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Webinar: Using Deep Learning to Improve Prediction and Understanding of High-impact Weather
Monday, 18 May 2020, 12:00
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.
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.
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.