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Jebb Stewart
Supervisory Meteorologist
325 Broadway
Boulder, CO 80305-3328


Jebb Q. Stewart is at the forefront of developing innovative technologies for advanced AI techniques to improve weather forecasting and interactive visualization of environmental data and as lead of the Weather Visualization and AI Research Development branch with the NOAA Global Systems Laboratory in Boulder Colorado.  With a unique blend of expertise in both Meteorology and Computer Science, he has over 20 years of experience in software development tailored for interactive visualizations, processing, and distributing geophysical data.


Weather Visualization and AI Research Development (WIZARD) Branch Chief 

Research Interests

  • Machine Learning and Artificial Intelligence
  • Cloud Computing 
  • Exascale and Big Data
  • Interactive Data Visualization and Processing


  • MS in Computer Science, Colorado State University, Fort Collins, CO
  • BS in Meteorology, University of Utah, Salt Lake City, UT


2022 - Present NOAA/GSL Weather Visualization and AI Research Development Branch Chief
2019 – present NOAA/ESRL/GSL/Informatics and Visualization Branch 
2014 – 2019 NOAA/ESRL/GSL/Informatics and Visualization, Branch – Cooperative Institute for Research in the Atmosphere (CIRA) 
2010 – 2014 Program Manager/Senior Software Developer, NOAA/ESRL/GSL – CIRA 
2003 – 2010 Technical Lead Software Engineer, NOAA/ESRL/GSL - CIRA
2001 – 2003 Programmer/Analyst, Systems Research Group at Forecast System Laboratory

Professional Activities

  • Co-Chair NOAA AI Executive Committee
  • Member NOAA Center for AI Team
  • Member of NOAA Environmental Data Management Workshop Committee
  • AGU Member

Honors and Awards

  • Department of Commerce Silver Medal Award “for the operational value of predictions of wildfire smoke transport and its impact on weather to support air quality alerts and visibility forecasts” (2023)
  • NOAA Bronze Award "For the Department's first research to education transition that secured NOAA's ability to deliver data products to a vast network with global reach" (2022)
  • CIRA Research Initiative Award for “For team leadership/mentoring, implementation of innovative and creative technology, and achievements that resulted in substantial impact within the workplace and cutting edge research.” (06-2018)
  • CIRA Recognition “In Honor of the CIRA Team’s Valuable Contributions to NOAA’s Science on a Sphere Bronze Medal Achievement of 100+ Installations and Over 33 Million Annual Visitors” (02-2015)
  • GSD Team Member of the Month, December 2013 in recognition for “a number of outstanding efforts in assisting GSD and TOB throughout the year on Science on a Sphere and the High Impact Weather Prediction Project (HIWPP)”.
  • CIRA Team Research Initiative Award for innovative research accomplishments in conceiving, designing and developing the NOAA Environmental Information Services (NEIS) data access and visualization framework. (07-2012)
  • Certificate of Recognition for extraordinary and responsive effort in preparing the Flow Following Finite Volume Icosahedral Model (FIM) for display on Science On a Sphere from the National Oceanic Atmospheric Administration. (03-2008)
  • CIRA Research Initiative Award for recognition of technical leadership in the system design and development of enabling technology for Gridded FX-Net System. (07-2007)
  • Certificate of Recognition for leadership and extraordinary efforts for the support of the NOAA/NWS Fire Weather Program from the Director of the National Weather Service. (03-2007)
  • FX-Net project received Fire Weather Honor Award from the Bureau of Land Management and U.S. Forest Service's Predictive Services Program. (11-2005)


Frolov, S., Garrett, K., Jankov, I., Kleist, D., Stewart, J. Q., & Ten Hoeve, J. (2024). Integration of emerging data-driven models into the NOAA research to operation pipeline for numerical weather prediction. In Bulletin of the American Meteorological Society. American Meteorological Society.

Bostrom, A., Demuth, J. L., Wirz, C. D., Cains, M. G., Schumacher, A., Madlambayan, D., Bansal, A. S., Bearth, A., Chase, R., Crosman, K. M., Ebert‐Uphoff, I., Gagne, D. J., II, Guikema, S., Hoffman, R., Johnson, B. B., Kumler‐Bonfanti, C., Lee, J. D., Lowe, A., McGovern, A., … Williams, J. K. (2023). Trust and trustworthy artificial intelligence: A research agenda for AI in the environmental sciences. In Risk Analysis. Wiley.

