1. RUC Convective Probability Forecasts using Ensembles and Hourly Assimilation Steve Weygandt Stan Benjamin Forecast Systems Laboratory NOAA

2. Background on Rapid-Update Cycle Background Fields 1-hr fcst 1-hr fcst 1-hr fcst 11 12 13 Time (UTC) Analysis Fields 3DVAR Obs 3DVAR Obs U.S. operational model, short-range applications, situational awareness model Used by aviation, severe weather and general forecast communities 1-h update cycle, many obs types including: ACARS, profiler, METAR, radar Full cycling cloud/precip Grell/Devenyi ensemble cumulus parameterization Benjamin, Thurs. 9:30 talk

3. Research Background Problem: Thunderstorm likelihood information needed by aviation traffic community for strategic planning (Collaborative Convective Forecast Product) Goals: Utilize outputs from RUC hourly output cycle to provide guidance for aviation forecasters. Blend model-based probabilities with observation- based probabilities (Pinto, next talk) Collaboration: NCAR Research Applications Lab (Mueller, Poster 5.21) National Weather Service Aviation Weather Center

4. Model-based Convective Probability Forecasts Principle: Convective forecasts at specific model grid points from a single deterministic model run less likely to be correct than ensembles of model outputs. Ensemble Approaches: Adjacent model gridpoints (2003) Time-lagged ensembles (2004) Cumulus parameterization closures Procedure: Use model 1-h parameterized precipitation Specify length-scale and precipitation threshold Bracketing 1-h model outputs from successive cycles

5. RUC convective precipitation forecast 5-h fcst valid 19z 4 Aug 2003 3-h conv. precip. (mm)

6. % 10 20 30 40 50 60 70 80 90 Prob. of convection within 120 km RUC convective probability forecast 5-h fcst valid 19z 4 Aug 2003 Threshold > 2 mm/3h Length Scale = 120 km Box size = 7 GPs 7 pt, 2 mm (gridpoint ensemble)

7. Time-lagged ensemble Model Init Time Eg: 15z + 2, 4, 6 hour RCPF forecast Forecast Valid Time (UTC) 11z 12z 13z 14z 15z 16z 17z 18z 19z 20z 21z 22z 23z 13z+4,5 12z+5,6 11z+6,7 13z+6,7 12z+7,8 13z+8,9 12z+9 RCPF 2 4 6 18z 17z 16z 15z 14z 13z 12z 11z 6 7 5 6 7 8 9 10 4 5 6 7 8 9 Model runs used model has 2h latency

8. Precipitation threshold adjusted diurnally and regionally to optimize the forecast bias Use smaller filter length-scale in Western U.S. Forecast Valid Time GMT EDT Higher threshold to reduce coverage Lower threshold to increase coverage Multiply threshold by 0.6 over Western U.S. Bias corrections

9. .24,.25.22,.23.20,.21.18,.19.16,.17.14,.15.12,.13.10,.11 CSI by lead-time, time of day Forecast Valid Time Diurnal cycle of convection 6-h 4-h 2-h 6-h 4-h 2-h 6-h 4-h 2-h RCPF v2004 RCPF v2003 CCFP (Verifiation 6-31 Aug. 2004) Fcst Lead Time GMT

10. .24,.25.22,.23.20,.21.18,.19.16,.17.14,.15.12,.13.10,.11 CSI by lead-time, time of day Forecast Valid Time Diurnal cycle of convection 6-h 4-h 2-h 6-h 4-h 2-h 6-h 4-h 2-h RCPF v2004 RCPF v2003 CCFP (Verifiation 6-31 Aug. 2004) Fcst Lead Time GMT Quick spin-up 18z init

11. .24,.25.22,.23.20,.21.18,.19.16,.17.14,.15.12,.13.10,.11 CSI by lead-time, time of day Forecast Valid Time Diurnal cycle of convection 6-h 4-h 2-h 6-h 4-h 2-h 6-h 4-h 2-h RCPF v2004 RCPF v2003 CCFP (Verifiation 6-31 Aug. 2004) Fcst Lead Time GMT Quick spin-up 18z init

12. Bias by lead-time, time of day 6-h 4-h 2-h 6-h 4-h 2-h 6-h 4-h 2-h 2.75-3.0 2.5-2.75 2.25-2.5 2.0-2.25 1.75-2.0 1.5-1.75 1.25-1.5 1.0-1.25 0.75-1.0 0.5-0.75 v2004 v2003 CCFP (Verifiation 6-31 Aug. 2004) Forecast Valid Time Diurnal cycle of convection Fcst Lead Time GMT

13. 2005 Sample RCPF and CCFP 25 – 49% 50 – 74% 75 – 100% Verification 00z 8 Mar 2005 NCWD CCFP 18z + 6h Forecast RCPF Verification from FSL Real-Time Verification System (Kay, Thurs. 12:48 talk)

14. Height (ft x 1000) RUC 4-h Forecast Potential Echo Top Observed Composite Radar Reflectivity/ EchoTops 38 26 37 22 36 25 53 43 45 37 38 55 57 44 50 51 39 33 27 33 27 34 57 56 36 35 45 52

15. A-S M-ConCAPEGrell Use of Ensemble Cumulus Closure Information Normalized 1-h avg. rainrates From different closure groups VERIFICATION 2100 UTC 26 Aug 2005 RCPF 8-h fcst

17. Relative Operating Characteristic (ROC) curves Show tradeoff: “detection” vs. “false-alarm” “Left and high” curve best Does gridpoint ensemble add skill? POD POFD ----- gridpoint ensemble ----- deterministic forecast Sample: 5-h fcst from 14z 04 Aug 2003 Low prob Low precip High precip High prob detection false detection 9 pt, 4 mm 25%

18. CSI = 0.22 Bias = 0.99 RCPF – 20 AUG ’05 11z+8h Scores for 40% Prob. NCWD valid 19z 20 AUG 05 RCPF20 RCPF13 CSI = 0.15 Bias = 1.19 25 – 49% 50 – 74% 75 – 100%

19. Sample 3DVAR analysis with radial velocity 500 mb Height/Vorticity * Amarillo, TX Dodge City, KS * * Analysis WITH radial velocity * * Cint = 2 m/s * * Cint = 1 m/s K = 15 wind Vectors and speed 0800 UTC 10 Nov 2004 Dodge City, KS Vr Amarillo, TX Vr * * Analysis difference (WITH radial velocity minus without)