1 Jamie Rhome WMO Workshop National Hurricane Center Jamie Rhome WMO Workshop National Hurricane Center NATIONAL CENTERS FOR ENVIRONMENTAL PREDICTION WHERE AMERICA’S CLIMATE AND WEATHER SERVICES BEGIN Tropical Cyclone Track Forecasting

2 OUTLINE: TRACK FORECASTING Factors affecting TC motion Factors affecting TC motion Guidance Models Guidance Models Climatology and Statistical modelsClimatology and Statistical models Beta and Advection ModelsBeta and Advection Models Dynamical ModelsDynamical Models Ensembles and ConsensusEnsembles and Consensus NHC Forecasting Guidelines NHC Forecasting Guidelines Using Initial MotionUsing Initial Motion ContinuityContinuity Bringing it All Together: Interpreting Track Models Bringing it All Together: Interpreting Track Models Exercise Exercise Factors affecting TC motion Factors affecting TC motion Guidance Models Guidance Models Climatology and Statistical modelsClimatology and Statistical models Beta and Advection ModelsBeta and Advection Models Dynamical ModelsDynamical Models Ensembles and ConsensusEnsembles and Consensus NHC Forecasting Guidelines NHC Forecasting Guidelines Using Initial MotionUsing Initial Motion ContinuityContinuity Bringing it All Together: Interpreting Track Models Bringing it All Together: Interpreting Track Models Exercise Exercise

3 Factors Affecting TC Motion: Large-scale Large-scale Vortex Moves with “Steering Flow”  main contributor to TC motionVortex Moves with “Steering Flow”  main contributor to TC motion Cyclone-scale Cyclone-scale Vortex induces beta-gyres and other asymmetries that affect motionVortex induces beta-gyres and other asymmetries that affect motion Convective distributionConvective distribution Vertical StructureVertical Structure Other Other Binary interaction (Fujiwhara effect)Binary interaction (Fujiwhara effect) Landmass interactionLandmass interaction Internal dynamics (trochoidal motion)Internal dynamics (trochoidal motion) Large-scale Large-scale Vortex Moves with “Steering Flow”  main contributor to TC motionVortex Moves with “Steering Flow”  main contributor to TC motion Cyclone-scale Cyclone-scale Vortex induces beta-gyres and other asymmetries that affect motionVortex induces beta-gyres and other asymmetries that affect motion Convective distributionConvective distribution Vertical StructureVertical Structure Other Other Binary interaction (Fujiwhara effect)Binary interaction (Fujiwhara effect) Landmass interactionLandmass interaction Internal dynamics (trochoidal motion)Internal dynamics (trochoidal motion)

4 Main Contributor to TC Motion The Large-scale Steering Flow is the Main Contributor to TC Motion H L L

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6 LOWER VALUES OF EARTH’S VORTICITY The Beta Effect β v>0 β v<0 N H L INDUCED STEERING 1-2 m/s NW INDUCED STEERING 1-2 m/s NW The circulation of a TC, combined with the North- South variation of the Coriolis parameter, induces asymmetries known as Beta Gyres. Beta Gyres produce a net steering current across the TC, generally toward the NW at a few knots. This motion is knows as the Beta Drift. HIGHER VALUES OF EARTH’S VORTICITY

7 Binary Interaction (Fujiwhara Effect) Fujiwhara effect—Occurs when two tropical cyclones become close enough (< 1450 km) to rotate cyclonically about each other as a result of their circulations' mutual advection. Fujiwhara effect—Occurs when two tropical cyclones become close enough (< 1450 km) to rotate cyclonically about each other as a result of their circulations' mutual advection. Named after Dr. Sakuhei Fujiwhara who initially described it in a 1921 paper about the motion of vortices in water Named after Dr. Sakuhei Fujiwhara who initially described it in a 1921 paper about the motion of vortices in water Most often occurs in the northwestern and eastern North Pacific basin…less often in the Atlantic. Most often occurs in the northwestern and eastern North Pacific basin…less often in the Atlantic. Presents a unique forecast challenge since the complex interplay results in different scenarios which determine the final result of the interaction Presents a unique forecast challenge since the complex interplay results in different scenarios which determine the final result of the interaction Some of the factors affecting the outcome of binary interaction include: comparable strength of the two cyclones, comparable size of the two cyclones, distance apart, background flow Some of the factors affecting the outcome of binary interaction include: comparable strength of the two cyclones, comparable size of the two cyclones, distance apart, background flow Fujiwhara effect—Occurs when two tropical cyclones become close enough (< 1450 km) to rotate cyclonically about each other as a result of their circulations' mutual advection. Fujiwhara effect—Occurs when two tropical cyclones become close enough (< 1450 km) to rotate cyclonically about each other as a result of their circulations' mutual advection. Named after Dr. Sakuhei Fujiwhara who initially described it in a 1921 paper about the motion of vortices in water Named after Dr. Sakuhei Fujiwhara who initially described it in a 1921 paper about the motion of vortices in water Most often occurs in the northwestern and eastern North Pacific basin…less often in the Atlantic. Most often occurs in the northwestern and eastern North Pacific basin…less often in the Atlantic. Presents a unique forecast challenge since the complex interplay results in different scenarios which determine the final result of the interaction Presents a unique forecast challenge since the complex interplay results in different scenarios which determine the final result of the interaction Some of the factors affecting the outcome of binary interaction include: comparable strength of the two cyclones, comparable size of the two cyclones, distance apart, background flow Some of the factors affecting the outcome of binary interaction include: comparable strength of the two cyclones, comparable size of the two cyclones, distance apart, background flow Relative rotation diagram of 12-h positions relative to the midpoints between Bopha (in solid typhoon symbol) and Saomai (in solid dot) based on a direct binary interaction interpretation (Wu et. al, 2003)

8 Trochoidal Motion (Wobble) Related to inner-core structure, convective asymmetries, and dynamic instability Related to inner-core structure, convective asymmetries, and dynamic instability Unable to forecast Unable to forecast Simply observeSimply observe Beware of the “wobble”Beware of the “wobble” Wait for a sustained (several hours) change in motionWait for a sustained (several hours) change in motion Related to inner-core structure, convective asymmetries, and dynamic instability Related to inner-core structure, convective asymmetries, and dynamic instability Unable to forecast Unable to forecast Simply observeSimply observe Beware of the “wobble”Beware of the “wobble” Wait for a sustained (several hours) change in motionWait for a sustained (several hours) change in motion

