bruce sullivan, faye barthold, richard bann, mike bodner, david novak, and robert oravec

National Weather Service

EXPLORING THE USE OF EXPLORING THE USE OF CONVECTIVE ALLOWING GUIDANCE CONVECTIVE ALLOWING GUIDANCE

TO IMPROVE WARM SEASON TO IMPROVE WARM SEASON QUANTITATIVE PRECIPITATION QUANTITATIVE PRECIPITATION

FORECASTSFORECASTS

THE 2010 SPRING EXPERIMENTTHE 2010 SPRING EXPERIMENT

Bruce Sullivan, Faye Barthold, Richard Bann, Mike Bodner, David Novak, and Robert

OravecHydrometeorological Predication Center

Camp Springs, MD


MotivationMotivation

HPC Monthly 1.00" Threat Score (June 2005 - March 2010)

0

0.1

0.2

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0.5

0.6

Jun-0

5Oct Feb

Jun-0

6Oct Feb

Jun-0

7Oct Feb

Jun-0

8Oct Feb

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9Oct Feb

•Typically a warm-season phenomenon

•Flash flooding is a leading cause of weather-related deaths in the U.S. (~130 deaths annually)

•Warm-season QPF is difficult


Warm Season Forecasting ChallengesWarm Season Forecasting Challenges• Model initialization errors—limited observations on

convective scales• Mesoscale boundaries often dominate• Mishandling of MCVs• Model biases• Convection is parameterized in operational models

- Erroneous convective feedback- SREF not calibrated

0.50” in 6h @ F24

Perfec

t

SREF


2010 Spring Experiment2010 Spring Experiment• GOAL: Explore use of convection-allowing

models (~4 km grid spacing)• 3 components (Severe, Aviation, QPF)• 5 week program (May 17- June 18)• Participants included researchers,

academia, operational forecasters, students• Rotation thru desks• Facilitator at each desk


Models used in Spring ExperimentModels used in Spring Experiment

Experimental QPF forecasts out to 30 h

Provider Model Delta X Notes LabelCAPS WRF-ARPS

26 member ensemble

4 km Multi-model, multi-physics, multi-IC ensemble system with radar assimilation

Storm scale ensemble forecast (SSEF)

CAPS WRF-ARW 1 km 1 km resolution CAPS 1 km

NCAR WRF-ARW 3 km RUC ICs and GFS LBCs NCAR

NSSL WRF-ARW 4 km NAM ICs and LBCs NSSL

NCEP-EMC WRF-NMM 4 km NAM ICs and LBCs EMC-NMM

NCEP-EMC WRF-ARW 4 km NAM ICs and LBCs EMC-ARW


The 2010 Spring ExperimentThe 2010 Spring ExperimentQPF Objective/GoalsQPF Objective/Goals

• Document strengths and weaknesses of high res QPF forecasts

• Determine appropriate ways to use operational mesoscale and experimental CAMS/SSEF models in a complementary manner

• Explore creation of probabilistic QPF products

Simply put, do the high res models add value to

the warm season forecast problem?


Daily QPF ScheduleDaily QPF Schedule• Subjective verification of previous days forecast

• Synoptic overview• Produce experimental 6 hr probabilistic QPF

- .50” and 1” thresholds- Forecasts valid 18-00Z and 00-06Z

• Subjective evaluation of previous days experimental model guidance

• Afternoon briefing and discussion of daily forecasts

and evaluation activities


Experimental Ensemble ProductsExperimental Ensemble Products

• Probability Matched Mean• Max QPF (based on 4km SSEF members)

SSEF MEANPROB. MATCHED

MEAN MAX QPF


Experimental Ensemble ProductsExperimental Ensemble Products• Neighborhood Probabilities

-probability of event within 80 km of a point

SSEF PROB NEPROB


Examples where Examples where Convection Allowing Convection Allowing

Deterministic Forecasts Deterministic Forecasts Improve upon Convective Improve upon Convective

