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Application of Short Range Ensemble Forecasts to Convective Aviation Forecasting

Application of Short Range Ensemble Forecasts to Convective Aviation Forecasting. David Bright NOAA/NWS/Storm Prediction Center Norman, OK Southwest Aviation Weather Safety Workshop October 23-24, 2008. Where Americas Climate and Weather Services Begin. Outline. Introduction

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Application of Short Range Ensemble Forecasts to Convective Aviation Forecasting

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  1. Application of Short Range Ensemble Forecasts to Convective Aviation Forecasting David Bright NOAA/NWS/Storm Prediction Center Norman, OK Southwest Aviation Weather Safety Workshop October 23-24, 2008 Where Americas Climate and Weather Services Begin

  2. Outline • Introduction • Short Range Ensemble Forecasts (SREF) • Future Aviation Ensemble Applications

  3. Outline • Introduction • The SPC, predictability, and ensembles • Short Range Ensemble Forecasts (SREF) • Future Aviation Ensemble Applications

  4. STORM PREDICTION CENTER LOCALIZED HIGH IMPACT WEATHER • Hail, Wind, Tornadoes • Fire Weather • Winter Weather • Excessive Rainfall

  5. The Butterfly Effect • Ensemble forecasting can be formally traced to the discovery of the "Butterfly Effect" (Lorenz 1963, 1965)… • The atmosphere is a non-linear, non-periodic, dynamical system such that tiny errors grow ... resulting in forecast uncertainty that increases with time resulting in limited predictability • “Does the flap of a butterfly’s wings in Brazil set off a tornado in Texas?” (Lorenz 1972) • Or, does the formation of an isolated storm over the Mogollon Rim set off a flash flood near Phoenix? • Predictability is a function of temporal and spatial scales: e.g., thunderstorms are inherently less predictable than synoptic scale cyclones

  6. What are the Sources of These Tiny Errors? Observations, Assimilation, and Models • Observations • Data gaps • Error • Representative • QC • Analysis • Models • LBCs, etc. Satellite Aircraft and Land

  7. Reality One model forecast

  8. Numerical weather models... • All forecasts contain errors that increase with time • Doubling time of small initial errors ~1 to 2 days • Maximum large-scale (synoptic to planetary) predictability ~10 to 14 days • Ensembles… • A collection of models that provide information on the range of plausible forecasts, statistical measures of confidence, and extend predictability • Scales to the problem of interest • Increasing in popularity • Requires “tools” to view the large number of models using a slightly different approach (statistical) Weather forecasting: It’s impossible to be perfectly correct all of the time!

  9. Outline • Introduction • Short Range Ensemble Forecasts (SREF) • Currently available through the SPC website • Future Aviation Ensemble Applications

  10. Ensemble Guidance at the SPC • Develop specialized guidance for the specific application (convection, severe storms, fire weather, winter weather) • Design guidance that… • Help blend deterministic and ensemble approaches • Provide guidance for uncertainty/probabilistic forecasts • Provide guidance that aids confidence (i.e., better deterministic forecasts) • Illustrates plausible scenarios • Allows for diagnostic analysis – not just a statistical black-box

  11. Ensembles Available at the SPC: SREF NWS/NCEP Short Range Ensemble Forecast (SREF) • EMC SREF system (21 members) • 87 hr forecasts four times daily (03, 09, 15, 21 UTC) • North American domain • Model grid lengths 32-45 km • Multi-model: Eta, RSM, WRF-NMM, WRF-ARW • Multi-analysis: NAM, GFS initial and boundary conds. • IC perturbations and physics diversity • Recently added bias-correction to some fields (not covered and not available on the SPC webpage)

  12. All example products available on the SPC webpage http://www.spc.noaa.gov/exper/sref/ http://w1.spc.woc.noaa.gov/exper/sref/frames.php?run=case_2007060803

  13. Basic Overview [MN]: = Mean [SP]: = Spaghetti [MNSD]: = Mean/SD [MAX]: Max value at each grid point

  14. SREF 500 mb Mean Height, Wind, Temp

  15. Instability [MN]: = Mean [PR]: Probability [MDXN]: Median/union & intersect [SP]: = Spaghetti [MNSD]: = Mean/SD

  16. SREF Pr[MUCAPE > 1000 J/kg] & Mean MUCAPE=1000 (dash)

  17. Winds and Vertical Shear [MN]: = Mean [SP]: = Spaghetti [MNSD]: = Mean/SD [MDXN]: Median/union & intersect [MAX]: Max value at each grid point

  18. SREF Pr[ESHR > 30 kts] & Mean ESHR=30 kts (dash) “Effective Shear” (ESHR; Thompson et al. 2007, WAF) is the bulk shear in the lower half of the convective cloud

  19. Precipitation [MN]: = Mean [SP]: = Spaghetti [MNSD]: = Mean/SD [MDXN]: Median/union & intersect [MAX]: Max value at each grid point

  20. SREF Pr[C03I > .01”] and Mean C03I = .01” (dash) C03I = 3hr Convective Precipitation

  21. Severe Weather [MN]: = Mean [SP]: = Spaghetti [MNSD]: = Mean/SD [MDXN]: Median/union & intersect [MAX]: Max value at each grid point

  22. SREF Combined or Joint Probability Probability of convection in moderate CAPE, moderate shear environment Pr [MUCAPE > 1000 J/kg] X Pr [ESHR > 30 kts] X Pr [C03I > 0.01”]