9 Hierarchy of TC Track Guidance Models: Statistical Statistical Forecasts based on established relationships between storm-specific information (i.e., location and time of year) and the behavior of previous stormsForecasts based on established relationships between storm-specific information (i.e., location and time of year) and the behavior of previous storms CLIPERCLIPER Statistical-Dynamical Statistical-Dynamical Statistical models that use information from dynamical model outputStatistical models that use information from dynamical model output NHC91 still maintains skill in the eastern PacificNHC91 still maintains skill in the eastern Pacific Simplified Dynamical Simplified Dynamical LBAR simple two-dimensional dynamical track prediction model that solves the shallow-water equations initialized with vertically averaged ( hPa) winds and heights from the GFS global modelLBAR simple two-dimensional dynamical track prediction model that solves the shallow-water equations initialized with vertically averaged ( hPa) winds and heights from the GFS global model BAMD, BAMM, BAMS -> Forecasts based on simplified dynamic representation of interaction with vortex and prevailing flow (trajectory)BAMD, BAMM, BAMS -> Forecasts based on simplified dynamic representation of interaction with vortex and prevailing flow (trajectory) Dynamical Models Dynamical Models Solve the physical equations of motion that govern the atmosphereSolve the physical equations of motion that govern the atmosphere GFDL, GFDN, GFS, NOGAPS, UKMET, ECMWF, NAM, (HWRF)GFDL, GFDN, GFS, NOGAPS, UKMET, ECMWF, NAM, (HWRF) Statistical Statistical Forecasts based on established relationships between storm-specific information (i.e., location and time of year) and the behavior of previous stormsForecasts based on established relationships between storm-specific information (i.e., location and time of year) and the behavior of previous storms CLIPERCLIPER Statistical-Dynamical Statistical-Dynamical Statistical models that use information from dynamical model outputStatistical models that use information from dynamical model output NHC91 still maintains skill in the eastern PacificNHC91 still maintains skill in the eastern Pacific Simplified Dynamical Simplified Dynamical LBAR simple two-dimensional dynamical track prediction model that solves the shallow-water equations initialized with vertically averaged ( hPa) winds and heights from the GFS global modelLBAR simple two-dimensional dynamical track prediction model that solves the shallow-water equations initialized with vertically averaged ( hPa) winds and heights from the GFS global model BAMD, BAMM, BAMS -> Forecasts based on simplified dynamic representation of interaction with vortex and prevailing flow (trajectory)BAMD, BAMM, BAMS -> Forecasts based on simplified dynamic representation of interaction with vortex and prevailing flow (trajectory) Dynamical Models Dynamical Models Solve the physical equations of motion that govern the atmosphereSolve the physical equations of motion that govern the atmosphere GFDL, GFDN, GFS, NOGAPS, UKMET, ECMWF, NAM, (HWRF)GFDL, GFDN, GFS, NOGAPS, UKMET, ECMWF, NAM, (HWRF)

10 CLIPER (CLImatology and PERsistence) Model Statistical track model developed in 1972, extended to 120 h in 1998 Statistical track model developed in 1972, extended to 120 h in 1998 Required Input: Required Input: Current/12 h old speed/direction of motionCurrent/12 h old speed/direction of motion Current latitude/longitudeCurrent latitude/longitude Julian Day, Storm maximum windJulian Day, Storm maximum wind Average 24, 48, 72, 96 and 120 h errors: 100, 216, 318, 419, and 510 nautical miles respectively Average 24, 48, 72, 96 and 120 h errors: 100, 216, 318, 419, and 510 nautical miles respectively Used as a benchmark for other models and subjective forecasts; forecasts with errors greater than CLIPER are considered to have no skill. Used as a benchmark for other models and subjective forecasts; forecasts with errors greater than CLIPER are considered to have no skill. Statistical track model developed in 1972, extended to 120 h in 1998 Statistical track model developed in 1972, extended to 120 h in 1998 Required Input: Required Input: Current/12 h old speed/direction of motionCurrent/12 h old speed/direction of motion Current latitude/longitudeCurrent latitude/longitude Julian Day, Storm maximum windJulian Day, Storm maximum wind Average 24, 48, 72, 96 and 120 h errors: 100, 216, 318, 419, and 510 nautical miles respectively Average 24, 48, 72, 96 and 120 h errors: 100, 216, 318, 419, and 510 nautical miles respectively Used as a benchmark for other models and subjective forecasts; forecasts with errors greater than CLIPER are considered to have no skill. Used as a benchmark for other models and subjective forecasts; forecasts with errors greater than CLIPER are considered to have no skill.

11 Beta and Advection Model (BAM) Beta and Advection Model (BAM) Method: Steering (trajectories) given by layer-averaged winds from a global model (horizontally smoothed to T25 resolution), plus a correction term to simulate the so-called “Beta Effect” Three different layer averages: Shallow ( MB) - BAMS Medium ( MB) - BAMM Deep ( MB) - BAMD Method: Steering (trajectories) given by layer-averaged winds from a global model (horizontally smoothed to T25 resolution), plus a correction term to simulate the so-called “Beta Effect” Three different layer averages: Shallow ( MB) - BAMS Medium ( MB) - BAMM Deep ( MB) - BAMD

12 5,000 ft/850 mb 200 mb Typical cruising altitude of commercial airplane Typical cruising altitude of commercial airplane Surface 700 mb 400 mb WHICH BAM TO USE? L L SHALLOW TROPICAL DEPRESSION L L MEDIUM TROPICAL STORM / CAT. 1-2 HURRICANE L L DEEP MAJOR HURRICANE

13 LBAR (Limited-area BARotropic) Barotropic dynamics, i.e. 2-d motions – no temperature gradients or vertical shear Barotropic dynamics, i.e. 2-d motions – no temperature gradients or vertical shear Lack of baroclinic forcing means the model has little or no skill beyond 1-2 daysLack of baroclinic forcing means the model has little or no skill beyond 1-2 days Shallow water equations on Mercator projection solved using sine transforms Shallow water equations on Mercator projection solved using sine transforms Initialized with mb layer average winds/heights from NCEP global model (GFS) Initialized with mb layer average winds/heights from NCEP global model (GFS) Sum of idealized vortex and current motion vector added to large-scale analysis Sum of idealized vortex and current motion vector added to large-scale analysis Boundary conditions from global model Boundary conditions from global model Barotropic dynamics, i.e. 2-d motions – no temperature gradients or vertical shear Barotropic dynamics, i.e. 2-d motions – no temperature gradients or vertical shear Lack of baroclinic forcing means the model has little or no skill beyond 1-2 daysLack of baroclinic forcing means the model has little or no skill beyond 1-2 days Shallow water equations on Mercator projection solved using sine transforms Shallow water equations on Mercator projection solved using sine transforms Initialized with mb layer average winds/heights from NCEP global model (GFS) Initialized with mb layer average winds/heights from NCEP global model (GFS) Sum of idealized vortex and current motion vector added to large-scale analysis Sum of idealized vortex and current motion vector added to large-scale analysis Boundary conditions from global model Boundary conditions from global model

