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

M. Fiorino :: 64M. Fiorino :: 64thth IHC Savannah GA 20100304 IHC Savannah GA 20100304

The Potential for Improved Tropical The Potential for Improved Tropical Cyclone Forecasts from High-Resolution Cyclone Forecasts from High-Resolution

Global Modeling –Global Modeling –Results from the HFIP 2009 Summer Results from the HFIP 2009 Summer

Experiment Experiment

Mike FiorinoGlobal Systems [email protected]

Jeff Whitaker and Tom HamillPhysical Sciences Division

Earth System Research LaboratoryBoulder, CO

3 March 2010


Hi-Res Global Modeling for HFIP 2009Hi-Res Global Modeling for HFIP 2009

TACC supercomputer – yes we can! run hi-TACC supercomputer – yes we can! run hi-res global model (> ECMWF) in near real res global model (> ECMWF) in near real time in the USAtime in the USA

FIMFIM – – FFlow-following low-following IIcosahedral cosahedral MModel odel (fim.noaa.gov) (IC = GFS GSI)(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)GFS (T382 ~ 45 km) 60-member EnKF – highest-ever global EnKF 21-member ensemble with EnKF


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


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


Mean Forecast Error for Hi-Res ModelsMean Forecast Error for Hi-Res Models

> 72 h : 8-14 cases

@ 48h : EnKF lower error, no clear benefit from hi res


Intensity Bias/Error for Hi-Res ModelsIntensity Bias/Error for Hi-Res Models

assimilate central pressure in EnKF IC : lower bias (-30 -15 kt)

improved Vmax error with hi-res


Ensembles for my :: what the Ensembles for my :: what the 22 tell me…tell me…

ensembles attempt to ‘sample’ the ensembles attempt to ‘sample’ the model error spacemodel error space

how well ensembles sample/forecast how well ensembles sample/forecast error depends on the ratio of ‘spread’ error depends on the ratio of ‘spread’ to ‘error’; ideal is 1:1 or spread ~ to ‘error’; ideal is 1:1 or spread ~ errorerror

potential for accurate ‘guidance on potential for accurate ‘guidance on the guidance’ or ‘forecast of forecast the guidance’ or ‘forecast of forecast error’error’

‘‘error’ = track forecast errorerror’ = track forecast error


21-member GFS(T382) Ensemble using ESRL EnKF21-member GFS(T382) Ensemble using ESRL EnKF03L.2009 aka BILL 200908170003L.2009 aka BILL 2009081700

“spread” = mean of the distance between each member and the ensemble mean position

initial spread ~ 25 nm


The Bob Gall Plot – depict intensity in the ensembles…The Bob Gall Plot – depict intensity in the ensembles…03L.2009 aka BILL 200908170003L.2009 aka BILL 2009081700

colorize track segment based on difference between member intensity and mean intensity – which members are intensifying v weakening


Spread v Error haloes… spread ~ error (good)Spread v Error haloes… spread ~ error (good)03L.2009 aka BILL 200908170003L.2009 aka BILL 2009081700

add verifying track and the forecast error halo

desired relationship –error ~ spread


Spread v Error for ECMWF EPS(T399)Spread v Error for ECMWF EPS(T399)03L.2009 aka BILL 200908170003L.2009 aka BILL 2009081700

EPS under dispersive –spread << error


Mean Spread v Mean ErrorMean Spread v Mean Error


Some takeaways…Some takeaways… HFIP summer 2009 test demonstrated that HFIP summer 2009 test demonstrated that

models with res ≥ ECMWF can be run in models with res ≥ ECMWF can be run in near real-time on USA computers…near real-time on USA computers…

hi-res, however, is not a sufficient hi-res, however, is not a sufficient condition…for track/intensity skill, i.e., we condition…for track/intensity skill, i.e., we can’t compute our way to improved TC can’t compute our way to improved TC forecasting with more CPUs… modeling forecasting with more CPUs… modeling challenge is still physics as Δx challenge is still physics as Δx 10 km 10 km

hi-res global ensembles showed both good hi-res global ensembles showed both good track forecast skill and spread/error ratios ~ track forecast skill and spread/error ratios ~ 1 1 potential for forecasting the forecast potential for forecasting the forecast error,but all systems under dispersiveerror,but all systems under dispersive


resources:resources:

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

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

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