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Steve Weygandt Stan Benjamin Forecast Systems Laboratory NOAA

Hourly RUC Convective Probability Forecasts using Ensembles and Radar Assimilation. Steve Weygandt Stan Benjamin Forecast Systems Laboratory NOAA. AUTOMATED CONVECTIVE WEATHER GUIDANCE. PRESENT 0-2 h forecasts from radar extrapolation with growth and decay (nowcasting techniques)

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Steve Weygandt Stan Benjamin Forecast Systems Laboratory NOAA

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

  2. AUTOMATED CONVECTIVE WEATHER GUIDANCE • PRESENT • 0-2 h forecasts from radar extrapolation with • growth and decay (nowcasting techniques) • Beyond 2 h guidance from model output helpful • FUTURE • A seamless convective guidance product utilizing a variety of inputs including nowcasts and model ensemble informationto provide guidance to humans and automated decision support systems

  3. Model-based Probability Forecasts for Convective Weather • Principle: • Convective forecasts at specific model grid points from a single deterministic model run less likely to be correct than averages of model outputs. • Procedure: • Aggregate model convective information to • larger time/space scales (~1-2 h, 80-100 km) • Scales should increase with increasing lead time • Scales will decrease as models get better

  4. Ensembles provide technique for aggregating forecast information • Types of ensembles • Multi-model ensembles • Initial/boundary condition ensembles • Model physics ensembles • Time-lagged model ensembles (2004) • Model gridpoint ensembles (2003)

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

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

  7. Does probability beat model precip? ----- probability ----- conv precip • 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

  8. Gridpoint Ensembles • Adjustable parameters • Length scale • Precipitation Threshold • Inherent weaknesses • Constrained to single model run • Non-zero probability can only extend • out as far as the characteristic distance More ensemble information  better probabilities

  9. Different box sizes and convective precip. thresholds give different probability fields 5 pt, 1 mm 7 pt, 2 mm % 10 20 30 40 50 60 70 80 90 Need to calculate statistical reliability to calibrate probabilities 9 pt, 4 mm 9 pt, 2 mm

  10. Optimal threshold and length scale? 40% 25% 5-h fcst valid 19z 4 Aug 2003

  11. RUC Convective Probabilistic Forecast (RCPF) evolution • Automated convective probability forecast • Gridded fields derived from model ensembles • Real-time forecasts started 2003 (RCPFv2003) • Testing/improvements during 2004 (RCPFv2004) • 2-, 4-, 6-h forecasts every 2 hours (CCFP guidance) • Verification of forecasts by RTVS • AWC evaluation of product during 2005 • Merge with short-range techniques (NCAR/MIT)

  12. Sample 2003 RUC product 7-h fcst valid 21z 3 Aug 2003 RUC Convective Probability Forecast POD=0.55 Bias = 1.4 CSI = 0.30 5 pt, 1 mm / 3h, 40% thresh Threshold probability forecast to get a categorical forecast Verification display from RTVS

  13. 2003 verification of RCPFv2003 • RCPF most useful • for initial convective • development • RCPF bias too • large all times • except evening Threshold probability forecast at 40% to get categorical forecast 6h Fcst RCPF v2003 Forecast length Forecast Valid Time GMT EDT Diurnal cycle of convection

  14. Improvements to RCPF for 2004 • GOALS (maximize skill) • Reduce large bias (diurnal effects, western differences) • Improve spatial coherency, temporal consistency • Improve robustness • Reduce latency • ALGORITHM CHANGES • Increase filter size (9 GP east, 7 GP west) • Time-lagged ensemble (multiple hourly projections • from multiple RUC forecast cycles) • Diurnal cycle for precip. thresh. (maximum daytime, • minimum nightime; smaller value in the west) • Increase forecast lead time one hour (eg: 6-h fcst • from 13z valid 19z available at 1245z instead of 1345z)