14 relocates the first-guess TC vortex U.S. NWS Global Forecast System (GFS) < relocates the first-guess TC vortex United Kingdom Met. Office (UKMET) < bogus (syn. data) U.S. Navy Operational Global Atmospheric Prediction System (NOGAPS) < bogus (syn. data) U.S. NWS Geophysical Fluid Dynamics Laboratory (GFDL) model <bogus (spinup vortex) GFDN- Navy version of GFDL <bogus (spinup vortex) European Center for Medium-range Weather Forecasting (ECMWF) model (no bogus) relocates the first-guess TC vortex U.S. NWS Global Forecast System (GFS) < relocates the first-guess TC vortex United Kingdom Met. Office (UKMET) < bogus (syn. data) U.S. Navy Operational Global Atmospheric Prediction System (NOGAPS) < bogus (syn. data) U.S. NWS Geophysical Fluid Dynamics Laboratory (GFDL) model <bogus (spinup vortex) GFDN- Navy version of GFDL <bogus (spinup vortex) European Center for Medium-range Weather Forecasting (ECMWF) model (no bogus) Primary Dynamical Models used at NHC

15BogussingBogussing Since the globally analyzed vortex does not typically represent the structure of a true TC, “Bogussing” is often employed. Since the globally analyzed vortex does not typically represent the structure of a true TC, “Bogussing” is often employed. Bogussing involves an analysis of synthetic data to describe the TC vortex. Bogussing involves an analysis of synthetic data to describe the TC vortex. Bogussing can significantly affect the surrounding environmentBogussing can significantly affect the surrounding environment vertical shearvertical shear Creating and inserting a bogus is not straight forward Creating and inserting a bogus is not straight forward Forecast can be very sensitive to small changes in the bogus stormForecast can be very sensitive to small changes in the bogus storm Bogus storms tend to be too resilient during ET Bogus storms tend to be too resilient during ET Bogus retains warm core too long leading to poor intensity and structure forecastsBogus retains warm core too long leading to poor intensity and structure forecasts Since the globally analyzed vortex does not typically represent the structure of a true TC, “Bogussing” is often employed. Since the globally analyzed vortex does not typically represent the structure of a true TC, “Bogussing” is often employed. Bogussing involves an analysis of synthetic data to describe the TC vortex. Bogussing involves an analysis of synthetic data to describe the TC vortex. Bogussing can significantly affect the surrounding environmentBogussing can significantly affect the surrounding environment vertical shearvertical shear Creating and inserting a bogus is not straight forward Creating and inserting a bogus is not straight forward Forecast can be very sensitive to small changes in the bogus stormForecast can be very sensitive to small changes in the bogus storm Bogus storms tend to be too resilient during ET Bogus storms tend to be too resilient during ET Bogus retains warm core too long leading to poor intensity and structure forecastsBogus retains warm core too long leading to poor intensity and structure forecasts

16 The NCEP Global Forecast System (GFS) Global spectral model truncated at total wave number T382L64 (equivalent to about 40-km horizontal grid spacing with 64 vertical sigma levels) out to 180 hours T190L64 (equivalent to about 80-km grid spacing and 64 levels) out to 384 hours 3D-Var initialization globally analyzed vortex relocated to NHC position Simplified Arakawa-Schubert (SAS) convective parameterization scheme First-order closure method to represent the PBL (non-local) actual mixing during one time step only occurs between adjacent vertical levels so PBL may behave similar to a local scheme Global spectral model truncated at total wave number T382L64 (equivalent to about 40-km horizontal grid spacing with 64 vertical sigma levels) out to 180 hours T190L64 (equivalent to about 80-km grid spacing and 64 levels) out to 384 hours 3D-Var initialization globally analyzed vortex relocated to NHC position Simplified Arakawa-Schubert (SAS) convective parameterization scheme First-order closure method to represent the PBL (non-local) actual mixing during one time step only occurs between adjacent vertical levels so PBL may behave similar to a local scheme

17 Non-hydrostatic global model Non-hydrostatic global model 4-D VAR analysis scheme with bogus TC 4-D VAR analysis scheme with bogus TC Arakawa C-grid: east-west horizontal grid spacing of 0.5° longitude and a north-south grid spacing of 0.4° latitude Arakawa C-grid: east-west horizontal grid spacing of 0.5° longitude and a north-south grid spacing of 0.4° latitude (~40 km at mid- latitudes) Hybrid vertical coordinate system with 50 levels In 2002, completely new formulation including new dynamical core, fundamental equations, and physical parameterizations Run twice daily at 0000Z and 1200Z producing forecasts for up to 144 hours (6 days) Intermediate runs at 0600Z and 1800Z, but only produce forecasts to 48 hoursIntermediate runs at 0600Z and 1800Z, but only produce forecasts to 48 hours Non-hydrostatic global model Non-hydrostatic global model 4-D VAR analysis scheme with bogus TC 4-D VAR analysis scheme with bogus TC Arakawa C-grid: east-west horizontal grid spacing of 0.5° longitude and a north-south grid spacing of 0.4° latitude Arakawa C-grid: east-west horizontal grid spacing of 0.5° longitude and a north-south grid spacing of 0.4° latitude (~40 km at mid- latitudes) Hybrid vertical coordinate system with 50 levels In 2002, completely new formulation including new dynamical core, fundamental equations, and physical parameterizations Run twice daily at 0000Z and 1200Z producing forecasts for up to 144 hours (6 days) Intermediate runs at 0600Z and 1800Z, but only produce forecasts to 48 hoursIntermediate runs at 0600Z and 1800Z, but only produce forecasts to 48 hours The U.K. Met. Office Model

