Using Convection-Allowing Models to Produce Forecast Guidance For Severe Thunderstorm Hazards via a Surrogate-Severe Approach! Ryan Sobash! University of Oklahoma, School of Meteorology, Norman, OK! J. S. Kain and M. Coniglio! NOAA/OAR/National Severe Storms Laboratory, Norman, OK! D. R. Bright, A. R. Dean, and S. J. Weiss! NOAA/NCEP/Storm Prediction Center, Norman, OK! 25th Conference on Severe Local Storms, Denver, CO!
Observations! Storm Reports! Brooks et al. (1998) developed a baseline for SPC forecast verification! Baseline! Forecast! Can this be applied to predicted severe convection?! Predicted locations of severe convection! Forecast! x ~! O(1km)!
What model fields could serve as surrogates for intense simulated convection?! Tornadoes! Large Hail! High Wind! Mesocyclone! Mesocyclone! Mesocyclone! Updraft! Updraft! 10-m Wind Speed! Reflectivity! Downdraft! We will initially focus on mesocyclone identification, since supercells are prodigious producers of severe weather.! Shaded:! UH > 25 m 2 s -2! Updraft helicity (UH) used to identify mesocyclones in convection-allowing model output.! Hourly-maximum UH captures intra-hourly maxima (Kain et al. 2010).! Kain et al. (2008)!
Creating surrogate reports! Examined WRF model output over 2008 convective season (4/18 6/8).!!NSSL-WRF, run daily at NSSL!!ARW V2.2 core, 4 km grid spacing!!initialized at 00 UTC, integrated 36 hours.! Locations of severe convection chosen by flagging grid points exceeding a UH threshold during a 24-hour period. Referred to as surrogate severe reports (SSRs).! Threshold selected from UH frequency distribution, centered on subjective estimate.! Shaded:! UH > 25 m 2 s -2! Predicted locations of severe convection! Kain et al. (2008)!
UH SSR Climatology! 18 April 8 June 2008! UH > 34 m 2 s -2!! 78,838 SSRs!!! 8,017 OSRs! Owing to sampling differences, a disparity exists between SSRs on the native NSSL-WRF grid and observed storm reports (OSRs). SSRs and OSRs were mapped to an 80 km grid to produce biases closer to 1. No discrimination between storm report type.!
UH SSR Climatology! 18 April 8 June 2008! On the 80 km grid, the bias of the lowest threshold (UH > 34) is closer to one.! Bias! 0.87! The bias gradually decreases when the threshold is increased.! UH climatology on the 80 km grid reproduces maximum in central US. Deficiencies in eastern US, west of Cont. Divide.! 2,349! OSRs!
Surrogate Severe Probability Forecast! Apply kernel density estimation, i.e. linear smoother, to the field of SSRs.! A Gaussian was used as the kernel for the density estimate. The kernel is a pdf so we refer the final field as a surrogate severe probability forecast (SSPF).! SSPF =100 N n =1 1 2πσ exp d 2 n 2 2σ 2 SSPF! Location of SSRs! Surrogate Severe Reports for! 24-hr period! Surrogate Severe Probability Forecast!
SSR and SSPF Example! 24-hour forecasts (12 UTC to 12 UTC)!
SSPF Verification! Verified SSPFs using standard probabilistic verification methods. Focused on forecast resolution (using ROC curves) and forecast reliability (using reliability diagrams).! ROC curves Reliability Diagram 0.88 (SSR=UH > 34)! 0.74 (SSR=UH > 103)! No skill! Climo! Greatest ROC curve area for the lowest threshold. Good reliability for the lowest threshold, but even better if threshold is increased to near 40 m 2 s -2.!
SSPF Verification! Changing the value of sigma, the Gaussian standard deviation, impacts the resulting probabilities. Varying sigma could improve forecast reliability.! σ = 120 km σ = 160 km! σ = 240 km! Too hot! Just right?! Too cold!
SSPF Verification: Varied sigma! σ = 80 km! σ = 160 km! Observed! Frequency! σ = 220 km! σ = 260 km! Forecast probability!
Preliminary investigation of other surrogate fields! Investigated behavior of 4 other surrogate fields:!!10 m Wind Speed (UU)!!1 km Reflectivity (RF)!!Max. Col. Updraft (UP)!!Max. Col. Downdraft (DN)! UH! 0.87! UU! 0.79! Field! Bias! RF! 1.12! UP! 1.57! DN! 1.42! OBS! produced w/ lowest threshold!
Preliminary investigation of other surrogate fields! UU, RF, UP, DN have a tendency to overforecast at the lowest thresholds.! Better reliability at higher thresholds, but these thresholds suffer from poor resolution (small ROC curve areas).! UU! UP! RF! DN!
Summary! UH diagnostic especially suited to identify severe convection in convectionallowing model output.! During the Spring of 2008, SSPFs created with UH SSRs at thresholds near 35 m 2 s -2 with σ near 160 km were most reliable with good forecast resolution.! Other fields appear to be less skillful within SSPF framework.! Future Work! Calibrate SSPFs over a longer time period (~ 2 years) to determine if these findings are applicable under all seasons and regions.! SPC prob fcst! Discriminate between severe weather type (hail, wind, etc).! Apply SSPF procedure to an ensemble of forecasts (next talk!).! What format is best suited for operational use?! SSPF fcst!