Joint Session 43 Big Data, Big Computing, Bigger Science: High-Performance Computing enabled Artificial Intelligence

Wednesday, 15 January 2020: 1:30 PM-2:30 PM
155 (Boston Convention and Exhibition Center)
Hosts: (Joint between the Sixth Symposium on High Performance Computing for Weather, Water, and Climate; and the 19th Conference on Artificial Intelligence for Environmental Science )
Cochairs:
Timothy S. Sliwinski, Texas Tech Univ., Department of Geosciences, Atmospheric Science Group, Lubbock, TX and David John Gagne II, NCAR, Boulder, CO

This session will be a joint session between the 6th Symposium on High-Performance Computing for Weather, Water, and Climate (6HPC) and the 19th Conference on Artificial Intelligence for Environmental Science. As datasets grow larger and artificial intelligence methods grow more complex, researchers have pushed the need for more powerful computing resources to make scientific advances. This session will explore how these Big Data projects are enabled by researchers using new computing methods, greater levels of parallelism, faster storage systems, better optimized algorithms, and more to push artificial intelligence and machine learning forward. Example submissions may include (but are not limited to) using clusters of specialized hardware such as graphical processing units (GPUs) to enable shorter time to results or larger pools of results, optimized storage systems that can reduce the latency of delivering data to learning algorithms distributed over a high-performance system, or work creating or using advanced algorithms that improve scalability towards exascale computing.

Papers:
1:30 PM
J47.1
Deep Learning for Automated Feature Detection in Climate, Weather, and Space
David Hall, NVIDIA Corporation, Lafayette, CO; and C. Tierney, S. Posey, and J. Hooks

1:45 PM
J47.2
Towards Unsupervised Segmentation of Extreme Weather Events
Adam Rupe, Univ. of California, Davis, CA; and K. Kashinath, N. Kumar, V. Lee, M. Prabhat, and J. P. Crutchfield

2:00 PM
J47.3
Assessing Changes in Tropical Cyclone Genesis Under Varying Climate Scenarios
Arturo Fernandez, Univ. of California, Berkeley, CA; Uber Technologies, Inc., San Francisco, CA; and K. Kashinath, J. McAuliffe, D. Nolan, C. M. Patricola, M. Prabhat, and M. F. Wehner

2:15 PM
J47.4
Meteorological Event Identification Using National Weather Service Forecast Discussions
Brian Freitag, Univ. of Alabama, Huntsville, AL; and K. Bugbee, J. Miller, J. Zhang, R. Ramachandran, and M. Maskey

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