1 Jamie Rhome WMO Workshop National Hurricane Center Jamie Rhome WMO Workshop National Hurricane Center NATIONAL CENTERS FOR ENVIRONMENTAL PREDICTION WHERE

11

Jamie RhomeJamie RhomeWMO WorkshopWMO Workshop

National Hurricane CenterNational Hurricane Center

Jamie RhomeJamie RhomeWMO WorkshopWMO Workshop

National Hurricane CenterNational Hurricane Center

NATIONAL CENTERS FOR ENVIRONMENTAL PREDICTIONNATIONAL CENTERS FOR ENVIRONMENTAL PREDICTION

WHERE AMERICA’S CLIMATE AND WEATHER SERVICES BEGINWHERE AMERICA’S CLIMATE AND WEATHER SERVICES BEGIN

Tropical Cyclone Track Tropical Cyclone Track ForecastingForecasting

Tropical Cyclone Track Tropical Cyclone Track ForecastingForecasting

22

OUTLINE: TRACK FORECASTINGOUTLINE: TRACK FORECASTINGOUTLINE: TRACK FORECASTINGOUTLINE: TRACK FORECASTING

Factors affecting TC motionFactors affecting TC motion Guidance Models Guidance Models

• Climatology and Statistical Climatology and Statistical models models

• Beta and Advection ModelsBeta and Advection Models• Dynamical ModelsDynamical Models• Ensembles and ConsensusEnsembles and Consensus

NHC Forecasting GuidelinesNHC Forecasting Guidelines• Using Initial Motion Using Initial Motion • ContinuityContinuity

Bringing it All Together: Interpreting Bringing it All Together: Interpreting Track ModelsTrack Models

ExerciseExercise

Factors affecting TC motionFactors affecting TC motion Guidance Models Guidance Models

• Climatology and Statistical Climatology and Statistical models models

• Beta and Advection ModelsBeta and Advection Models• Dynamical ModelsDynamical Models• Ensembles and ConsensusEnsembles and Consensus

NHC Forecasting GuidelinesNHC Forecasting Guidelines• Using Initial Motion Using Initial Motion • ContinuityContinuity

Bringing it All Together: Interpreting Bringing it All Together: Interpreting Track ModelsTrack Models

ExerciseExercise

33

Factors Affecting TC Motion:Factors Affecting TC Motion:Factors Affecting TC Motion:Factors Affecting TC Motion:

Large-scaleLarge-scale • Vortex Moves with “Steering Vortex Moves with “Steering

Flow” Flow” main contributor to TC main contributor to TC motionmotion

Cyclone-scaleCyclone-scale • Vortex induces beta-gyres and Vortex induces beta-gyres and

other asymmetries that affect other asymmetries that affect motionmotion

• Convective distributionConvective distribution• Vertical StructureVertical Structure

OtherOther• Binary interaction (Fujiwhara Binary interaction (Fujiwhara

effect)effect)• Landmass interactionLandmass interaction• Internal dynamics (trochoidal Internal dynamics (trochoidal

motion)motion)

Large-scaleLarge-scale • Vortex Moves with “Steering Vortex Moves with “Steering

Flow” Flow” main contributor to TC main contributor to TC motionmotion

Cyclone-scaleCyclone-scale • Vortex induces beta-gyres and Vortex induces beta-gyres and

other asymmetries that affect other asymmetries that affect motionmotion

• Convective distributionConvective distribution• Vertical StructureVertical Structure

OtherOther• Binary interaction (Fujiwhara Binary interaction (Fujiwhara

effect)effect)• Landmass interactionLandmass interaction• Internal dynamics (trochoidal Internal dynamics (trochoidal

motion)motion)

44

The Large-scale Steering Flow is the Main Main Contributor to TC MotionContributor to TC Motion

The Large-scale Steering Flow is the Main Main Contributor to TC MotionContributor to TC Motion

HL L

55

66LOWER VALUES OF EARTH’S VORTICITY

The Beta EffectThe Beta Effect

βv>0

βv<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.

• 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.

• 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

77

Binary Interaction (Fujiwhara Effect)Binary Interaction (Fujiwhara Effect)

Fujiwhara effectFujiwhara effect—Occurs when two —Occurs when two tropical cyclones become close enough tropical cyclones become close enough (< 1450 km) to rotate cyclonically (< 1450 km) to rotate cyclonically about each other as a result of their about each other as a result of their circulations' mutual advection. circulations' mutual advection.

Named after Dr. Sakuhei Fujiwhara Named after Dr. Sakuhei Fujiwhara who initially described it in a 1921 who initially described it in a 1921 paper about the motion of vortices in paper about the motion of vortices in waterwater

Most often occurs in the northwestern Most often occurs in the northwestern and eastern North Pacific basin…less and eastern North Pacific basin…less often in the Atlantic.often in the Atlantic.

