1. Report of the Q2 Short Range QPF Discussion Group Jon Ahlquist Curtis Marshall John McGinley - lead Dan Petersen D. J. Seo Jean Vieux

2. Q2 Operational Needs Operational needs are mandating a short-range QPF component to Q2 –Warm-season convection Flash flood prediction –Tropical storms Flash floods Landslides –Winter precipitation Snow, freezing rain, mixed phase Transportation weather

3. OH Valley Case Study-Using Models/Radar/Satellite to Compose QPF 1719z Radar June 14 2005

4. OH Valley Case Study-Using Models/Radar/Satellite to Compose QPF HPC Forecast qpf 18z-00z QPF Jun14-15 2005

5. Q2 Definition The next generation multi-sensor precipitation product that leverages the national QPE mosiac and short range QPF Is a predictive component needed for a precipitation product? Yes!

6. Short Range QPF for Q2 Extrapolation/ Advection Methods –Centroid tracking –Radar cross-correlation and translation methods –Background wind advection methods –Kalman Filter methods Numerical Weather Prediction –Very high resolution mesoscale models –Advanced data assimilation - radar, satellite –Minimal spin-up - Diabatic initialization

7. A Proposed Q2 Vision Vision for Q2: An Integrated National Quantitative Precipitation Estimation/Forecast Product that allows a user to look at precipitation rates and accumulation for any period from the current hour, H, backward to H-A hr and forward to H+P hr. This would require blending a national mosaic with a short range forecast.

8. Q2 Vision (cont) Combine QPE, Extrapolation, and NWP in a seamless product extending backwards A hours and forward P hours from observation time Time A hour Current Time 1hr 2hrP hr QPE Extrapolation Numerical Weather Prediction

9. Designing a Forecast/ Observation, QPE/ QPF Blending Scheme wiwi wiwi wiwi wiwi Forecasts 0H 1H 2H 3H Observations 0H 1H 2H 3H Correlations Coefficients/ Weights from Training Set Forecast Set For New Event Post- processor Optimum Forecast Set NWP and Extrapolation

10. Path to operations Who runs Q2?: NCEP (with enhanced resources and staff) What are the needs for Short Range QPF in the context of a Q2 product suite? Advanced extrapolation schemes that smoothly propagate precipitation estimates; Numerical models with microphysics ingest, diabatic (cloud and precip) initial state Are models fully integrated within Q2? Yes, we see the QPE and QPF process run in an integrated process What is the role of ensembles in Q2? Provide an assessment of uncertainty in QPF Can a national product serve all needs? Yes, may have to use “tile” strategies to avoid excessive internet bandwidth

11. Use of Ensembles Are Ensembles a viable tool for Q2? Yes. We discussed not only ensembles for QPF but also QPE. How would they be employed? Consider running a number of QPE systems. There is enough uncertainty in QPE to justify a probabilistic approach. QPF could use multi model and/or time phased approach

13. WRF 1 N WRF 2 H H+1 H+2 H+3 H+4 H+5 Time-Phased Ensemble: an efficient way to get many members in limited computing environments Time t0t0 Ensemble at time = t 0 Time weighting is applied to each member (Number of members) = (Number of models) x (Length of Forecast) / (Start Interval) Each pair of runs Has a unique Initial condition based on new satellite, surface and radar data.

14. Ensemble Probabilities: Threshold: 5 mm/3 hr at 12 GMT 13 Oct 04 >20 >40 > 60 >80 Precipitation Probabilities %

15. Use of Ensembles QPF Enhancement/Correction –Technique aimed at improving single forecast (eg T. Hamil) Ensemble average, analog historical data set, detailed precipitation analyses Increased detail and accuracy Probabilistic QPF –Meets NWS long-term goals –Advanced post processing –Merging with user decision aids

16. Science Needs What is the needed science to add a QPF component to Q2? –Deterministic Short Range QPF Improved extrapolation procedures –Scale-decomposition methods – propagation/ amplitude –Maximizing length of useful forecast Improved Meso-models –Microphysics (capable of utilizing input data from Q2) –Surface Processes (past precipitation influencing surface heat and moisture flux) –Terrain Impacts Improved Initialization –Diabatic initial condition »Cloud and precipitation initialization »Error characteristics of moisture data – Product merging (post processing) Blending QPE and QPF Automated optimization (relative weights) of QPE and QPF components –Verification Q2 QPE comparisons with Stage IV precip analysis Improved precip verification

17. Summary Observation or Forecast Radar Observation S-C-G Model M-P Model ParametersZ, dBZ W, g m -3 Z, dBZW, g m -3 Cell 147.750.93.1255.85.35 Cell 246.146.71.6753.33.81 Cell 344.443.91.1154.14.23 Cell 447.449.82.6548.31.98 Cell 546.447.61.9052.13.28 thanks to Guifu Zhang

18. Science Needs What is the needed science to add a QPF component to Q2? (continued) –Model Ensemble Suite of QPE systems or at least an estimate of uncertainty from a single QPE Suite of extrapolation methods Suite of meso-models –Multi-model single initialization time (physics differences) –Single model, time phased (initialization differences) Probabilistic post processing –Precipitation probabilities –Precipitation correction schemes Verification –Appropriate Metrics

19. Recommendations Q2 should be a fully blended, continuous grid of observed and forecast precipitation QPF should include both extrapolation and NWP components, with optimal blending An enhanced (staff and facility) NCEP is proper place to create Q2 Ensembles and probabilities are needed for both QPE and QPF