Lagerquist, R., Turner, D. D., Ebert-Uphoff, I., & Stewart, J. Q. (2023). Estimating full longwave and shortwave radiative transfer with neural networks of varying complexity. In Journal of Atmospheric and Oceanic Technology. American Meteorological Society.

McGovern, A., Bostrom, A., Davis, P., Demuth, J. L., Ebert-Uphoff, I., He, R., Hickey, J., Gagne II, D. J., Snook, N., Stewart, J. Q., Thorncroft, C., Tissot, P., & Williams, J. K. (2022). NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES). In Bulletin of the American Meteorological Society (Vol. 103, Issue 7, pp. E1658–E1668). American Meteorological Society.

Lagerquist, R., Stewart, J. Q., Ebert-Uphoff, I., & Kumler, C.. (2021), Using Deep Learning to Nowcast the Spatial Coverage of Convection from Himawari-8 Satellite Data. In Monthly Weather Review (Vol. 149, Issue 12, pp. 3897–3921). American Meteorological Society.

Lagerquist, R., Turner, D., Ebert-Uphoff, I., Stewart, J., & Hagerty, V. (2021). Using deep learning to emulate and accelerate a radiative-transfer model. In Journal of Atmospheric and Oceanic Technology. American Meteorological Society.

Lagerquist R., J. Q. Stewart, I. Ebert-Uphoff, C. Kumler, Using Deep Learning to Nowcast the Spatial Coverage of Convection from Himawari-8 Satellite Data. (2021). Monthly Weather Review. American Meteorological Society.

Kumler-Bonfanti, C., J. Stewart, D. Hall, and M. Govett, Tropical and Extratropical Cyclone Detection Using Deep Learning. J. Appl. Meteor. Climatol., 2020 doi:

S.-A. Boukabara, V. M. Krasnopolsky, J. Q. Stewart, A. McGovern, D. Hall, J. E. T. Hoeve, J. Hickey, H.-L. A. Huang, J. Williams, K. Ide, P. Tissot, S. E. Haupt, K. S. Casey, N. Oza, S. G. Penny, A. Geer, E. S. Maddy, and R. N. Hoffman. Outlook for exploiting artificial intelligence in Earth science. Bull. Am. Meteorol. Soc., 2020.

Boukabara, S.-A., V. Krasnopolsky, J. Q. Stewart, S. G. Penny, R. N. Hoffman, and E. Maddy (2019), Artificial Intelligence may be key to better weather forecasts, Eos, 100, Published on 01 August 2019.

Boukabara, S., V. Krasnopolsky, J. Q. Stewart, E. S. Maddy, N. Shahroudi, and R. N. Hoffman, 2019: Leveraging Modern Artificial Intelligence for Remote Sensing and NWP: Benefits and Challenges. Bull. Amer. Meteor. Soc., 100, ES473–ES491,

Lee, Y., Hall, D., Stewart, J., & Govett, M. (2019). Machine Learning for Targeted Assimilation of Satellite Data. Machine Learning and Knowledge Discovery in Databases Lecture Notes in Computer Science,53-68. doi:10.1007/978-3-030-10997-4_4

Bonfanti, C., L. Trailovic, J. Stewart, & M. Govett (2018). Machine Learning: Defining Worldwide Cyclone Labels for Training. In 2018 21st International Conference on Information Fusion (FUSION). IEEE. 753-760

Armstrong, L. et all. 2015. Mapping and Modeling Weather and Climate With GIS, Chapter 17. Interoperability Interfaces. Esri Press.

Summers, Sara, and Jebb Stewart. “Public Eye.” Meteorological Technology International Aug. 2014: 31–34. Print.

Gutman, S.I., K.L. Holub, S.R. Sahm, J.Q. Stewart, T.L. Smith, S.G. Benjamin, and B.E. Schwartz, 2004: Rapid Retrieval and Assimilation of Ground Based GPS-Met Observations at the NOAA Forecast Systems Laboratory: Impact on Weather Forecasts. J. Meteor. Society of Japan 82, 351-360.

Stewart, J. Q., C. D. Whiteman, W. J. Steenburgh, and X. Bian, 2002: A Climatological Study of Wind Systems of the United States Intermountain West. Bull. Amer. Meteor. Soc., 83, 699-708.