18 Global spectral model Global spectral model T382L64 (~ 35-km horizontal grid spacing with 64 vertical levels) through 180 hours. T382L64 (~ 35-km horizontal grid spacing with 64 vertical levels) through 180 hours. T190L64 (~ 80-km grid spacing and 64 levels) hours T190L64 (~ 80-km grid spacing and 64 levels) hours Hybrid sigma-pressure vertical coordinate system (May 2007) Hybrid sigma-pressure vertical coordinate system (May 2007) Simplified Arakawa-Schubert (SAS) convective parameterization scheme Simplified Arakawa-Schubert (SAS) convective parameterization scheme PBL: First-order closure method PBL: First-order closure method 3D-Var Gridpoint Statistical Interpolation (GSI) (May 2007) 3D-Var Gridpoint Statistical Interpolation (GSI) (May 2007) Rather than bogussing, the GFS relocates the first-guess TC vortex to the official NHC position. Rather than bogussing, the GFS relocates the first-guess TC vortex to the official NHC position. Often leads to an incomplete representation of the true TC structure Often leads to an incomplete representation of the true TC structure Run four times per day (00, 06, 12, and 18 UTC) out to 384 hours Run four times per day (00, 06, 12, and 18 UTC) out to 384 hours Global spectral model Global spectral model T382L64 (~ 35-km horizontal grid spacing with 64 vertical levels) through 180 hours. T382L64 (~ 35-km horizontal grid spacing with 64 vertical levels) through 180 hours. T190L64 (~ 80-km grid spacing and 64 levels) hours T190L64 (~ 80-km grid spacing and 64 levels) hours Hybrid sigma-pressure vertical coordinate system (May 2007) Hybrid sigma-pressure vertical coordinate system (May 2007) Simplified Arakawa-Schubert (SAS) convective parameterization scheme Simplified Arakawa-Schubert (SAS) convective parameterization scheme PBL: First-order closure method PBL: First-order closure method 3D-Var Gridpoint Statistical Interpolation (GSI) (May 2007) 3D-Var Gridpoint Statistical Interpolation (GSI) (May 2007) Rather than bogussing, the GFS relocates the first-guess TC vortex to the official NHC position. Rather than bogussing, the GFS relocates the first-guess TC vortex to the official NHC position. Often leads to an incomplete representation of the true TC structure Often leads to an incomplete representation of the true TC structure Run four times per day (00, 06, 12, and 18 UTC) out to 384 hours Run four times per day (00, 06, 12, and 18 UTC) out to 384 hours The Global Forecast System (GFS)

19 NOGAPS Model Global spectral model: T239L30 (approximately 55 km and 30 vertical levels) Hybrid sigma-pressure vertical coordinate system ~ six terrain-following sigma levels below 850 mb and remaining 24 pressure levels occurring above 850 mb. Time step is five minutes, but is reduced if necessary to prevent numerical instability associated with fast moving weather features. 3-D VAR analysis scheme Run 144 hours at each of the synoptic times. Emanuel convective parameterization scheme with non-precipitating convective mixing based on the Tiedtke method. Like other global models, the NOGAPS cannot provide skillful intensity forecasts but can provide skillful track forecasts. Global spectral model: T239L30 (approximately 55 km and 30 vertical levels) Hybrid sigma-pressure vertical coordinate system ~ six terrain-following sigma levels below 850 mb and remaining 24 pressure levels occurring above 850 mb. Time step is five minutes, but is reduced if necessary to prevent numerical instability associated with fast moving weather features. 3-D VAR analysis scheme Run 144 hours at each of the synoptic times. Emanuel convective parameterization scheme with non-precipitating convective mixing based on the Tiedtke method. Like other global models, the NOGAPS cannot provide skillful intensity forecasts but can provide skillful track forecasts.

20 ECMWF Model Considered one of the most sophisticated and computationally expensive of all the global models currently used by the NHC. Among the latest of all available dynamical model guidance. Hydrostatic global model: T799L91 (approximately 25 km and 91 vertical levels) Hybrid vertical coordinate system with as many levels in the lowest 1.5 km of the model atmosphere as in the highest 45 km. (4-D Var) analysis scheme Provides forecasts out to 240 hours (10 days). Even though there is no bogussing or relocation (i.e. no specific treatment of TCs in the initialization), the model produces credible forecasts of TC track. Considered one of the most sophisticated and computationally expensive of all the global models currently used by the NHC. Among the latest of all available dynamical model guidance. Hydrostatic global model: T799L91 (approximately 25 km and 91 vertical levels) Hybrid vertical coordinate system with as many levels in the lowest 1.5 km of the model atmosphere as in the highest 45 km. (4-D Var) analysis scheme Provides forecasts out to 240 hours (10 days). Even though there is no bogussing or relocation (i.e. no specific treatment of TCs in the initialization), the model produces credible forecasts of TC track.

21 THE GEOPHYSICAL FLUID DYNAMICS LABORATORY (GFDL) HURRICANE MODEL: Only purely dynamical model capable of producing skillful intensity forecasts Coupled with a high-resolution version of the Princeton Ocean Model (POM) (1/6° horizontal resolution with 23 vertical sigma levels) Replaces the GFS vortex with an axisymmetric vortex spun up in a separate model simulation Sigma vertical coordinate system with 42 vertical levels Limited-area domain (not global) with 2 grids nested within the parent grid. Outer grid spans 75°x75° at 1/2° resolution or approximately 30 km. Middle grid spans 11°x11° at 1/6° resolution or approximately 15 km. Inner grid spans 5°x5° at 1/12° resolution or approximately 7.5 km Only purely dynamical model capable of producing skillful intensity forecasts Coupled with a high-resolution version of the Princeton Ocean Model (POM) (1/6° horizontal resolution with 23 vertical sigma levels) Replaces the GFS vortex with an axisymmetric vortex spun up in a separate model simulation Sigma vertical coordinate system with 42 vertical levels Limited-area domain (not global) with 2 grids nested within the parent grid. Outer grid spans 75°x75° at 1/2° resolution or approximately 30 km. Middle grid spans 11°x11° at 1/6° resolution or approximately 15 km. Inner grid spans 5°x5° at 1/12° resolution or approximately 7.5 km

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23 THE HURRICANE WEATHER RESEARCH & FORECASTING (HWRF) PREDICTION SYSTEM Next generation non-hydrostatic weather research and hurricane prediction system Movable, 2- way nested grid (9km; 27km/42L; ~68X68) Coupled with Princeton Ocean Model 3-D VAR data assimilation scheme But with more advanced data assimilation for hurricane core (make use of airborne doppler radar obs and land based radar) Operational this season (under development since 2002) Will run in parallel with the GFDL Next generation non-hydrostatic weather research and hurricane prediction system Movable, 2- way nested grid (9km; 27km/42L; ~68X68) Coupled with Princeton Ocean Model 3-D VAR data assimilation scheme But with more advanced data assimilation for hurricane core (make use of airborne doppler radar obs and land based radar) Operational this season (under development since 2002) Will run in parallel with the GFDL

