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

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

7. Relative Operating Characteristic (ROC) curves Show tradeoff: “detection” vs. “false-alarm” “Left and high” curve best Does probability beat model precip? POD POFD ----- probability ----- conv precip 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%

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. 5 pt, 1 mm 7 pt, 2 mm 9 pt, 2 mm9 pt, 4 mm % 10 20 30 40 50 60 70 80 90 Different box sizes and convective precip. thresholds give different probability fields Need to calculate statistical reliability to calibrate probabilities

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

11. 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) RUC Convective Probabilistic Forecast (RCPF) evolution

12. 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 Sample 2003 RUC product Verification display from RTVS Threshold probability forecast to get a categorical forecast

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

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

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

17. .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 GMT EDT 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

18. Bias by lead-time, time of day GMT EDT Diurnal cycle of convection 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 Fcst Lead Time

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

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

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

22. 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) Tradeoffs between reliability, resolution, sharpness FORECAST probability (/100) OBSERVED frequency (/100) Under forecast Over forecast Climatology perfect reliability Actual reliability Interpreting Reliability Plots

23. 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) FORECAST probability (/100) OBSERVED frequency (/100) Climatology perfect reliability RUC-NCWF 6-h fcsts valid 19z Under Over 0.10 0.08 0.06 0.04 0.02 0.00 FCST fract. areal cover. FORECAST probability (/100) 6-31 Aug. 2004

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

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

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. 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, 2 nd trip, unfolding, birds,) - Optimal uses (clear-air, stratiform precip., t-storms) RUC Radar Data Assimilation Plans

32. 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)

34. 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?

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

36. 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+4M1+5M2+6 M0+5M1+6M2+7 6-h RCPF inputs M0+6M1+7 M0+7M1+8 8-h RCPF inputs M0+8M1+9 M0+9 M# = # hours back to model initial time

37. Time-lagged ensemble inputs Forecast Valid Time (UTC) 12z 13z 14z 15z 16z 17z 18z 19z 20z 21z 22z 23z 00z RUC model forecasts (HHz+F) Initial Time 1 4z 15z 16z 17z 18z 19z 20z 21z 22z 23z 1 2z 13z 14z 15z 16z 17z 18z 19z 20z 21z Available RCPF has 2h latency 2 4 6 8 14z+2,3 13z+3,4 12z+4,5 14z+4,5 13z+5,6 12z+6,7 14z+6,7 13z+7,8 14z+8,9 13z+9 2 3 4 5 6 7 8 9 15+2,3 14+3,4 13+4,5 15+3,4 14+4,5 13+5,6 15+4,5 14+5,6 13+6,7 15+5,6 14+6,7 13+7,8 15+6,7 14+7,8 13+8,9 15+7,8 14+8,9 13+9,10 15+8,9 14+9,10 13+10,11 15+9,10 14+10,11 13+11,12 HHz = model intial time F = forecast length (h) 14z RCPF (16z CCFP) 15z RCPF (17z CCFP)