Third Annual World Congress on Intelligent Transportation Systems

Third Annual World Congress on Intelligent Transportation Systems

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) 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 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 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) RUC convective precipitation forecast 3-h conv. precip. (mm) 5-h fcst valid 19z 4 Aug 2003 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 Does probability beat model precip? ----- probability ----- conv precip Relative detection vs. false-alarm Left and high curve best detection Show tradeoff: POD Operating Characteristic (ROC) curves Low prob Low

precip 25% High prob 9 pt, 4 mm High precip POFD Sample: 5-h fcst from 14z 04 Aug 2003 false detection 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 Different box sizes and convective precip. thresholds give different probability fields 7 pt, 2 mm 5 pt, 1 mm % 10 90 20

30 40 50 60 70 80 Need to calculate statistical reliability to calibrate probabilities 9 pt, 4 mm 9 pt, 2 mm Optimal threshold and length scale? 40% 25%

5-h fcst valid 19z 4 Aug 2003 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) Sample 2003 RUC product RUC Convective Probability Forecast 7-h fcst valid 21z 3 Aug 2003 5 pt, 1 mm / 3h, 40% thresh Threshold probability forecast to get a categorical forecast

POD=0.55 Bias = 1.4 CSI = 0.30 Verification display from RTVS 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 RCPF v2003 6h Fcst Forecast length

Forecast Valid Time GMT EDT Diurnal cycle of 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)

Diurnal variation of Precipitation Threshold Rate West of 104 deg. longitude, multiply threshold by 0.6 Higher threshold to reduce coverage Lower threshold to increase coverage Forecast Valid Time GM T EDT Threshold adjusted to optimize the forecast bias - Threshold likely too low at night (bias still too large) Comparison of RCPFv2003 and RCPFv2004 6h Forecast Forecast length

Diurnal cycle of convection Forecast Valid Time Verification for 26 day period (6-31 Aug. 2004) GM T EDT RCPFv2004 fcst is a 1-h older than RCPFv2003 RCPFv2004 has similar CSI, much improved bias RCPF v2003 (Verifiation 6-31 Aug. 2004) Fcst Lead Time

CSI by lead-time, time of day 6-h .24, .25 4-h .22, .23 2-h 6-h .18, .19 4-h .16, .17 2-h .14, .15 .12, .13 .10, .11 CCFP

RCPF v2004 .20, .21 Forecast Valid Time 6-h 4-h 2-h Diurnal cycle of convection GMT EDT v2003 6-h CCFP v2004 (Verifiation 6-31 Aug. 2004) Fcst Lead

Time Bias by lead-time, time of day 2.0-2.25 6-h 1.75-2.0 4-h 1.5-1.75 2-h 1.25-1.5 6-h 1.0-1.25 4-h 0.75-1.0 2-h

2.75-3.0 2.5-2.75 2.25-2.5 0.5-0.75 Forecast Valid Time 4-h 2-h Diurnal cycle of convection GMT EDT CSI vs. bias for 2003 vs. 2004 (6-h forecasts valid 19z) Low Probabilities 40% Points at 5% intervals 40%

High Probabilities RCPFv2004 fcst is a 1-h older than RCPFv2003 RCPFv2004 has better CSI for given bias value Sample RCPFv2004 product 13z + 6h Forecast 19z RCPF v2004 At fcst Time... 13z convection verif 25 49% 50 74% 75 100%

Verification 19z NCWD 10 Aug 2004 Sample RCPFv2004 product 15z + 6h Forecast 21z RCPF v2004 At fcst Time... 15z convection verif 25 49% 50 74% 75 100% Verification 21z NCWD 23 July 2004

Interpreting Reliability Plots 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 OBSERVED frequency (/100) RELIABILITY Tendency for forecast probabilities to be near extreme values (0%, 100%) (not hedging) Under forecast rf e p

y lit i b ia l re t ec Actual reliability Climatology Over forecast FORECAST probability (/100) Tradeoffs between reliability, resolution, sharpness 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% FCST fract. areal cover. RELIABILITY OBSERVED frequency (/100) RUC-NCWF 6-h fcsts valid 19z 6-31 Aug. 2004 rf e p

lit i b il a re t ec y Under Climatology Over FORECAST probability (/100) 0.10 0.08 0.06 0.04 0.02 0.00 FORECAST probability (/100) 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) Sample RCPF 2005 product 18z + 6h Forecast 16z + 8h Forecast 2005 RCPF 25 49% 50 74%

75 100% Verification 00z NCWD 8 Mar 2005 CCFP 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?)

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 2 hr Nowcast (scale - 60 km) Performance Forecast Closures groups in RUC Grell-Devenyi ensemble cumulus scheme

Radar 2100 UTC 10 July, 9-h 2002 fcst valid 21z 10 Jul 2002 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) 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 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 nd Sample 3DVAR analysis with radial velocity 0800 UTC Cint = 2 m/s 10 Nov 2004 Dodge City, KS * * * *

V r Dodge City, * Amarillo, TX KS ** V r Amarillo, TX 500 mb Height/Vorticity * K = 15 wind Vector s and speed

Analysis WITH radial velocity Cint = 1 m/s * * Analysis difference (WITH radial velocity minus without) 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? CONVECTIVE STORM TYPE Squall-line 30% Discernible from probability shape 50% Not as clear for other shapes 70% Scattered storms (high likelihood, 20% coverage) MCS

(20% likelihood, significant coverage) Storm-type affects correlation of adjacent probabilities, cumulative probability for flight track 30% 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 M# = # hours M0+9 back to model initial time Initial 12z 13z 14z 15z 16z 17z 18z 19z 20z 21z Time Available 14z 15z 16z 17z 18z 19z 20z 21z 22z 23z RCPF has 2h latency 12z Time-lagged ensemble inputs RUC model HHz = model intial forecasts time (HHz+F) F = forecast length

(h) 15+3,4 15+4,5 15z RCPF 15+2,3 14+3,4 14+4,5 14+5,6 (17z 13+4,5 13+5,6 13+6,7 CCFP) 2 3 4 2 14z RCPF (16z CCFP) 13z 14z 4 15+9,10 15+5,6 15+6,7 15+7,8 15+8,9 14+7,8 14+9,10 14+8,9 14+10,11

14+6,7 13+7,8 13+8,9 13+9,10 13+10,11 13+11,12 5 6 6 7 8 9 8 14z+2,3 14z+4,5 14z+6,7 14z+8,9 13z+3,4 13z+5,6 13z+7,8 13z+9 12z+4,5 12z+6,7 15z 16z 17z

18z 19z 20z 21z 22z 23z 00z Forecast Valid Time (UTC)

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