24 HWRF GFDL HWRF GFDL Grid configuration 2-nests (coincident) 3-nests(not coincident) NestingForce-feedback Interaction thru intra- nest fluxes Convective parameterization SAS mom.mix. Explicit condensation FerrierFerrier Boundary layer GFS non-local Surface layer GFDL..(Moon et. al.) Land surface model GFDL slab Dissipative heating Based on D-L Zhang Based on M-Y tke 2.5 Radiation GFDL (cloud differences) GFDL

25 HWRF Hurricane Wilma

26 Models not available at synoptic time are known as “Late Models” Dynamical models are not usually available until 4-6 hrs after the initial synoptic time (i.e., the 12Z run is not available until as late as ~18Z in real time) Results must be interpolated to latest NHC position (GFDL  GFDI, NGP  NGPI, etc) Models available shortly after synoptic time are known as “Early Models” Late Models: GFS, UKMET, NOGAPS, GFDL, GFDN Early Models: LBAR, BAM, AND CLIPER Models not available at synoptic time are known as “Late Models” Dynamical models are not usually available until 4-6 hrs after the initial synoptic time (i.e., the 12Z run is not available until as late as ~18Z in real time) Results must be interpolated to latest NHC position (GFDL  GFDI, NGP  NGPI, etc) Models available shortly after synoptic time are known as “Early Models” Late Models: GFS, UKMET, NOGAPS, GFDL, GFDN Early Models: LBAR, BAM, AND CLIPER “LATE” VS. “EARLY”

27 Ensemble Forecasts (Classic Method) A number of forecasts are made with a single model using perturbed initial conditions that represent the likely initial analysis error distribution Each different model forecast is known as a “member model” The spread of the various member models indicates uncertainty small spread among the member model may imply high confidence large spread among the member model may imply low confidence A number of forecasts are made with a single model using perturbed initial conditions that represent the likely initial analysis error distribution Each different model forecast is known as a “member model” The spread of the various member models indicates uncertainty small spread among the member model may imply high confidence large spread among the member model may imply low confidence

28 GFS ENSEMBLE FOR RITA – 9/19/05 12Z

29 Ensemble Forecasts (multi-model method) A group of forecast tracks from DIFFERENT PREDICTION MODELS (i.e. GFDL, UKMET, NOGAPS, ETC.) at the SAME INITIAL TIME A multi-model ensemble is usually superior to an ensemble from a single model different models typically have different biases, or random errors that will cancel or offset each other when combined. The multi-model ensemble is often called a CONSENSUS forecast. Primary Consensus forecasts used at NHC GUNA CONU FSSE A group of forecast tracks from DIFFERENT PREDICTION MODELS (i.e. GFDL, UKMET, NOGAPS, ETC.) at the SAME INITIAL TIME A multi-model ensemble is usually superior to an ensemble from a single model different models typically have different biases, or random errors that will cancel or offset each other when combined. The multi-model ensemble is often called a CONSENSUS forecast. Primary Consensus forecasts used at NHC GUNA CONU FSSE

30 Ensemble Forecasts (multi-model method) GUNA: a simple track consensus calculated by averaging the track guidance provided by the GFDI, UKMI, NGPI, and GFSI models. All four member models must be available to compute GUNA. CONU: a simple track consensus calculated by averaging the track guidance provided by the GFDI, UKMI, NGPI, GFNI, and GFSI models. CONU only requires two of the five member models. The FSSE is not a simple average of the member models. Rather, the FSSE is constantly learning by using the performance of past member model forecasts along with the previous official NHC forecast in an effort to correct biases FSSE: The FSSE is not a simple average of the member models. Rather, the FSSE is constantly learning by using the performance of past member model forecasts along with the previous official NHC forecast in an effort to correct biases GUNA: a simple track consensus calculated by averaging the track guidance provided by the GFDI, UKMI, NGPI, and GFSI models. All four member models must be available to compute GUNA. CONU: a simple track consensus calculated by averaging the track guidance provided by the GFDI, UKMI, NGPI, GFNI, and GFSI models. CONU only requires two of the five member models. The FSSE is not a simple average of the member models. Rather, the FSSE is constantly learning by using the performance of past member model forecasts along with the previous official NHC forecast in an effort to correct biases FSSE: The FSSE is not a simple average of the member models. Rather, the FSSE is constantly learning by using the performance of past member model forecasts along with the previous official NHC forecast in an effort to correct biases

Atlantic GUNA Ensemble TC Forecast Error (nm) Atlantic GUNA Ensemble TC Forecast Error (nm) Number of Forecasts467229

32 Excellent example of GUNA consensus: HURRICANE ISABEL, 1200 UTC 11 SEP 2003

33 Florida State Super Ensemble The limitation of such a technique occurs when the past performance of the member models does not accurately represent their present performance The limitation of such a technique occurs when the past performance of the member models does not accurately represent their present performance For example, the FSSE may have to “relearn” a particular model’s bias at the beginning of a season, after changes were made to that member modelFor example, the FSSE may have to “relearn” a particular model’s bias at the beginning of a season, after changes were made to that member model The limitation of such a technique occurs when the past performance of the member models does not accurately represent their present performance The limitation of such a technique occurs when the past performance of the member models does not accurately represent their present performance For example, the FSSE may have to “relearn” a particular model’s bias at the beginning of a season, after changes were made to that member modelFor example, the FSSE may have to “relearn” a particular model’s bias at the beginning of a season, after changes were made to that member model

Corrected Consensus Derived statistically, based on parameters known at the start of the forecast, such as model spread, initial intensity, location, etc. Can also be derived using historical biases of CONU or GUNA Typically a small correction Derived statistically, based on parameters known at the start of the forecast, such as model spread, initial intensity, location, etc. Can also be derived using historical biases of CONU or GUNA Typically a small correction CONU and CCON Forecast Tracks Hurricane Daniel – 00Z 20 July 2006 CONU and CCON Forecast Tracks Hurricane Daniel – 00Z 20 July 2006

35 Goerss Corrected Consensus CCON 120 h FSP: 36%CGUN 120 h FSP: 33% Small improvements of 1-3%, but benefit lost by 5 days.

