TJ20.4 Do Forecasters Add Value to Machine Learning Algorithms of Cloud-to-Ground Lightning?

Thursday, 10 January 2019: 11:15 AM
North 225AB (Phoenix Convention Center - West and North Buildings)
Cara Gregg, The Ohio State Univ., Columbus, OH; and T. C. Meyer and K. M. Calhoun

Cloud-to-ground lightning is an extremely dangerous weather phenomenon resulting in 28 deaths annually over the last decade; currently, there are no requirements for National Weather Service to communicate lightning dangers or hazards to the public. A probabilistic algorithm was developed at the National Severe Storms Laboratory using machine learning to create an automated system that generates objects around areas where it predicts cloud-to-ground (CG) lightning will occur. In spring 2017, nine forecasters from the National Weather Service tested a Probabilistic Hazard Information prototype in the Hazardous Weather Testbed in which they used the guidance of the automated system, modified these objects from the system, and created their own objects to ideally create better forecasts of CG lightning. These forecaster and automated objects were verified and aspects of their performance, such as the probability of detection, were compared to see if the forecasters added value to the automated system. Forecasters added value to the system by adding discussion to the objects and through modifying the size, severity, duration, and probability of the lightning storms. However, forecasters found the task particularly tedious to complete. The areas where the forecasters are adding the most value could be used to improve the automated system’s performance at predicting CG lightning, further reducing forecaster workload.
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