Report of the Q2 Short Range QPF Discussion Group

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 QPFHPC 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 QPE Numerical Weather Prediction Extrapolation A hour Current Time 1hr 2hr P hr Time

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

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. Time-Phased Ensemble: an efficient way to get many members in limited computing environments t0 WRF 1 H H+1 H+2 H+3 H+4 H+5 WRF 2 Time Each pair of runs Has a unique Initial condition based on new satellite, surface and radar data. (Number of members) = (Number of models) x (Length of Forecast) / (Start Interval) N Time weighting is applied to each member Ensemble at time = t0

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

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 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