Lessons in predictability part 2 the march 2009 megastorm
Download
1 / 15

- PowerPoint PPT Presentation


  • 85 Views
  • Uploaded on

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.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about '' - evangelina


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
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


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


Deterministic major meteorological centers deterministic models and ensemble packages

GFS

Deterministic

ECMWF HR

Verifying

84 HR FCST


GEFS major meteorological centers deterministic models and ensemble packages

mean

8 member Poor Man’s Ensemble (GFS and EC)

Verifying

84 HR FCST


Deterministic major meteorological centers deterministic models and ensemble packages

GFS

Deterministic

ECMWF HR

Verifying

96 HR FCST


GEFS major meteorological centers deterministic models and ensemble packages

mean

8 member Poor Man’s Ensemble (GFS and EC)

Verifying

96 HR FCST


Deterministic major meteorological centers deterministic models and ensemble packages

GFS

Deterministic

ECMWF HR

Verifying

108 HR FCST


GEFS major meteorological centers deterministic models and ensemble packages

mean

8 member Poor Man’s Ensemble (GFS and EC)

Verifying

108 HR FCST


Calculation for 500 hpa flip flop tool results in units of decameters

Calculation for 500 hPa Flip Flop tool – results in units of decameters

________________________________

√(cycle-12hr-cycle-24hr)x(cyclecurrent-cycle-12hr)


500 hPa D-Prog/Dt Flip Flop Tool of decameters

GFS and ECMWF

84 HR

FCST


500 hPa D-Prog/Dt Flip Flop Tool of decameters

GFS and ECMWF

96 HR

FCST


500 hPa D-Prog/Dt Flip Flop Tool of decameters

GFS and ECMWF

108 HR

FCST



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


Conclusions introducing the lagged average forecast and flip flop tool
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?