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Report of the Q2 Short Range QPF Discussion Group. Jon Ahlquist Curtis Marshall John McGinley - lead Dan Petersen D. J. Seo Jean Vieux. Q2 Operational Needs. Operational needs are mandating a short-range QPF component to Q2 Warm-season convection Flash flood prediction - PowerPoint PPT Presentation
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Report of the Q2 Short Range QPF Discussion Group
Jon Ahlquist Curtis Marshall John McGinley - leadDan PetersenD. J. Seo Jean Vieux
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
OH Valley Case Study-Using Models/Radar/Satellite to Compose QPF 1719z
Radar June 14 2005
OH Valley Case Study-Using Models/Radar/Satellite to Compose QPF
HPC Forecast qpf 18z-00z QPF Jun14-15 2005
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!
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
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.
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 2hr P hr
QPE Extrapolation Numerical Weather Prediction
Designing a Forecast/ Observation, QPE/ QPF Blending Scheme
wi wi wiwi
Forecasts 0H 1H 2H 3H
Observations 0H 1H 2H 3H
CorrelationsCoefficients/
Weights from Training Set
Forecast SetFor New Event
Post-processor
Optimum Forecast
Set
NWP and Extrapolation
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
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
WRF 1
NN
WRF 2WRF 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
t0
Ensemble at time = t0 Time weighting is applied to each member
(Number of members) =
(Number of models) x(Length of Forecast) /(Start Interval)
Each pair of runsHas a unique Initial condition basedon new satellite, surface and radar data.
Ensemble Probabilities:Threshold: 5 mm/3 hrat 12 GMT 13 Oct 04
>20
>40
> 60
>80
PrecipitationProbabilities %
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
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
Summary
Observation or Forecast
RadarObservation
S-C-G Model M-P Model
Parameters Z, dBZ Z, dBZ W, g m-3 Z, dBZ W, g m-3
Cell 1 47.7 50.9 3.12 55.8 5.35
Cell 2 46.1 46.7 1.67 53.3 3.81
Cell 3 44.4 43.9 1.11 54.1 4.23
Cell 4 47.4 49.8 2.65 48.3 1.98
Cell 5 46.4 47.6 1.90 52.1 3.28
thanks to Guifu Zhang
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
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