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Evaluation of One-Hour Probabilistic Severe Weather Forecasts Issued during the 2014 NOAA Hazardous Weather Testbed Spring Forecasting Experiment

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Monday, 3 November 2014
Capitol Ballroom AB (Madison Concourse Hotel)
Michael C. Coniglio, NOAA/NSSL, Norman, OK ; and D. A. Imy, C. D. Karstens, A. Clark, J. Correia Jr., and C. J. Melick

The NOAA Hazardous Weather Testbed (HWT) Spring Forecast Experiment (SFE) is held each spring at the National Weather Center in Norman, OK with the goal of accelerating the transfer of research into operations. This annual five-week forecast exercise is a joint effort between the National Severe Storms Laboratory (NSSL) and the Storm Prediction Center (SPC). Meteorologists from different disciplines (modelers, academia, NWS field office forecasters, etc.) around the world come together to issue experimental forecasts of severe weather in a simulated operational environment.

The primary emphasis for the 2014 SFE was creating probabilistic severe weather forecasts for shorter time periods than currently issued by the SPC. The goal is to develop and explore ways of generating probabilistic guidance on a continuum of scales, ranging from the day 1 Convective Outlook (15 to 24 hours) to Convective Watches (1 to 6 hours). Current forecast products issued by the SPC contain little probabilistic information on the shorter time scales. Two forecast desks were established during the 2014 SFE, with one desk issuing separate 3-hourly forecasts for hail, wind and tornadoes, and the other desk issuing nine 1-hour total severe weather forecasts (hail, wind, and tornadoes combined). The participants issued five different sets of 1-hour forecasts with one set issued by the lead forecaster (a SPC or NSSL facilitator). This presentation will discuss the challenges and lessons learned from these issuances and include a verification of the forecasts and numerical ensemble guidance. The 1-hour forecasts and reasoning for the May 21st convective event in the Ohio Valley and for the June 3rd convective event in the Midwest will be presented to illustrate the complexities for issuing these forecasts. ->