Parameterized ModelsParameterized Models


CASE 1CASE 1

• 30 h forecast of 6 hr QPF valid 06z 11 June 2010

6hr QPE GFS 35 KM


CASE 1CASE 1


6hr QPE ECMWF 16 KM


CASE 1CASE 1


6hr QPE NAM 12 KM


CASE 1CASE 1


6hr QPE NSSL 4KM


CASE 2CASE 2• 24 h forecast of 6 hr QPF valid 00z 21 May

2010

6hr QPE NAM12



2010

6hr QPE NSSL-ARW 4KM



2010

6hr QPE NCEP-ARW 4KM


Examples where Storm Scale Examples where Storm Scale Ensemble Improves upon Ensemble Improves upon SREF Ensemble ForecastsSREF Ensemble Forecasts


CASE 1CASE 1


6hr QPE SREF MEAN 32 KM


CASE 1CASE 1


6hr QPE SSEF MEAN 4 KM

SSEF CORRECTLY ADJUSTS MCS AN ENTIRE STATE SOUTH


CASE 2CASE 2• 24 h forecast of 6 hr QPF valid 00z 21May

2010

6hr QPE SREF MEAN 32 KM


CASE 2CASE 2

• 24 h forecast of 6 hr QPF valid 00z 21May 2010

6hr QPE SSEF MEAN 4 KM

SSEF has correct areas of enhanced precipitation


Examples where Examples where Convection Allowing Convection Allowing

Deterministic Forecasts Deterministic Forecasts Degrade NAMDegrade NAM


CASE 1CASE 1


6hr QPE NAM12 KM


CASE 1CASE 1


6hr QPE NCEP-ARW 4 km


CASE 1CASE 1


6hr QPE SPC-NMM 4 KM

CAM runs too far south


Example of Example of NMM High BiasNMM High Bias


CASE 1CASE 1

• 24 h forecast of 6 hr QPF valid 00z 21 May 2010

6hr QPE NAM-12


CASE 1CASE 1

• 24 h forecast of 6 hr QPF valid 00z 21 May 2010

6hr QPE SPC-NMM

4 INCHES IN 6 HRS!


Overall ResultsOverall Results


RESULTSRESULTS2010 HWT Spring Experiment

Percentage of Responses Indicating High Resolution Models Provided Improved Guidance

26/40

24/4214/28

11/2813/42 9/30

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

SSEF NSSLWRF-ARW

CAPSARW

NCEPHRW-ARW

SPCWRF NCARARW

Model

Per

cent

age

SSEF NSSL CAPS 1 km

EMC ARW

EMC NMM

NCAR


RESULTS (cont)RESULTS (cont)2010 HWT Spring Experiment

Percentage of Responses Indicating High Resolution Models Provided Worse Guidance

18/4212/30

7/287/287/42

3/40

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

SSEF NSSLWRF-ARW

CAPSARW

NCEPHRW-ARW

NCARARW

SPCWRF

Model

Per

cent

age

SSEF NSSL CAPS 1 km

EMC ARW

EMC NMM

NCAR


Results (cont)Results (cont)Post processed guidance (CAPS ensemble)Post processed guidance (CAPS ensemble)• Ensemble mean—useful, provided a realistic depiction of

amounts and coverage

• Probability matched mean—question about validity of using this technique on a national scale

-Recommendation: recalculate using a regional scheme

• Neighborhood probabilities—probabilities often too high and coverage too broad

-Recommendation: recalculate using different smoothing parameters

• Ensemble maximum precipitation—not useful, values too high


LIMITATIONS/CHALLENGESLIMITATIONS/CHALLENGES

• Model run time is long• Slow to load on operational workstations• Still have placement/amplitude

errors/failures• Experiment did not cover CONUS• How do we get the data to operations?• Can forecasters issue reliable probability

forecasts given current time and staffing constraints?


SUMMARYSUMMARY• Although certainly not perfect, convection-

allowing model guidance is useful and can improve warm season QPF- CAPS ensemble particularly impressive

• Further investigation needed to determine best way to incorporate guidance into the forecast process

bruce sullivan, faye barthold, richard bann, mike bodner, david novak, and robert oravec

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