J9 Advances in the Use of Artificial Intelligence in Aviation, Range, and Aerospace Meteorology

Wednesday, 15 January 2025: 8:30 AM-10:00 AM
354 (New Orleans Ernest N. Morial Convention Center)
Hosts: (Joint between the 25th Conference on Aviation, Range, and Aerospace Meteorology; and the 24th Conference on Artificial Intelligence for Environmental Science )
Submitters:
Nathan L Curtis, Federal Aviation Administration, Washington, DC and Christina E. Kumler, CIRA, Boulder, CO
Cochairs:
Daniel R. Adriaansen, NCAR, RAL, Boulder, CO and Kyle A. Hilburn

Advances in artificial intelligence have allowed for better understanding of both numerical weather forecasting and real-time meteorological situational awareness. This session will focus on how these advances are utilized in aviation, range, and aerospace operations and vehicle design to mitigate risk and increase the safety of flight. Example topics include use of artificial intelligence to improve situational awareness and decision support for aviation applications such as visibility, clouds, icing, and turbulence as well as range and aerospace applications such as evaluating range safety criteria and day of launch support.

Papers:
8:30 AM
J9.1
Application of Machine Learning to a Low-level Aviation Turbulence Forecasting System
YESEUL LEE, Yonsei Univ., Seoul, South korea; and H. Y. Chun

8:45 AM
J9.2
Verification and Analysis of Aviation Jetstream Forecast from Artificial Intelligence Models
Christy Y. Y. Leung, Hong Kong Observatory, Hong Kong, Hong kong; and C. Y. Leung
Manuscript (6.7 MB)

Handout (5.7 MB)

9:00 AM
J9.3
The Captain Has Turned the Seatbelt Sign On: 43 Years of Reanalysis and AI to Predict Clear Air Turbulence
Mayra Ivelisse Oyola-Merced, University of Wisconsin-Madison, Madison, WI; and I. E. Rodriguez Nazario

9:15 AM
J9.4
Turbulence Nowcasting through Machine Learning with Geostationary Satellite and NWP
Anthony J. Wimmers, Associate Scientist, Univ. of Wisconsin CIMSS, Madison, WI; and S. Griffin, W. Feltz, and T. Rahmes

9:30 AM
J9.5
Neural Hydrometeor Phase Detection: Deep Learning-Based Prediction of Liquid and Ice Water Content from NEXRAD Radar Observations
Alberto Hojel, University of California, Berkeley, CA; Rainmaker Technologies, El Segundo, CA; and D. Serke, K. Suski, K. Adair, T. Crabtree, J. Schultz, D. Vinson, T. Cash, and A. Doricko

9:45 AM
J9.6
Machine Learning and Numerical Weather Prediction Prediction of Aircraft Turbulence and Icing: Operations and Verification
Lance E. Steele, Certified Consulting Meteorologist, Weathernews, Norman, OK

Recorded Presentation
- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
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