Evaluation of a Probabilistic Forecasting Methodology for Severe Convective Weather in the 2014 Hazardous Weather Testbed

Abstract

A proposed new method for hazard identification and prediction was evaluated with forecasters in the National Oceanic and Atmospheric Administration Hazardous Weather Testbed during 2014. This method combines hazard-following objects with forecaster-issued trends of exceedance probabilities to produce probabilistic hazard information, as opposed to the static, deterministic polygon and attendant text product methodology presently employed by the National Weather Service to issue severe thunderstorm and tornado warnings. Three components of the test bed activities are discussed: usage of the new tools, verification of storm-based warnings and probabilistic forecasts from a control–test experiment, and subjective feedback on the proposed paradigm change. Forecasters were able to quickly adapt to the new tools and concepts and ultimately produced probabilistic hazard information in a timely manner. The probabilistic forecasts from two severe hail events tested in a control–test experiment were more skillful than storm-based warnings and were found to have reliability in the low-probability spectrum. False alarm area decreased while the traditional verification metrics degraded with increasing probability thresholds. The latter finding is attributable to a limitation in applying the current verification methodology to probabilistic forecasts. Relaxation of on-the-fence decisions exposed a need to provide information for hazard areas below the decision-point thresholds of current warnings. Automated guidance information was helpful in combating potential workload issues, and forecasters raised a need for improved guidance and training to inform consistent and reliable forecasts.

The 2015 Plains Elevated Convection at Night Field Project

“…Targeting bores and related wave phenomena is difficult because their development and evolution depend on the strength of the convectively generated cold pool and of the SBL, and on the ambient wind profile, all of which are difficult to predict. A webbased PHI tool, developed originally for the NOAA Hazardous Weather Testbed Spring Experiments and used for MCS and CI forecasting, was extended to predict bores as well in PECAN (Karstens et al 2015;Haghi et al 2015). The data driving this tool in PECAN were obtained from a 1-km WRF model developed and run in real time by the University of Oklahoma MAP group (Johnson et al 2015).…”

“…7). A probabilistic hazard information (PHI) tool (Karstens et al 2015) was used by the PECAN field forecasters to help prepare their probabilistic forecast graphics.…”

Severe Convective Storms in Europe: Ten Years of Research and Education at the European Severe Storms Laboratory

“…Since 2012, ESSL has also organized the annual ESSL Testbed, which was inspired by the Spring Experiment of the NOAA Hazardous Weather Testbed (e.g., Kain et al 2003Kain et al , 2006Kain et al , 2010Clark et al 2012;Karstens et al 2015; http://hwt.nssl.noaa.gov/spring_experiment/). ESSL opened its Research and Training Centre in (Fig.…”

An investigation of local and national NWS warning outbreaks for severe convective events.

“…While this study does not investigate the human factors that go into the warning process (Boustead and Mayes 2014), the results of this study are of importance to ongoing comprehensive efforts to improve the NWS warning system (Karstens et al 2015), in addition to raising a general awareness of these extreme situations and their relationship to overall warning performance. When a severe convective event requires a large number of warnings, warning outbreaks can occur.…”

“…How do these situations verify compared to annual averages? An investigation into these questions could offer knowledge that immediately benefits operational forecasters and could provide informative guidance to longer-term efforts aiming to modernize the current warning system used by the NWS (Rothfusz et al 2014;Karstens et al 2015). It is important to note that warning verification is a difficult task and far from perfect, especially during significant weather events.…”