  23. Post-Processed Guidance [PR]: = Probability (Calibrated)

  24. SREF 3h Calibrated Probability of a Thunderstorm Thunderstorm = > 1 CG Lightning Strike in 40 km grid box (Bright et al., 2005) http://ams.confex.com/ams/pdfpapers/84173.pdf

  25. Calibrated SREF Thunder Reliability Frequency [0%, 5%, …] Perfect Forecast No Skill Climatology Calibrated Thunder Probability

  26. SREF 3h Calibrated Probability of a Severe Thunderstorm Severe Thunderstorm = > 1 CG Lightning Strike in 40 km grid box and Wind > 50 kts or Hail > 0.75” or Tornado (Bright and Wandishin, 2006) http://ams.confex.com/ams/pdfpapers/98458.pdf

  27. Severe Verification Hail > .75” Wind > 50 kts Tornado 21 UTC F039 24h forecasts from 15 April to 15 October 2005 ROC Area= .86 Ave Hit = 15% Ave Miss= 3% (24h Fcst: F39, 21Z only) 21 UTC SREF only

  28. Aviation Guidance [MN]: = Mean [MD]: Median/union & intersect [MAX]: Max value at each grid point [CPR]: Conditional probability [PR]: Probability

  29. SREF Probability Convective Cloud Top > 37 KFt

  30. Test Version: 03Z SREF Day 1 ENH Guidance (Summer 2008) Probability Echo Tops >= 35KFT

  31. Severe Weather on June 9, 2007 • 107 total severe reports in CONUS • 3 tornadoes over nrn New Mexico (~22 UTC)

  32. Outline • Introduction • Short Range Ensemble Forecasts (SREF) • Future Aviation Ensemble Applications • Applications and calibration under development • One hourly SREF thunderstorm guidance (through F036) * • Calibration of potential impacts of convection in SREF ^ • Rapid Refresh Ensemble Forecast (RREF) – 1hr updates, RUC based * • Storm scale (e.g., supercells, squall lines) applications being evaluated *Not discussed today ^Collaborating with John Huhn, Mitre Corp.

  33. Gridded Flight Composite (20 km)December 2007 to August 2008 – Above 250 KFT Probability (%) aircraft is inside grid box 20 km Grid (AWIPS 215) Data from John Huhn, Mitre

  34. Gridded Flight Composite (40 km)December 2007 to August 2008 21 UTC Composite < 10 KFT 21 UTC Composite > 25 KFT Probability (%) aircraft is inside grid box at exactly 21 UTC

  35. Example of Potential ImpactsProbability Thunderstorm X Gridded Composites SREF F036: 3h Calibrated Probability of a T-storm Valid: 21 UTC 8 June 2007 SREF F036: Impact Potential Below 10 KFT Valid: 21 UTC 8 June 2007 SREF F036: Impact Potential Above 25 KFT Valid: 21 UTC 8 June 2007

  36. Future Applications: Storm Scale Ensemble (SSEF) • NOAA Hazardous Weather Testbed (HWT) • HWT Spring Experiment • Focused on experimental high-res WRF forecasts since 2004 (dx ~2-4 km) • Convection allowing ensemble forecasts (2007-2009) to address uncertainty • 10 WRF members • 4 km grid length over 3/4 CONUS • Major contributions from: SPC, NSSL, OU/CAPS, EMC, NCAR • Resolving convection explicitly in the model • Evaluate the ability of convection allowing ensembles to predict: • Convective mode (i.e., type of severe wx) • Magnitude of severe type (e.g., peak wind) • Aviation impacts (e.g., convective lines/tops) • QPF/Excessive precipitation • Year 1 Objective (2007): Assess the role of physics vs. initial condition uncertainty at high resolution 2003 Spring Experiment

  37. Probability Updraft Helicity > 50 m2/s2 Probability of Supercell Thunderstorms F026: Valid 02 UTC 22 Apr 2008 UH > 50 + 25 mi

  38. Observed Radar Radar BREF 0142 UTC 22 Apr 2008

  39. Probability Updraft Helicity > 50 m2/s2 Jack Hales View of the left split looking south from Norman, OK (0145 UTC 22 Apr 2008) (Numerous large hail reports up to 2.25”)

  40. Convective Mode: Linear Detection • Determine contiguous areas exceeding 35 dbZ • Estimate mean length-to-width ratio of the contiguous area; search for ratios > 5:1 • Flag grid point if the length exceeds: • 200 miles

  41. Probability Linear Mode Exceeding 200 miles Squall Line Detection F024: Valid 00 UTC 18 Apr 2008 Linear mode + 25 miles

  42. Probability Linear Mode Exceeding 200 miles Squall Line Detection F026: Valid 02 UTC 18 Apr 2008 Linear mode + 25 miles

  43. Probability Linear Mode Exceeding 200 miles Squall Line Detection F028: Valid 04 UTC 18 Apr 2008 Linear mode + 25 miles

  44. Linear Convective Mode: Impacts Aviation impacts ~ 01 UTC 18 April 2008 Image provided by Jon Racy

  45. Summary: Ensemble Applications in Convective/Aviation Forecasting • Ensemble approach to forecasting has many similarities to the deterministic approach • Ingredients based inputs • Diagnostic and parameter evaluation • Ensembles contribute appropriate levels of confidence to the forecast process • Ability to view diagnostics and impacts in probability space • Calibration of ensemble output can remove systematic biases and improve the spread • Ensemble techniques scale to the problem of interest (weeks, days, or hours) • Ensemble systems are here to stay…and will evolve downscale toward high-impact forecasting

  46. SPC SREF Products on WEB http://www.spc.noaa.gov/exper/sref/ Questions/Comments… david.bright@noaa.gov

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