  15. Diurnal variation of Precipitation Threshold Rate West of 104 deg. longitude, multiply threshold by 0.6 Lower threshold to increase coverage Higher threshold to reduce coverage GMT EDT Forecast Valid Time • Threshold adjusted to optimize the forecast bias - Threshold likely too low at night (bias still too large)

  16. Comparison of RCPFv2003 and RCPFv2004 6h Forecast Diurnal cycle of convection Forecast length GMT EDT Forecast Valid Time • Verification for 26 day period (6-31 Aug. 2004) • RCPFv2004fcst is a 1-h older than RCPFv2003 • RCPFv2004 has similar CSI, much improved bias

  17. 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 GMT EDT Forecast Valid Time

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

  19. CSI vs. bias for 2003 vs. 2004(6-h forecasts valid 19z) Low Probabilities Points at 5% intervals 40% 40% High Probabilities • RCPFv2004fcst is a 1-h older than RCPFv2003 • RCPFv2004 has better CSI for given bias value

  20. Sample RCPFv2004 product At fcst Time... 13z convection 25 – 49% 50 – 74% 75 – 100% 13z + 6h Forecast 19z verif RCPF v2004 Verification 19z NCWD 10 Aug 2004

  21. Sample RCPFv2004 product At fcst Time... 15z convection 25 – 49% 50 – 74% 75 – 100% 21z verif 15z + 6h Forecast RCPF v2004 Verification 21z NCWD 23 July 2004

  22. Interpreting Reliability Plots RELIABILITY For all 60% fcsts, event occurs 60% of time (45 deg line) RESOLUTION Strong change in obs freq for given change in fcst probability (vertical line) SHARPNESS Tendency for forecast probabilities to be near extreme values (0%, 100%) (not hedging) Under forecast perfect reliability Actual reliability OBSERVED frequency (/100) Climatology Over forecast FORECAST probability (/100) Tradeoffs between reliability, resolution, sharpness

  23. RUC-NCWF 6-h fcsts valid 19z • RELIABILITY • Better reliability • for 2004 vs. 2003 • Underfcst low prob., • overfcst high prob. • 2004 has many fewer • 0% prob. pts that • have convection • Fractional Coverage • 2004 has more low • prob. pts, fewer high • prob. pts • 2004 has fewer 0% • prob. pts (not shown) 6-31 Aug. 2004 perfect reliability OBSERVED frequency (/100) Under Over Climatology FORECAST probability (/100) 0.10 0.08 0.06 0.04 0.02 0.00 FCST fract. areal cover. FORECAST probability (/100)

  24. ACTIVITIES FOR 2005 • Dissemination and evaluation Realtime use and evaluation by AWC Hourly output and update frequency NCAR password protected web-site (model and radar extrapolation) • Ongoing product development Ensemble-based potential echo top information Use of ensemble cumulus closure information Upgrade from 20-km RUC to 13-km RUC Use of other RUC fields • Merge RCPF with NCWF2 (E-NCWF)

  25. Sample RCPF 2005 product 25 – 49% 50 – 74% 75 – 100% 18z + 6h Forecast 16z + 8h Forecast 2005 RCPF Verification 00z NCWD 8 Mar 2005 CCFP

  26. Sample Probability/Echo Top Display Probabilities shown with color shading Potential echo top height shown with black Lines (kft) -- Echo top from parcel overshoot level -- Contour echo top height at desired interval (3kft or 6kft?)

  27. Grell-Devenyi Cumulus Parameterization • Uses ensemble of closures: • Cape removal • Moisture convergence • Low-level vertical mass flux • Stability equilibrium • Includes multiple values for parameters: • Cloud radius (entrainment) • Detrainment (function of stability) • Precipitation efficiency (function of shear) • Convective inhibition threshold PRESENT:Mean from ensembles fed back to model FUTURE:Optimally weight ensembles closures, Use ensemble information to inprove probabilities

  28. Closures groups in RUC Grell-Devenyi ensemble cumulus scheme 2 hr Nowcast (scale - 60 km) Forecast Radar 2100 UTC 10 July, 2002 9-h fcst valid 21z 10 Jul 2002 Performance

