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Monday, May 18, 2020. 12;00 PM. Webinar: Using Deep Learning to Improve Prediction and Understanding of High-impact Weather. Ryan Lagerquist, University of Oklahoma. Sponsored by NOAA. More information.

Abstract: 
I describe the application of convolutional neural networks (CNN), a type of deep-learning method, to high-impact weather.  CNNs are specially designed to learn directly from spatial grids, which improves both skill and interpretability.  Specifically, I develop and test CNNs for two tasks.  The first is tornado prediction, where two CNNs predict next-hour tornado occurrence for a given storm, using datasets similar to those used by forecasters in real-time operations.  The tornado models achieve an area under the receiver-operating-characteristic curve (AUC) of 0.94 and critical success index (CSI) of ~0.3.  This is competitive with a machine-learning model currently used in operations, which suggests that the CNNs would also be suitable for operations.  Specialized machine-learning-interpretation methods highlight the importance of a deep reflectivity core and strong mesocyclone, as well as low-level instability and wind shear in the surrounding environment.  Also, interpretation methods suggest that a rear-flank downdraft with too much precipitation and negative buoyancy can lead to tornadogenesis failure, which corroborates some previous literature.  The second task is front detection, where a CNN draws warm and cold fronts in reanalysis data.  I use the CNN-detected fronts to create a 40-year climatology over North America.  On a large scale, fronts are most common in the mid-latitude cyclone track, which migrates poleward from winter to summer, equatorward during El Niño, and poleward during La Niña.  Also, the cyclone track appears to be migrating poleward as a consequence of global warming.  These results are broadly consistent with the few pre-existing climatologies, although there are some discrepancies that should be investigated in the future.  Overall, I demonstrate that deep learning can be used to advance both the prediction and understanding of high-impact weather.

Bio:
Dr. Ryan Lagerquist recently graduated with a Ph.D. in Meteorology from the University of Oklahoma.  He has been researching machine-learning applications in atmospheric science for 8 years with organizations including Environment Canada, the University of Alberta, Google, NCAR, and CIMMS.  Ryan begins a postdoc with the Cooperative Institute for Research in the Atmosphere (CIRA) in June.  Ryan is also program co-chair of the 2021 Artificial Intelligence conference at the AMS annual meeting.