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EXPLORING THE USE OF CONVECTIVE ALLOWING GUIDANCE TO IMPROVE WARM SEASON QUANTITATIVE PRECIPITATION FORECASTS THE 2010 SPRING EXPERIMENT. Bruce Sullivan, Faye Barthold, Richard Bann, Mike Bodner, David Novak, and Robert Oravec Hydrometeorological Predication Center Camp Springs, MD. Motivation.

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EXPLORING THE USE OF CONVECTIVE ALLOWING GUIDANCE TO IMPROVE WARM SEASON QUANTITATIVE PRECIPITATION FORECASTSTHE 2010 SPRING EXPERIMENT

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

Hydrometeorological Predication Center

Camp Springs, MD


Motivation WARM SEASON QUANTITATIVE PRECIPITATION FORECASTS

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

  • Typically a warm-season phenomenon

  • Warm-season QPF is difficult


Warm season forecasting challenges
Warm Season Forecasting Challenges WARM SEASON QUANTITATIVE PRECIPITATION FORECASTS

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

Perfect

SREF


2010 spring experiment
2010 Spring Experiment WARM SEASON QUANTITATIVE PRECIPITATION FORECASTS

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 experiment
Models used in Spring Experiment WARM SEASON QUANTITATIVE PRECIPITATION FORECASTS

Experimental QPF forecasts out to 30 h


The 2010 spring experiment qpf objective goals
The 2010 Spring Experiment WARM SEASON QUANTITATIVE PRECIPITATION FORECASTSQPF 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 schedule
Daily QPF Schedule WARM SEASON QUANTITATIVE PRECIPITATION FORECASTS

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 products
Experimental Ensemble Products WARM SEASON QUANTITATIVE PRECIPITATION FORECASTS

Probability Matched Mean

Max QPF (based on 4km SSEF members)

PROB. MATCHED MEAN

SSEF MEAN

MAX QPF


Experimental ensemble products1
Experimental Ensemble Products WARM SEASON QUANTITATIVE PRECIPITATION FORECASTS

Neighborhood Probabilities

-probability of event within 80 km of a point

NEPROB

SSEF PROB


Examples where Convection Allowing Deterministic Forecasts Improve upon Convective Parameterized Models


Case 1
CASE 1 Improve upon Convective Parameterized Models

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

GFS 35 KM

6hr QPE


CASE 1 Improve upon Convective Parameterized Models

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

ECMWF 16 KM

6hr QPE


CASE 1 Improve upon Convective Parameterized Models

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

NAM 12 KM

6hr QPE


CASE 1 Improve upon Convective Parameterized Models

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

NSSL 4KM

6hr QPE


CASE 2 Improve upon Convective Parameterized Models

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

NAM12

6hr QPE


CASE 2 Improve upon Convective Parameterized Models

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

NSSL-ARW 4KM

6hr QPE


CASE 2 Improve upon Convective Parameterized Models

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

NCEP-ARW 4KM

6hr QPE



CASE 1 Ensemble Forecasts

  • 30 h forecast of 6 hr QPF valid 06z 2 June 2010

SREF MEAN 32 KM

6hr QPE


CASE 1 Ensemble Forecasts

  • 30 h forecast of 6 hr QPF valid 06z 2 June 2010

SSEF CORRECTLY ADJUSTS MCS AN ENTIRE STATE SOUTH

SSEF MEAN 4 KM

6hr QPE


CASE 2 Ensemble Forecasts

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

SREF MEAN 32 KM

6hr QPE


CASE 2 Ensemble Forecasts

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

SSEF has correct areas of enhanced precipitation

SSEF MEAN 4 KM

6hr QPE



CASE 1 Degrade NAM

  • 24 h forecast of 6 hr QPF valid 00z 2 June 2010

NAM12 KM

6hr QPE


CASE 1 Degrade NAM

  • 24 h forecast of 6 hr QPF valid 00z 2 June 2010

NCEP-ARW 4 km

6hr QPE


CASE 1 Degrade NAM

  • 24 h forecast of 6 hr QPF valid 00z 2 June 2010

CAM runs too far south

SPC-NMM 4 KM

6hr QPE



CASE 1 Degrade NAM

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

NAM-12

6hr QPE


CASE 1 Degrade NAM

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

4 INCHES IN 6 HRS!

SPC-NMM

6hr QPE


Overall Results Degrade NAM


Results
RESULTS Degrade NAM

SSEF

NSSL

CAPS 1 km

EMC ARW

EMC NMM

NCAR


Results cont
RESULTS (cont) Degrade NAM

SSEF

NSSL

CAPS 1 km

EMC ARW

EMC NMM

NCAR


Results (cont) Degrade NAMPost 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 challenges
LIMITATIONS/CHALLENGES Degrade NAM

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?


Summary
SUMMARY Degrade NAM

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


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