Presents a unique forecast challenge Presents a unique forecast challenge since the complex interplay results in since the complex interplay results in different scenarios which determine different scenarios which determine the final result of the interactionthe final result of the interaction

Some of the factors affecting the Some of the factors affecting the outcome of binary interaction include: outcome of binary interaction include: comparable strength of the two comparable strength of the two cyclones, comparable size of the two cyclones, comparable size of the two cyclones, distance apart, background cyclones, distance apart, background flowflow

Fujiwhara effectFujiwhara effect—Occurs when two —Occurs when two tropical cyclones become close enough tropical cyclones become close enough (< 1450 km) to rotate cyclonically (< 1450 km) to rotate cyclonically about each other as a result of their about each other as a result of their circulations' mutual advection. circulations' mutual advection.

Named after Dr. Sakuhei Fujiwhara Named after Dr. Sakuhei Fujiwhara who initially described it in a 1921 who initially described it in a 1921 paper about the motion of vortices in paper about the motion of vortices in waterwater

Most often occurs in the northwestern Most often occurs in the northwestern and eastern North Pacific basin…less and eastern North Pacific basin…less often in the Atlantic.often in the Atlantic.

Presents a unique forecast challenge Presents a unique forecast challenge since the complex interplay results in since the complex interplay results in different scenarios which determine different scenarios which determine the final result of the interactionthe final result of the interaction

Some of the factors affecting the Some of the factors affecting the outcome of binary interaction include: outcome of binary interaction include: comparable strength of the two comparable strength of the two cyclones, comparable size of the two cyclones, comparable size of the two cyclones, distance apart, background cyclones, distance apart, background flowflow

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)

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)

88

Trochoidal Motion (Wobble)Trochoidal Motion (Wobble)

Related to inner-core Related to inner-core structure, convective structure, convective asymmetries, and dynamic asymmetries, and dynamic instabilityinstability

Unable to forecastUnable to forecast• Simply observeSimply observe• Beware of the “wobble”Beware of the “wobble”• Wait for a sustained Wait for a sustained

(several hours) change in (several hours) change in motionmotion

Related to inner-core Related to inner-core structure, convective structure, convective asymmetries, and dynamic asymmetries, and dynamic instabilityinstability

Unable to forecastUnable to forecast• Simply observeSimply observe• Beware of the “wobble”Beware of the “wobble”• Wait for a sustained Wait for a sustained

(several hours) change in (several hours) change in motionmotion

99

Hierarchy of TC Track Guidance Hierarchy of TC Track Guidance Models:Models:

Hierarchy of TC Track Guidance Hierarchy of TC Track Guidance Models:Models:

StatisticalStatistical • Forecasts based on established relationships between storm-specific Forecasts based on established relationships between storm-specific

information (i.e., location and time of year) and the behavior of previous information (i.e., location and time of year) and the behavior of previous stormsstorms

• CLIPERCLIPER

Statistical-DynamicalStatistical-Dynamical • Statistical models that use information from dynamical model outputStatistical models that use information from dynamical model output• NHC91NHC91 still maintains skill in the eastern Pacific still maintains skill in the eastern Pacific

Simplified Dynamical Simplified Dynamical • LBAR LBAR simple two-dimensional dynamical track prediction model that solves simple two-dimensional dynamical track prediction model that solves

the shallow-water equations initialized with vertically averaged (850-200 the shallow-water equations initialized with vertically averaged (850-200 hPa) winds and heights from the GFS global model hPa) winds and heights from the GFS global model

• BAMD, BAMM, BAMSBAMD, BAMM, BAMS -> Forecasts based on simplified dynamic -> Forecasts based on simplified dynamic representation of interaction with vortex and prevailing flow (trajectory)representation of interaction with vortex and prevailing flow (trajectory)

Dynamical Models Dynamical Models • Solve the physical equations of motion that govern the atmosphere Solve 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)

StatisticalStatistical • Forecasts based on established relationships between storm-specific Forecasts based on established relationships between storm-specific

information (i.e., location and time of year) and the behavior of previous information (i.e., location and time of year) and the behavior of previous stormsstorms

• CLIPERCLIPER

Statistical-DynamicalStatistical-Dynamical • Statistical models that use information from dynamical model outputStatistical models that use information from dynamical model output• NHC91NHC91 still maintains skill in the eastern Pacific still maintains skill in the eastern Pacific

Simplified Dynamical Simplified Dynamical • LBAR LBAR simple two-dimensional dynamical track prediction model that solves simple two-dimensional dynamical track prediction model that solves

the shallow-water equations initialized with vertically averaged (850-200 the shallow-water equations initialized with vertically averaged (850-200 hPa) winds and heights from the GFS global model hPa) winds and heights from the GFS global model

