Boulder, CO 80305-3328
Jebb Q. Stewart is the lead of the Informatics and Visualization Branch with the NOAA Global System Laboratory in Boulder Colorado. With a unique background in both Meteorology and Computer Science, he has over 20 years of experience in software development for visualizing, processing, distributing, and interacting with geophysical data. Over the last couple of years, his work has expanded to machine learning applications for object identification and improving data processing capabilities along with leveraging commercial cloud capabilities to provide tools and services to efficiently explore the ever growing volumes of data.
- 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
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
- 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
- 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)
Kumler-Bonfanti, C., J. Stewart, D. Hall, and M. Govett, Tropical and Extratropical Cyclone Detection Using Deep Learning. J. Appl. Meteor. Climatol., 2020 doi: https://doi.org/10.1175/JAMC-D-20-0117.1.
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. https://doi.org/10.1175/BAMS-D-20-0031.1.
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, https://doi.org/10.1029/2019EO129967. 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, https://doi.org/10.1175/BAMS-D-18-0324.1.
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.