首页|Studies Conducted at University of Oklahoma on Machine Learning Recently Publish ed (Exploring the Usefulness of Machine Learning Severe Weather Guidance in the Warn-on-Forecast System: Results from the 2022 NOAA Hazardous Weather Testbed Sp ring ...)
Studies Conducted at University of Oklahoma on Machine Learning Recently Publish ed (Exploring the Usefulness of Machine Learning Severe Weather Guidance in the Warn-on-Forecast System: Results from the 2022 NOAA Hazardous Weather Testbed Sp ring ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on artificial in telligence have been published. According to news originating from Norman, Oklah oma, by NewsRx correspondents, research stated, "Artificial intelligence (AI) is gaining popularity for severe weather forecasting. Recently, the authors develo ped an AI system using machine learning (ML) to produce probabilistic guidance f or severe weather hazards, including tornadoes, large hail, and severe winds, us ing the National Severe Storms Laboratory's (NSSL) Warn-on-Forecast System as in put (WoFS)." Our news editors obtained a quote from the research from University of Oklahoma: "Known as WoFSML- Severe, it performed well in retrospective cases, but its ope rational usefulness had yet to be determined. To examine the potential usefulnes s of the ML guidance, we conducted a control and treatment (experimental) group experiment during the 2022 NOAA Hazardous Weather Testbed Spring Forecasting Exp eriment (HWT-SFE). The control group had full access to WoFS, while the experime ntal group had access to WoFS and ML products. Explainability graphics were also integrated into the WoFS web viewer. Both groups issued 1-hr convective outlook s for each hazard. After issuing their forecasts, we surveyed participants on th eir confidence, the number of products viewed, and the usefulness of the ML guid ance. We found the ML-based outlooks outperformed non-ML-based outlooks for mult iple verification metrics for all three hazards and were rated subjectively high er by the participants."
University of OklahomaNormanOklahomaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMach ine Learning
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