  29. STRENGTHS OF MODEL GUIDANCE • Capturing initial convective development • Long lead-time and early morning forecasts • Improvements to the model and assimilation system lead directly to • improvements in probability forecasts • For RUC model: • Assimilate surface obs throughout PBL • 13-km horizontal resolution (June 2005) • Radar data assimilation • Full North American coverage (2007)

  30. ISSUES FOR MODEL GUIDANCE • Short-range forecasts (spin-up problem) • Poor performance for short-range forecast • does not invalidate longer-range forecasts • Propagation of convective systems • Robustness (spurious convection, complete misses) • Model bias issues Differences for parameterized vs. explicit treatments of convection

  31. RUC Radar Data Assimilation Plans • Reflectivity:mosaic data • NSSL pre-processing code transferred to NCEP • Integrate mosaic data into RUC cloud analysis • Couple to ensemble cumulus parameterization • Couple to model velocity fields • Radial Velocity:level II data • Generalized 3DVAR solver from lidar OSSE • Use horizontal projection of 3D radial velocity • Outstanding Issues • - Data thinning/superobbing • - Quality Control (AP, 2nd trip, unfolding, birds,) • - Optimal uses (clear-air, stratiform precip., t-storms)

  32. 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

  33. Thoughts and questions • Predictability very limited for small-scale convective precipitation features • Smoothing improves many scores • Smoothing alters spectra, probability information • Many “radar” approaches applicable • to model forecast precipitation fields • Probabilities from spatial variability of model precip. • Model depicts “displacement”, and “temporal evolution” • Apply “tracking” algorithms to model precipitation fields? • Many opportunities for blending model- and • radar-based techniques • Need extensive comparison to find “break even” points • Assess ability of radar and model for different tasks • Merge radar structure with model favored regions?

  34. CONVECTIVE STORM TYPE • Squall-line • Discernible from probability shape 30% 50% • Not as clear for other shapes • Scattered storms • (high likelihood, 20% coverage) • MCS • (20% likelihood, significant coverage) 70% 30% Storm-type affects correlation of adjacent probabilities, cumulative probability for flight track

  35. How is the RCPF created? 1. Gridpoint ensemble (for each model GP) - Fraction of 20-km model gridpoints within 9 x 9 box with 1-h convective precipitation exceeding threshold (use 7 x 7 km box west of 104 deg. Longitude) - Diurnal variation to 1-h convective precipitation threshold (smaller value for threshold west of 104 deg. longitude) 2. Time-lagged ensemble - Use up to six forecasts bracketing valid time - 9-h RUC forecast every hour with hourly output - 2-h latency to RUC model forecast output 4-h RCPF inputs M0+4 M1+5 M2+6 M0+5 M1+6 M2+7 6-h RCPF inputs M0+6 M1+7 M0+7 M1+8 8-h RCPF inputs M0+8 M1+9 M0+9 M# = # hours back to model initial time

  36. Time-lagged ensemble inputs RCPF has 2h latency RUC model forecasts (HHz+F) HHz = model intial time F = forecast length (h) 14z 15z 16z 17z 18z 19z 20z 21z 22z 23z 12z 13z 14z 15z 16z 17z 18z 19z 20z 21z 15+6,7 14+7,8 13+8,9 15+8,9 14+9,10 13+10,11 15+4,5 14+5,6 13+6,7 15+7,8 14+8,9 13+9,10 15+9,10 14+10,11 13+11,12 15+3,4 14+4,5 13+5,6 15+5,6 14+6,7 13+7,8 15+2,3 14+3,4 13+4,5 15z RCPF (17z CCFP) 2 3 4 5 6 7 8 9 2 4 6 8 14z+8,9 13z+9 14z+6,7 13z+7,8 14z+2,3 13z+3,4 12z+4,5 14z RCPF (16z CCFP) 14z+4,5 13z+5,6 12z+6,7 Available Initial Time 12z 13z 14z 15z 16z 17z 18z 19z 20z 21z 22z 23z 00z Forecast Valid Time (UTC)

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