Joint Session 4 Scalable Operational Artificial Intelligence Applications with Python

Thursday, 10 January 2019: 8:30 AM-9:30 AM
North 221C (Phoenix Convention Center - West and North Buildings)
Hosts: (Joint between the Ninth Symposium on Advances in Modeling and Analysis Using Python; and the 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences )
David John Gagne II, NCAR, Boulder, CO; Scott Collis, Argonne National Laboratory, Environmental Science Division, Argonne, IL; Daniel Rothenberg, ClimaCell Inc., Boston, MA and Sarvesh Garimella, ACME AtronOmatic, LLC, Portland, OR

This session highlights projects in the private sector, government, and academia that have used Python software libraries to power applications utilizing artificial intelligence, machine learning, and other data science techniques for earth, oceanic, and atmospheric science problems. The presenters are encouraged to discuss the details of how they have implemented these systems and discuss both their successes and the challenges they have overcome or still face. Following the presentations, there will be a panel discussion featuring all of the presenters where they can answer more questions from the audience about incorporating and scaling AI systems. If there are a large number of submissions to this session, then a poster session will be added following the oral session to highlight other scalable AI and Python systems.

8:30 AM
8:45 AM
Exploring the Use of Artificial Intelligence (AI) to Optimize the Exploitation of Big Satellite Data in NWP and Nowcasting
Sid Ahmed Boukabara, NOAA/NESDIS, College Park, MD; and E. Maddy, N. Shahroudi, R. N. Hoffman, T. Connor, S. Upton, A. Karpovich, C. Sprague, and K. Kumar
9:00 AM
Deep Learning for Improved Use of Satellite Observations
David Hall, NVIDIA Corporation, Lafayette, CO; and J. Q. Stewart, C. Bonfanti, M. W. Govett, S. Maksimovic, and L. Trailovic
9:15 AM
Using Python and Machine Learning Techniques for High-Impact Weather Nowcasting in an Operational Center in Brazil
Cesar Beneti, Parana Meteorological System (SIMEPAR), Curitiba, Brazil; and N. Rozin, L. Calvetti, J. Ruviaro, and C. Oliveira
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