Predicting TC Track Forecast Error Statistical method used to compute consensus TC track forecast error for each combination of forecast length, consensus model, and basin Regression models also used to determine the radii of circular areas drawn around the consensus model forecast positions within which the verifying TC position expected to be contained approximately 75% of the time Circular areas graphically displayed on the ATCF for use by the forecasters This graphical predicted consensus error product is referred to as GPCE (“gypsy”) Statistical method used to compute consensus TC track forecast error for each combination of forecast length, consensus model, and basin Regression models also used to determine the radii of circular areas drawn around the consensus model forecast positions within which the verifying TC position expected to be contained approximately 75% of the time Circular areas graphically displayed on the ATCF for use by the forecasters This graphical predicted consensus error product is referred to as GPCE (“gypsy”) 72-h CONU Confidence circle, or “gypsy (GPCE)” Emily, 12Z 13 July h Predicted Consensus Error Hurricane Rita - 06Z 22 September h Predicted Consensus Error Hurricane Rita - 06Z 22 September 2005

37 Using Extrapolation of Initial (Current) Motion Initial motion typically computed using the 6, 12, or 18 hour past motion Initial motion typically computed using the 6, 12, or 18 hour past motion Use shorter time intervals for rapidly changing motionUse shorter time intervals for rapidly changing motion Use longer time intervals for uncertain motion and/or center locationUse longer time intervals for uncertain motion and/or center location Very important for short-term forecast Very important for short-term forecast 12-hr forecast is typically based heavily on extrapolated initial motion12-hr forecast is typically based heavily on extrapolated initial motion Most difficult for systems with erratic motion and/or poorly defined centers Most difficult for systems with erratic motion and/or poorly defined centers Be wary of the “Wobble” Be wary of the “Wobble” Don’t put too much stock in short-term motion (<6 hrs) unless you are sure it is not a wobbleDon’t put too much stock in short-term motion (<6 hrs) unless you are sure it is not a wobble Initial motion typically computed using the 6, 12, or 18 hour past motion Initial motion typically computed using the 6, 12, or 18 hour past motion Use shorter time intervals for rapidly changing motionUse shorter time intervals for rapidly changing motion Use longer time intervals for uncertain motion and/or center locationUse longer time intervals for uncertain motion and/or center location Very important for short-term forecast Very important for short-term forecast 12-hr forecast is typically based heavily on extrapolated initial motion12-hr forecast is typically based heavily on extrapolated initial motion Most difficult for systems with erratic motion and/or poorly defined centers Most difficult for systems with erratic motion and/or poorly defined centers Be wary of the “Wobble” Be wary of the “Wobble” Don’t put too much stock in short-term motion (<6 hrs) unless you are sure it is not a wobbleDon’t put too much stock in short-term motion (<6 hrs) unless you are sure it is not a wobble

38 ContinuityContinuity Changes to the previous forecast are normally made in small increments Changes to the previous forecast are normally made in small increments Official forecast typically trends in a given direction (left, right, slower, faster)Official forecast typically trends in a given direction (left, right, slower, faster) Significant changes in the TC track forecast should be avoided since: Significant changes in the TC track forecast should be avoided since: Models can shift back and forth from one cycle to the nextModels can shift back and forth from one cycle to the next Credibility can be damaged by making big changesCredibility can be damaged by making big changes Can confuse the public and/or generate over/under reactionCan confuse the public and/or generate over/under reaction Occasional exceptions (Katrina)Occasional exceptions (Katrina) Changes to the previous forecast are normally made in small increments Changes to the previous forecast are normally made in small increments Official forecast typically trends in a given direction (left, right, slower, faster)Official forecast typically trends in a given direction (left, right, slower, faster) Significant changes in the TC track forecast should be avoided since: Significant changes in the TC track forecast should be avoided since: Models can shift back and forth from one cycle to the nextModels can shift back and forth from one cycle to the next Credibility can be damaged by making big changesCredibility can be damaged by making big changes Can confuse the public and/or generate over/under reactionCan confuse the public and/or generate over/under reaction Occasional exceptions (Katrina)Occasional exceptions (Katrina)

39 Model forecast Model biases Data Continuity Bringing It Altogether

40 Piecing Together a Forecast Evaluate the large-scale synoptic environment Evaluate the large-scale synoptic environment Analyze in-situ and remotely sensed data before looking at model outputAnalyze in-situ and remotely sensed data before looking at model output Assess the steering patternAssess the steering pattern Compare observations to model initial fieldsCompare observations to model initial fields Look for areas where the model fields do not match the observations “garbage in equals garbage out”Look for areas where the model fields do not match the observations “garbage in equals garbage out” Compare the “conceptual model” with the numerical model Compare the “conceptual model” with the numerical model How might variations between the model analysis and current data affect the forecastHow might variations between the model analysis and current data affect the forecast Based on the large-scale pattern, what seems most reasonable?Based on the large-scale pattern, what seems most reasonable? Interpret model tracks Interpret model tracks There is rarely a single “Model of the Day” so don’t look for itThere is rarely a single “Model of the Day” so don’t look for it Start with a consensus of high-quality dynamical modelsStart with a consensus of high-quality dynamical models Consider past performance of each member (look at model trends)Consider past performance of each member (look at model trends) When possible, try a “selected consensus” based on a thorough analysis of all guidanceWhen possible, try a “selected consensus” based on a thorough analysis of all guidance Always Honor Continuity Always Honor Continuity Avoid the “WINDSHIELD WIPER” effectAvoid the “WINDSHIELD WIPER” effect Evaluate the large-scale synoptic environment Evaluate the large-scale synoptic environment Analyze in-situ and remotely sensed data before looking at model outputAnalyze in-situ and remotely sensed data before looking at model output Assess the steering patternAssess the steering pattern Compare observations to model initial fieldsCompare observations to model initial fields Look for areas where the model fields do not match the observations “garbage in equals garbage out”Look for areas where the model fields do not match the observations “garbage in equals garbage out” Compare the “conceptual model” with the numerical model Compare the “conceptual model” with the numerical model How might variations between the model analysis and current data affect the forecastHow might variations between the model analysis and current data affect the forecast Based on the large-scale pattern, what seems most reasonable?Based on the large-scale pattern, what seems most reasonable? Interpret model tracks Interpret model tracks There is rarely a single “Model of the Day” so don’t look for itThere is rarely a single “Model of the Day” so don’t look for it Start with a consensus of high-quality dynamical modelsStart with a consensus of high-quality dynamical models Consider past performance of each member (look at model trends)Consider past performance of each member (look at model trends) When possible, try a “selected consensus” based on a thorough analysis of all guidanceWhen possible, try a “selected consensus” based on a thorough analysis of all guidance Always Honor Continuity Always Honor Continuity Avoid the “WINDSHIELD WIPER” effectAvoid the “WINDSHIELD WIPER” effect

