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