3B.1 Generating Ensemble-Derived Next-Day Probabilistic Severe Weather Forecasts with Machine Learning

Tuesday, 14 January 2020: 8:30 AM
156BC (Boston Convention and Exhibition Center)
Eric D. Loken, CIMMS/Univ. of Oklahoma, Norman, OK; and A. J. Clark

One challenge of severe weather forecasting is that numerical weather prediction guidance cannot explicitly simulate severe weather occurrence. Instead, proxies, such as simulated 2-5 km updraft helicity (UH), are typically used to infer if, when, and where severe weather will occur. A disadvantage of this approach is that the proxies must be calibrated, and the calibrations may be sensitive to geographic region and time of year. Additionally, proxies generally use only a small subset of available ensemble data, potentially ignoring valuable information. Machine learning (ML) can help solve these problems by directly relating a variety of ensemble forecast data to observed severe weather occurrence.

One ML method that has shown success in the past for post-processing ensemble-based precipitation forecasts is the random forest (RF). This presentation shows that, using a RF approach with data from 2015-2017, skillful probabilistic next-day severe weather forecasts can be obtained from the Storm Prediction Center’s (SPC’s) 7-member Storm-Scale Ensemble of Opportunity (SSEO) and the 10-member National Center for Atmospheric Research Ensemble Prediction System (NCAR EPS). The RF-based severe weather probabilities compare favorably with UH-based probabilities as well as the SPC’s day 1 convective outlooks, especially during the fall, when UH-based proxies are less skillful and severe weather forecasting skill reaches an annual minimum. It is envisioned that real-time RF-based probabilistic severe weather forecasts could help SPC forecasters bolster their day-1 convective outlooks by improving their access to information contained within high-resolution ensembles.

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