41 BAD INITIALIZATION FOR TROPICAL STORM GORDON – 9/11/ UTC

42 GFS track forecasts for Javier Sep Javier 130 kt Ivan 140 kt Isis 45 kt Initial vortex too weak Incorrect initial structure leads to a west bias

43 Data

44 TRACK FORECAST IMPROVEMENTS IN THE NCEP GLOBAL MODEL (GFS) DUE TO GPS DROPSONDES,

45 Impact of Dropsondes on Model Forecast

46 Jung and Zapotocny JCSDA Funded by NPOESS IPO Satellite data ~ 10-15% impact Better Worse Better EPAC ATL Impact of REMOVING Satellite Data

47 Think Conceptually Ask yourself what is happening in reality? Ask yourself what is happening in reality? What is happening in the model? What is happening in the model? Is the model forecast realistic? Is the model forecast realistic? What are possible error mechanisms of a model (error mechanisms always exists)?What are possible error mechanisms of a model (error mechanisms always exists)? How might these error mechanisms affect the forecast?How might these error mechanisms affect the forecast? Ask yourself what is happening in reality? Ask yourself what is happening in reality? What is happening in the model? What is happening in the model? Is the model forecast realistic? Is the model forecast realistic? What are possible error mechanisms of a model (error mechanisms always exists)?What are possible error mechanisms of a model (error mechanisms always exists)? How might these error mechanisms affect the forecast?How might these error mechanisms affect the forecast?

48 The Effects of a TC on the Large-scale Pattern (Ross and Kurihara 1995) The Effects of a TC on the Large-scale Pattern (Ross and Kurihara 1995) Compared differences in hurricane and non-hurricane integrations of the GFDL for Gloria (1985). The hurricane modified environment influenced intensity and storm motion Hurricane influence was more extensive in the upper levels Stronger upper-level anticyclone developed closer to the storm for the hurricane simulation Hurricane No Hurricane

49 1) A model cyclone which is too intense (weak) leads to enhanced (limited) heating 2) The redistribution of upper-level heating by mean flow can significantly affect the upper-level pattern 3) Modified upper- level pattern changes steering pattern and/or shear pattern which can indirectly affect steering The Power of Latent Heating Understand the convective parameterization scheme

50 Up shear Down shear Diabatic Heating

51 Interpreting the Tracks Understand the tracker Understand the tracker Don’t assume the tracker is tracking the correct featureDon’t assume the tracker is tracking the correct feature Compare tracker with the model fields (vorticity)Compare tracker with the model fields (vorticity) Understand the tracker Understand the tracker Don’t assume the tracker is tracking the correct featureDon’t assume the tracker is tracking the correct feature Compare tracker with the model fields (vorticity)Compare tracker with the model fields (vorticity)

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59 Interpreting the Tracks Understand the tracker Understand the tracker Don’t assume the tracker is tracking the correct featureDon’t assume the tracker is tracking the correct feature Compare tracker with the model fields (vorticity)Compare tracker with the model fields (vorticity) How has each model’s track changed as compared to previous cycles? How has each model’s track changed as compared to previous cycles? What is the recent trend?What is the recent trend? What is the reason for the change and is it believable?What is the reason for the change and is it believable? Understand the tracker Understand the tracker Don’t assume the tracker is tracking the correct featureDon’t assume the tracker is tracking the correct feature Compare tracker with the model fields (vorticity)Compare tracker with the model fields (vorticity) How has each model’s track changed as compared to previous cycles? How has each model’s track changed as compared to previous cycles? What is the recent trend?What is the recent trend? What is the reason for the change and is it believable?What is the reason for the change and is it believable?

60 GFS TRACK FORECASTS FOR IVAN FROM 9/7/04 12Z – 9/11/04 12Z

61 GFS TRACK FORECASTS FOR IVAN FROM 9/13/04 12Z – 9/15/04 12Z

62 Interpreting the Tracks Understand the tracker Understand the tracker Don’t assume the tracker is tracking the correct featureDon’t assume the tracker is tracking the correct feature Compare tracker with the model fields (vorticity)Compare tracker with the model fields (vorticity) How has each model’s track changed as compared to previous cycles? How has each model’s track changed as compared to previous cycles? What is the recent trend?What is the recent trend? What is the reason for the change and is it believable?What is the reason for the change and is it believable? How well is the cyclone represented in each model? How well is the cyclone represented in each model? Is the model storm properly located?Is the model storm properly located? Does the model storm have proper structure?Does the model storm have proper structure? Is forecast cyclone realistic?Is forecast cyclone realistic? Understand the tracker Understand the tracker Don’t assume the tracker is tracking the correct featureDon’t assume the tracker is tracking the correct feature Compare tracker with the model fields (vorticity)Compare tracker with the model fields (vorticity) How has each model’s track changed as compared to previous cycles? How has each model’s track changed as compared to previous cycles? What is the recent trend?What is the recent trend? What is the reason for the change and is it believable?What is the reason for the change and is it believable? How well is the cyclone represented in each model? How well is the cyclone represented in each model? Is the model storm properly located?Is the model storm properly located? Does the model storm have proper structure?Does the model storm have proper structure? Is forecast cyclone realistic?Is forecast cyclone realistic?

