenesis.  Mon. Wea. Rev., 135, 736-758.

Benjamin, S.G., D. Devenyi, S.S. Weygandt, K.J. Brundage, J.M. Brown, G.A. Grell, D. Kim, B.E. Schwartz, T.G. Smirnova, T.L. Smith, and G.S. Manikin, 2004: An hourly assimilation/forecast cycle: The RUC.  Mon. Wea. Rev. 132, 495-518.

Weygandt, S., A. Shapiro and K. Droegemeier 2000:  Retrieval of initial forecast fields from single-Doppler observations of a supercell thunderstorm. Part I: Single-Doppler velocity retrieval.  Mon. Wea. Rev., 130, 433-453.

Weygandt, S., A. Shapiro and K. Droegemeier 2000:  Retrieval of initial forecast fields from single-Doppler observations of a supercell thunderstorm. Part II: Thermodynamic retrieval and model prediction.  Mon. Wea. Rev., 130, 454-476.

Weygandt, S. and N. Seaman, 1994:  Quantification of predictive skill for mesoscale and synoptic-scale meteorological features as a function of horizontal grid resolution. Mon. Wea. Rev., 122, 57-71.