![]() Because the probabilistic FACETs paradigm is starkly different from the current binary yes/no warning paradigm, this creates the need for probabilistic guidance to communicate a level of certainty of potential threats. 2018, unpublished manuscript Rothfusz et al. This paradigm is part of NOAA’s Forecasting a Continuum of Environmental Threats (FACETs) effort (C. The NWS is exploring a new paradigm to its advisory/watch/warning products, whereby severe weather watches and warnings (as well as other hazards) may be disseminated in a grid-based, frequently updating, probabilistic manner. Given the very large data volume applicable to severe weather warning operations, the manual analysis techniques typically employed in operations will not always extract all of the pertinent information, especially when numerous storms are present. For short-fuse operational products such as severe thunderstorm and tornado warnings, forecasters must quickly analyze relevant data, identify threats, and issue warnings to the public in a timely manner. 2016), and other datasets means that forecasters have routine access to very large volumes of data. 2015), spaceborne lightning mappers, terrestrial lightning arrays, Multi-Radar Multi-Sensor products (MRMS Smith et al. The combination of high-resolution numerical weather prediction (NWP) models, next-generation Geostationary Observational Environmental Satellites (e.g., GOES-16 Schmit et al. Real-time meteorological datasets are becoming more advanced and sophisticated, with greater spatial resolution, frequency, and content. Issuing severe weather warnings is a critical function of the National Weather Service (NWS). Feedback from NWS users has been highly favorable, with most forecasters responding that ProbSevere increases confidence and lead time in numerous warning situations. The validation analysis demonstrates that ProbSevere has slightly less skill compared to NWS severe weather warnings, but can offer greater lead time to initial hazards. In addition, a thorough validation analysis is presented. This paper focuses on the integration of ground-based lightning detection data into ProbSevere. ProbSevere guidance, which updates approximately every 2 min, is available to National Weather Service (NWS) Weather Forecast Offices with very short latency. More specifically, ProbSevere utilizes short-term numerical weather prediction guidance (NWP), geostationary satellite, ground-based radar, and ground-based lightning data to determine the probability that convective storm cells will produce severe weather up to 90 min in the future. The empirical Probability of Severe (ProbSevere) model, developed by the National Oceanic and Atmospheric Administration (NOAA) and the Cooperative Institute for Meteorological Satellite Studies (CIMSS), automatically extracts information related to thunderstorm development from several data sources to produce timely, short-term, statistical forecasts of thunderstorm intensity. Five minutes after the warning was issued, multiple reports of 1-in.-diameter hail were recorded (at 0003 UTC). This storm west of Des Moines, IA, on the evening of 30 March 2016 was cause for a severe thunderstorm warning by the NWS 20 min after this image time (2358 UTC). ![]() MRMS MergedReflectivity is shaded from light blue (~15–20 dB Z) to green (~25–35 dB Z) to yellow and orange (~35–45 dB Z) to red (~50–55 dB Z) and then to white and magenta (60+ dB Z). When sampling is enabled in AWIPS-II, forecasters can scroll over polygons with their mouse cursors to see readouts of predictor values and the probability of severe weather. Pink shades denote probabilities in the 75%–100% range, whereas gray-to-purple shades denote probabilities in the 0%–15% range. Polygons represent storms identified and tracked by ProbSevere, colored by the computed probability of severe weather in the next 90 min. NOAA/CIMSS ProbSevere model output visualized in AWIPS-II (image time is 2338 UTC ProbSevere = 85%).
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