THE COMMUNITY LEVERAGED UNIFIED ENSEMBLE (CLUE) IN THE 2016 NOAA/HAZARDOUS WEATHER TESTBED SPRING FORECASTING EXPERIMENT

One primary goal of annual Spring Forecasting Experiments (SFEs), which are coorganized by NOAA’s National Severe Storms Laboratory and Storm Prediction Center and conducted in the National Oceanic and Atmospheric Administration’s (NOAA) Hazardous Weather Testbed, is documenting performance characteristics of experimental, convection-allowing modeling systems (CAMs). Since 2007, the number of CAMs (including CAM ensembles) examined in the SFEs has increased dramatically, peaking at six different CAM ensembles in 2015. Meanwhile, major advances have been made in creating, importing, processing, verifying, and developing tools for analyzing and visualizing these large and complex datasets. However, progress toward identifying optimal CAM ensemble configurations has been inhibited because the different CAM systems have been independently designed, making it difficult to attribute differences in performance characteristics. Thus, for the 2016 SFE, a much more coordinated effort among many collaborators was made by agreeing on a set of model specifications (e.g., model version, grid spacing, domain size, and physics) so that the simulations contributed by each collaborator could be combined to form one large, carefully designed ensemble known as the Community Leveraged Unified Ensemble (CLUE). The 2016 CLUE was composed of 65 members contributed by five research institutions and represents an unprecedented effort to enable an evidence-driven decision process to help guide NOAA’s operational modeling efforts. Eight unique experiments were designed within the CLUE framework to examine issues directly relevant to the design of NOAA’s future operational CAM-based ensembles. This article will highlight the CLUE design and present results from one of the experiments examining the impact of single versus multicore CAM ensemble configurations

THE COMMUNITY LEVERAGED UNIFIED ENSEMBLE (CLUE) IN THE 2016 NOAA/HAZARDOUS WEATHER TESTBED SPRING FORECASTING EXPERIMENT

One primary goal of annual Spring Forecasting Experiments (SFEs), which are coorganized by NOAA’s National Severe Storms Laboratory and Storm Prediction Center and conducted in the National Oceanic and Atmospheric Administration’s (NOAA) Hazardous Weather Testbed, is documenting performance characteristics of experimental, convection-allowing modeling systems (CAMs). Since 2007, the number of CAMs (including CAM ensembles) examined in the SFEs has increased dramatically, peaking at six different CAM ensembles in 2015. Meanwhile, major advances have been made in creating, importing, processing, verifying, and developing tools for analyzing and visualizing these large and complex datasets. However, progress toward identifying optimal CAM ensemble configurations has been inhibited because the different CAM systems have been independently designed, making it difficult to attribute differences in performance characteristics. Thus, for the 2016 SFE, a much more coordinated effort among many collaborators was made by agreeing on a set of model specifications (e.g., model version, grid spacing, domain size, and physics) so that the simulations contributed by each collaborator could be combined to form one large, carefully designed ensemble known as the Community Leveraged Unified Ensemble (CLUE). The 2016 CLUE was composed of 65 members contributed by five research institutions and represents an unprecedented effort to enable an evidence-driven decision process to help guide NOAA’s operational modeling efforts. Eight unique experiments were designed within the CLUE framework to examine issues directly relevant to the design of NOAA’s future operational CAM-based ensembles. This article will highlight the CLUE design and present results from one of the experiments examining the impact of single versus multicore CAM ensemble configurations

Incorporating UH Occurrence Time to Ensemble-Derived Tornado Probabilities

Probabilistic ensemble-derived tornado forecasts generated from convection-allowing models often use hourly maximum updraft helicity (UH) alone or in combination with environmental parameters as a proxy for right-moving (RM) supercells. However, when UH occurrence is a condition for tornado probability generation, false alarm areas can occur from UH swaths associated with nocturnal mesoscale convective systems, which climatologically produce fewer tornadoes than RM supercells. This study incorporates UH timing information with the forecast near-storm significant tornado parameter (STP) to calibrate the forecast tornado probability. To generate the probabilistic forecasts, three sets of observed climatological tornado frequencies given an RM supercell and STP value are incorporated with the model output, two of which use UH timing information. One method uses the observed climatological tornado frequency for a given 3-h window to generate the probabilities. Another normalizes the observed climatological tornado frequency by thenumberofhail,wind,andtornadoreportsobservedinthat3-hwindowcomparedtothemaximumnumber of reports in any 3-h window. The final method is independent of when UH occurs and uses the observed climatological tornado frequency encompassing all hours. The normalized probabilities reduce the false alarm area compared to the other methodsbut havea smaller area under the ROC curveandrequirea much higher percentile of the STP distribution to be used in probability generation to become reliable. Case studies demonstrate that the normalized probabilities highlight the most likely area for evening RM supercellular tornadoes, decreasing the nocturnal false alarm by assuming a linear convective mode