63 CROSS SECTION OF GFDL MODEL RUN FOR HURRICANE PAUL 10/23/ UTC (Hour 00)

64 HOUR 12 CROSS SECTION OF GFDL MODEL RUN FOR HURRICANE PAUL 10/23/ UTC

65 HOUR 24 CROSS SECTION OF GFDL MODEL RUN FOR HURRICANE PAUL 10/23/ UTC

66 HOUR 36 CROSS SECTION OF GFDL MODEL RUN FOR HURRICANE PAUL 10/23/ UTC

67 HOUR 48 CROSS SECTION OF GFDL MODEL RUN FOR HURRICANE PAUL 10/23/ UTC

68 Interpreting the Tracks Understand the tracker Understand the tracker Don’t assume the tracker is tracking the correct featureDon’t assume the tracker is tracking the correct feature Compare tracker with the model fields (vorticity)Compare tracker with the model fields (vorticity) How has each model’s track changed as compared to previous cycles? How has each model’s track changed as compared to previous cycles? What is the recent trend?What is the recent trend? What is the reason for the change and is it believable?What is the reason for the change and is it believable? How well is the cyclone represented in each model? How well is the cyclone represented in each model? Is the model storm properly located?Is the model storm properly located? Does the model storm have proper structure?Does the model storm have proper structure? Is forecast cyclone realistic?Is forecast cyclone realistic? How much divergence or spread exists in the model tracks? How much divergence or spread exists in the model tracks? Spread is often indicative of complex steering and ultimately uncertaintySpread is often indicative of complex steering and ultimately uncertainty Why is there spread? What are the differences in the model steering? Is it due to a single feature?Why is there spread? What are the differences in the model steering? Is it due to a single feature? Understand the tracker Understand the tracker Don’t assume the tracker is tracking the correct featureDon’t assume the tracker is tracking the correct feature Compare tracker with the model fields (vorticity)Compare tracker with the model fields (vorticity) How has each model’s track changed as compared to previous cycles? How has each model’s track changed as compared to previous cycles? What is the recent trend?What is the recent trend? What is the reason for the change and is it believable?What is the reason for the change and is it believable? How well is the cyclone represented in each model? How well is the cyclone represented in each model? Is the model storm properly located?Is the model storm properly located? Does the model storm have proper structure?Does the model storm have proper structure? Is forecast cyclone realistic?Is forecast cyclone realistic? How much divergence or spread exists in the model tracks? How much divergence or spread exists in the model tracks? Spread is often indicative of complex steering and ultimately uncertaintySpread is often indicative of complex steering and ultimately uncertainty Why is there spread? What are the differences in the model steering? Is it due to a single feature?Why is there spread? What are the differences in the model steering? Is it due to a single feature?

69 How to resolve the difference between guidance models?

70 Not-so-excellent example of GUNA consensus: HURRICANE KATE, 1800 UTC 29 SEP 2003 This is a case where forming a selective consensus can be effective. Not-so-excellent example of GUNA consensus: HURRICANE KATE, 1800 UTC 29 SEP 2003 This is a case where forming a selective consensus can be effective.

71 Model Performance No single model stays on top long No single model stays on top long Performance can vary from year to yearPerformance can vary from year to year Consensus models typically outperform individual models Consensus models typically outperform individual models

72 CONCLUDING REMARKS (TRACK FORECASTING) ● Multi-level dynamical models are the most skillful models for TC track prediction, although simple trajectory models, such as BAMD, can still be useful. Consensus track forecasts such as the GUNA and CONU generally produce more skillful forecasts than any individual model. A selective consensus generated by intelligently evaluating each model can be effective but should be used carefully. ● The HWRF model is the next generation high resolution hurricane model which will transition into operations this year. GFDL and HWRF will be run operationally in parallel this year. ● Multi-level dynamical models are the most skillful models for TC track prediction, although simple trajectory models, such as BAMD, can still be useful. Consensus track forecasts such as the GUNA and CONU generally produce more skillful forecasts than any individual model. A selective consensus generated by intelligently evaluating each model can be effective but should be used carefully. ● The HWRF model is the next generation high resolution hurricane model which will transition into operations this year. GFDL and HWRF will be run operationally in parallel this year.

73 TC Track Exercise Forecast Scenario Forecast Scenario Strong hurricane (90 kt) off the southern coast of MexicoStrong hurricane (90 kt) off the southern coast of Mexico Significant divergence in track guidanceSignificant divergence in track guidance Possible land interactionPossible land interaction Possible binary (Fujiwara) interactionPossible binary (Fujiwara) interaction Forecast Scenario Forecast Scenario Strong hurricane (90 kt) off the southern coast of MexicoStrong hurricane (90 kt) off the southern coast of Mexico Significant divergence in track guidanceSignificant divergence in track guidance Possible land interactionPossible land interaction Possible binary (Fujiwara) interactionPossible binary (Fujiwara) interaction

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75 Extra Slides

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77 Extra Slides

78 Up shear Down shear Diabatic Heating

79 Rita Track Forecasts 1200 UTC 21 September Severe left bias in track models

80 Rita Track Forecasts 1200 UTC 22 September Remarkable improvement in track guidance: Likely the impact of surveillance data from the NOAA G-IV jet?

81 EXCELLENT EXAMPLE OF GUNS & GUNA CONSENSUS -- HURRICANE ISABEL, 1200 UTC 11 SEP 2003 FORECAST VERIFYING POSITION MODEL CONSENSUS GUNA: GFDL UKMET NOGAPS GFS (AVN) GUNS: GFDL UKMET NOGAPS MODEL CONSENSUS GUNA: GFDL UKMET NOGAPS GFS (AVN) GUNS: GFDL UKMET NOGAPS

82 GOOD EXAMPLE OF THE GUNA CONSENSUS: GFDL AND GFS (AVN) TO THE RIGHT OF TRACK WITH U.K. MET AND NOGAPS TO THE LEFT OF TRACK. ERRORS MOSTLY CANCEL, RESULTING IN A NEARLY PERFECT TRACK FORECAST (72 H ERROR OF 17 N MI). 72 H FORECAST FOR HURRICANE LILI FROM 9/29/02 18Z

83 NOT SO GOOD EXAMPLE OF GUNA: GFS (AVN) ALMOST RIGHT ON TRACK, BUT ALL OTHERS BIASED TO THE NORTHEAST. 72 H FORECAST FOR HURRICANE ISIDORE FROM 9/20/02 00Z

84 NOT-SO EXCELLENT EXAMPLE OF GUNA CONSENSUS: HURRICANE KATE, 1200 UTC 11 SEP 2003 (OFFICIAL 5-DAY FORECAST WAS NEAR 34N 42W) VERIFYING POSITION

85 TRACK GUIDANCE OUT TO 5 DAYS – TROPICAL STORM ERNESTO, 8/26/ UTC OBSERVED 5-DAY POSITION OBSERVED 3-DAY POSITION

vs. 5-Year Mean

87 Models diverge, official forecast slow, consensus very accurate

88 Successive GFS 7 day forecasts Valid 23 Sep. 18zValid 24 Sep. 00z Valid 24 Sep. 06z Valid 24 Sep. 12z

89 Errors cut in half since 1990

90 GFS TRACK FORECASTS FOR IVAN FROM 9/13/04 12Z – 9/15/04 12Z WERE EXCELLENT IN SPECIFYING IVAN’S LANDFALL LOCATION ON GULF COAST.