13A.2 Integration of Automated Severe Weather Probabilistic Guidance within NWS Warnings in the Hazardous Weather Testbed

Thursday, 11 January 2018: 1:45 PM
Room 17A (ACC) (Austin, Texas)
Kristin M. Calhoun, Univ. of Oklahoma/CIMMS and NOAA/NSSL, Norman, OK; and C. D. Karstens, J. L. Cintineo, J. Sieglaff, G. J. Stumpf, J. J. James, and C. Ling

Automated, object-based guidance from the NOAA/CIMSS ProbSevere model has been integrated into the Probabilistic Hazard Information (PHI) tool and NWS Hazard Services to combat previous workload issues for NWS forecasters in creation for creation of storm-based probabilistic hazards and warnings. During the 2016-2017 Hazardous Weather Testbed (HWT) experiments, forecasters worked a series of both real-time events and case studies for a variety of severe and near-severe weather. Feedback was gathered from post-event discussions, case-walkthroughs, and surveys with forecasters, focused on multiple elements of the forecast information (e.g., tools, probabilities, workload, visualization, communication) and how each element could be improved.

In the 2016 experiment, the ProbSevere model provided forecasters with guidance for the occurrence of any severe weather associated with a storm over the next 60 min. While some difficulty with tracking hindered usability as commented in the forecaster feedback, ProbSevere was determined to be dependable (consistently available), timely (had little latency), and accurate (system probabilities reliably met storm likelihood) enough to move ahead into development within Hazard Services and continued development within the prototype PHI tool. During the 2017 experiment, the PHI prototype experiment expanded to contain hazard-specific (tornado, wind, and hail) automated guidance from ProbSevere for the forecaster.

This presentation focuses on how ProbSevere was utilized by forecasters in the testbed during both case studies and real-time events. For example: How much did forecasters depend upon the guidance? How did access and use of the automation modify the perceived workload? Were forecasters able to add value or did they decrease accuracy when modifying the probabilities away from the guidance? How did this vary according to hazard or hazards (e.g., hail, wind, and/or tornado) issued by the forecaster?

The goal of this work is to contribute to the Forecasting a Continuum of Environmental Threats (FACETs) paradigm, which proposes to evolve the National Weather Service (NWS) from product-centric watches and warnings to PHI.

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner