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

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

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  1. 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

  2. Motivation • 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

  3. Warm 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 Perfect SREF

  4. 2010 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

  5. Models used in Spring Experiment Experimental QPF forecasts out to 30 h

  6. The 2010 Spring ExperimentQPF 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?

  7. Daily 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

  8. Experimental Ensemble Products Probability Matched Mean Max QPF (based on 4km SSEF members) PROB. MATCHED MEAN SSEF MEAN MAX QPF

  9. Experimental Ensemble Products Neighborhood Probabilities -probability of event within 80 km of a point NEPROB SSEF PROB

  10. Examples where Convection Allowing Deterministic Forecasts Improve upon Convective Parameterized Models

  11. CASE 1 30 h forecast of 6 hr QPF valid 06z 11 June 2010 GFS 35 KM 6hr QPE

  12. CASE 1 • 30 h forecast of 6 hr QPF valid 06z 11 June 2010 ECMWF 16 KM 6hr QPE

  13. CASE 1 • 30 h forecast of 6 hr QPF valid 06z 11 June 2010 NAM 12 KM 6hr QPE

  14. CASE 1 • 30 h forecast of 6 hr QPF valid 06z 11 June 2010 NSSL 4KM 6hr QPE

  15. CASE 2 • 24 h forecast of 6 hr QPF valid 00z 21 May 2010 NAM12 6hr QPE

  16. CASE 2 • 24 h forecast of 6 hr QPF valid 00z 21 May 2010 NSSL-ARW 4KM 6hr QPE

  17. CASE 2 • 24 h forecast of 6 hr QPF valid 00z 21 May 2010 NCEP-ARW 4KM 6hr QPE

  18. Examples where Storm Scale Ensemble Improves upon SREF Ensemble Forecasts

  19. CASE 1 • 30 h forecast of 6 hr QPF valid 06z 2 June 2010 SREF MEAN 32 KM 6hr QPE

  20. CASE 1 • 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

  21. CASE 2 • 24 h forecast of 6 hr QPF valid 00z 21May 2010 SREF MEAN 32 KM 6hr QPE

  22. CASE 2 • 24 h forecast of 6 hr QPF valid 00z 21May 2010 SSEF has correct areas of enhanced precipitation SSEF MEAN 4 KM 6hr QPE

  23. Examples where Convection Allowing Deterministic Forecasts Degrade NAM

  24. CASE 1 • 24 h forecast of 6 hr QPF valid 00z 2 June 2010 NAM12 KM 6hr QPE

  25. CASE 1 • 24 h forecast of 6 hr QPF valid 00z 2 June 2010 NCEP-ARW 4 km 6hr QPE

  26. CASE 1 • 24 h forecast of 6 hr QPF valid 00z 2 June 2010 CAM runs too far south SPC-NMM 4 KM 6hr QPE

  27. Example of NMM High Bias

  28. CASE 1 • 24 h forecast of 6 hr QPF valid 00z 21 May 2010 NAM-12 6hr QPE

  29. CASE 1 • 24 h forecast of 6 hr QPF valid 00z 21 May 2010 4 INCHES IN 6 HRS! SPC-NMM 6hr QPE

  30. Overall Results

  31. RESULTS SSEF NSSL CAPS 1 km EMC ARW EMC NMM NCAR

  32. RESULTS (cont) SSEF NSSL CAPS 1 km EMC ARW EMC NMM NCAR

  33. Results (cont)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

  34. LIMITATIONS/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?

  35. SUMMARY 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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