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Steve Weygandt Stan Benjamin Forecast Systems Laboratory NOAA

RUC Convective Probability Forecasts using Ensembles and Hourly Assimilation. Steve Weygandt Stan Benjamin Forecast Systems Laboratory NOAA. 1-hr fcst. 1-hr fcst. 1-hr fcst. Background Fields. Analysis Fields. 3DVAR. 3DVAR. Obs. Obs. Time (UTC). 11 12 13.

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Steve Weygandt Stan Benjamin Forecast Systems Laboratory NOAA

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  1. RUC Convective Probability Forecasts using Ensembles and Hourly Assimilation Steve WeygandtStan BenjaminForecast Systems LaboratoryNOAA

  2. 1-hr fcst 1-hr fcst 1-hr fcst Background Fields Analysis Fields 3DVAR 3DVAR Obs Obs Time (UTC) 11 12 13 Background on Rapid-Update Cycle Benjamin, Thurs. 9:30 talk • 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

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

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

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

  9. Fcst Lead Time CSI by lead-time, time of day (Verifiation 6-31 Aug. 2004) 6-h 4-h 2-h RCPF v2003 .24, .25 .22, .23 .20, .21 .18, .19 .16, .17 .14, .15 .12, .13 .10, .11 6-h 4-h 2-h RCPF v2004 6-h 4-h 2-h CCFP Diurnal cycle of convection Forecast Valid Time GMT

  10. Fcst Lead Time CSI by lead-time, time of day (Verifiation 6-31 Aug. 2004) 6-h 4-h 2-h RCPF v2003 Quick spin-up 18z init .24, .25 .22, .23 .20, .21 .18, .19 .16, .17 .14, .15 .12, .13 .10, .11 6-h 4-h 2-h RCPF v2004 6-h 4-h 2-h CCFP Diurnal cycle of convection Forecast Valid Time GMT

  11. Fcst Lead Time CSI by lead-time, time of day (Verifiation 6-31 Aug. 2004) 6-h 4-h 2-h RCPF v2003 Quick spin-up 18z init .24, .25 .22, .23 .20, .21 .18, .19 .16, .17 .14, .15 .12, .13 .10, .11 6-h 4-h 2-h RCPF v2004 6-h 4-h 2-h CCFP Diurnal cycle of convection Forecast Valid Time GMT

  12. Fcst Lead Time Bias by lead-time, time of day (Verifiation 6-31 Aug. 2004) 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 6-h 4-h 2-h v2003 6-h 4-h 2-h v2004 6-h 4-h 2-h CCFP Diurnal cycle of convection Forecast Valid Time GMT

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

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

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

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

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

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

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