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Mike Fiorino Global Systems Division michael.fiorino@noaa Jeff Whitaker and Tom Hamill

The Potential for Improved Tropical Cyclone Forecasts from High-Resolution Global Modeling – Results from the HFIP 2009 Summer Experiment. Mike Fiorino Global Systems Division michael.fiorino@noaa.gov Jeff Whitaker and Tom Hamill Physical Sciences Division Earth System Research Laboratory

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Mike Fiorino Global Systems Division michael.fiorino@noaa Jeff Whitaker and Tom Hamill

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  1. The Potential for Improved Tropical Cyclone Forecasts from High-Resolution Global Modeling –Results from the HFIP 2009 Summer Experiment Mike Fiorino Global Systems Division michael.fiorino@noaa.gov Jeff Whitaker and Tom Hamill Physical Sciences Division Earth System Research Laboratory Boulder, CO 3 March 2010

  2. Hi-Res Global Modeling for HFIP 2009 • TACC supercomputer – yes we can! run hi-res global model (> ECMWF) in near real time in the USA • FIM – Flow-following Icosahedral Model (fim.noaa.gov) (IC = GFS GSI) • (fim8, f8em) G8 – 30 km; 00Z/12 • (fim9, f9em) G9 – 15 km; 00/12Z • (f0em) G9.5 – 10 km; 00Z only • (f8XX) G8 – 39 km; 21-member ensemble • GFS (T382 ~ 45 km) • 60-member EnKF – highest-ever global EnKF • 21-member ensemble with EnKF

  3. FIM G5 grid (dx~240km) – NHC LANT AOR

  4. FIM G5 (~240 km) JTWC/CPHC Local AOR

  5. FIM G6 (~120 km) JTWC/CPHC Local AOR

  6. FIM G7 (~60 km) JTWC/CPHC Local AOR

  7. FIM G8 (~30 km) JTWC/CPHC Local AOR

  8. FIM G9 (~15 km) JTWC/CPHC Local AOR

  9. Mean Forecast Error for Hi-Res Models > 72 h : 8-14 cases @ 48h : EnKF lower error, no clear benefit from hi res

  10. Intensity Bias/Error for Hi-Res Models improved Vmax error with hi-res assimilate central pressure in EnKF IC : lower bias (-30  -15 kt)

  11. Ensembles for my :: what the 2tell me… • ensembles attempt to ‘sample’ the model error space • how well ensembles sample/forecast error depends on the ratio of ‘spread’ to ‘error’; ideal is 1:1 or spread ~ error • potential for accurate ‘guidance on the guidance’ or ‘forecast of forecast error’ • ‘error’ = track forecast error

  12. 21-member GFS(T382) Ensemble using ESRL EnKF03L.2009 aka BILL 2009081700 “spread” = mean of the distance between each member and the ensemble mean position initial spread ~ 25 nm

  13. The Bob Gall Plot – depict intensity in the ensembles…03L.2009 aka BILL 2009081700 colorize track segment based on difference between member intensity and mean intensity – which members are intensifying v weakening

  14. Spread v Error haloes… spread ~ error (good)03L.2009 aka BILL 2009081700 add verifying track and the forecast error halo desired relationship – error ~ spread

  15. Spread v Error for ECMWF EPS(T399)03L.2009 aka BILL 2009081700 EPS under dispersive – spread << error

  16. Mean Spread v Mean Error

  17. Some takeaways… • HFIP summer 2009 test demonstrated that models with res ≥ ECMWF can be run in near real-time on USA computers… • hi-res, however, is not a sufficient condition…for track/intensity skill, i.e., we can’t compute our way to improved TC forecasting with more CPUs… modeling challenge is still physics as Δx  10 km • hi-res global ensembles showed both good track forecast skill and spread/error ratios ~ 1 potential for forecasting the forecast error,but all systems under dispersive

  18. resources:FIM: http://fim.noaa.govCurrent TC EPS graphics: http://ruc.noaa.gov/hfip/tcepsCurrent TC EPS tracks (google map): http://ruc.noaa.gov/tracksPython S/W to analyze/display ATCF data:http://sourceforge.net/projects/wxmap2/

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