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Lessons in Predictability: Part 2 The March 2009 “Megastorm”

Lessons in Predictability: Part 2 The March 2009 “Megastorm”. Michael J. Bodner, NCEP/HPC Camp Springs, MD Richard H. Grumm, NWS WFO State College, PA Neil A. Stuart, NWS WFO Albany, NY. NROW 2009.

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Lessons in Predictability: Part 2 The March 2009 “Megastorm”

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  1. Lessons in Predictability: Part 2 The March 2009 “Megastorm” Michael J. Bodner, NCEP/HPC Camp Springs, MD Richard H. Grumm, NWS WFO State College, PA Neil A. Stuart, NWS WFO Albany, NY NROW 2009

  2. The storm was well predicted predicited at days 4-7 by the major meteorological centers deterministic models and ensemble packages

  3. Deterministic GFS Deterministic ECMWF HR Verifying 84 HR FCST

  4. GEFS mean 8 member Poor Man’s Ensemble (GFS and EC) Verifying 84 HR FCST

  5. Deterministic GFS Deterministic ECMWF HR Verifying 96 HR FCST

  6. GEFS mean 8 member Poor Man’s Ensemble (GFS and EC) Verifying 96 HR FCST

  7. Deterministic GFS Deterministic ECMWF HR Verifying 108 HR FCST

  8. GEFS mean 8 member Poor Man’s Ensemble (GFS and EC) Verifying 108 HR FCST

  9. Calculation for 500 hPa Flip Flop tool – results in units of decameters ________________________________ √(cycle-12hr-cycle-24hr)x(cyclecurrent-cycle-12hr)

  10. 500 hPa D-Prog/Dt Flip Flop Tool GFS and ECMWF 84 HR FCST

  11. 500 hPa D-Prog/Dt Flip Flop Tool GFS and ECMWF 96 HR FCST

  12. 500 hPa D-Prog/Dt Flip Flop Tool GFS and ECMWF 108 HR FCST

  13. This is what happened – Is this a “Megastorm?

  14. This was the first event of 2008-09 to effect all of the major eastern cities. The storm received a NESIS classification of “1”

  15. Conclusions - Introducing the Lagged Average Forecast and “Flip Flop” Tool • Lagged average forecast or “poor man’s ensemble” - average the 4 most recent deterministic runs of both the GFS and ECMWF. • Advantage of the LAF • Uses a multi model approach to ensemble forecasting • Does not lose resolution because multiple deterministic forecasts are being used instead of ensemble means and members • Less smoothing of key features • The “flip flop” tool algorithmically combines the 3 most recent deterministic model runs • Displays the magnitude of reverting trends (flip flops) when contrasting previous model runs. • Positive values indicate that the model “flip flopped.” • Both tools provide the forecaster a quantitative way to evaluate model trend and uncertainty for specific features • Both geographical and temporal evaluation of uncertainty, thereby increasing or decreasing forecast confidence. • Future work includes formal verification and looking at other model output parameters. Thank you for your time – Any questions?

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