• BAMD, BAMM, BAMSBAMD, BAMM, BAMS -> Forecasts based on simplified dynamic -> Forecasts based on simplified dynamic representation of interaction with vortex and prevailing flow (trajectory)representation of interaction with vortex and prevailing flow (trajectory)

Dynamical Models Dynamical Models • Solve the physical equations of motion that govern the atmosphere Solve 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)

1010

CLIPER (CLImatology and CLIPER (CLImatology and PERsistence) ModelPERsistence) Model

CLIPER (CLImatology and CLIPER (CLImatology and PERsistence) ModelPERsistence) Model

Statistical track model developed in 1972, Statistical track model developed in 1972, extended to 120 h in 1998extended 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, Average 24, 48, 72, 96 and 120 h errors: 100, 216, 318, 419, and 510 nautical miles 216, 318, 419, and 510 nautical miles respectivelyrespectively

Used as a Used as a benchmarkbenchmark for other models and for other models and subjective forecasts; forecasts with errors greater subjective forecasts; forecasts with errors greater than CLIPER are considered to have than CLIPER are considered to have no skillno skill. .

Statistical track model developed in 1972, Statistical track model developed in 1972, extended to 120 h in 1998extended 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, Average 24, 48, 72, 96 and 120 h errors: 100, 216, 318, 419, and 510 nautical miles 216, 318, 419, and 510 nautical miles respectivelyrespectively

Used as a Used as a benchmarkbenchmark for other models and for other models and subjective forecasts; forecasts with errors greater subjective forecasts; forecasts with errors greater than CLIPER are considered to have than CLIPER are considered to have no skillno skill. .

1111

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 (850-700 MB) - BAMS

• Medium (850-400 MB) - BAMM

• Deep (850-200 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 (850-700 MB) - BAMS

• Medium (850-400 MB) - BAMM

• Deep (850-200 MB) - BAMD

1212

200 mb200 mb

Typical cruising altitude of commercial airplaneTypical cruising altitude of commercial airplane

SurfaceSurface

700 mb700 mb

400 mb400 mb

WHICH BAM TO USE? WHICH BAM TO USE?

LL

SHALLOW SHALLOW

TROPICAL DEPRESSION

TROPICAL DEPRESSION

LL

MEDIUMMEDIUM

TROPICAL STORM / CAT. 1-2 HURRICANETROPICAL STORM /

CAT. 1-2 HURRICANE

LL

DEEPDEEP

MAJOR HURRICANE

MAJOR HURRICANE

1313

LBAR (Limited-area BARotropic)LBAR (Limited-area BARotropic)

Barotropic dynamics, i.e. 2-d motions – no Barotropic dynamics, i.e. 2-d motions – no temperature gradients or vertical sheartemperature gradients or vertical shear• Lack of baroclinic forcing means the model has little or Lack of baroclinic forcing means the model has little or

no skill beyond 1-2 daysno skill beyond 1-2 days Shallow water equations on Mercator projection Shallow water equations on Mercator projection

solved using sine transformssolved using sine transforms Initialized with 850-200 mb layer average Initialized with 850-200 mb layer average

winds/heights from NCEP global model (GFS)winds/heights from NCEP global model (GFS) Sum of idealized vortex and current motion Sum of idealized vortex and current motion

vector added to large-scale analysisvector added to large-scale analysis Boundary conditions from global modelBoundary conditions from global model

Barotropic dynamics, i.e. 2-d motions – no Barotropic dynamics, i.e. 2-d motions – no temperature gradients or vertical sheartemperature gradients or vertical shear• Lack of baroclinic forcing means the model has little or Lack of baroclinic forcing means the model has little or

no skill beyond 1-2 daysno skill beyond 1-2 days Shallow water equations on Mercator projection Shallow water equations on Mercator projection

solved using sine transformssolved using sine transforms Initialized with 850-200 mb layer average Initialized with 850-200 mb layer average

winds/heights from NCEP global model (GFS)winds/heights from NCEP global model (GFS) Sum of idealized vortex and current motion Sum of idealized vortex and current motion

vector added to large-scale analysisvector added to large-scale analysis Boundary conditions from global modelBoundary conditions from global model

1414

• U.S. NWS Global Forecast System (GFS) < relocates the relocates the first-guess TC vortexfirst-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)

• U.S. NWS Global Forecast System (GFS) < relocates the relocates the first-guess TC vortexfirst-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

Primary Dynamical Models used at NHC

1515

BogussingBogussingBogussingBogussing Since the globally analyzed vortex does Since the globally analyzed vortex does

not typically represent the structure of a not typically represent the structure of a true TC, “Bogussing” is often true TC, “Bogussing” is often employed.employed.

Bogussing involves an analysis of synthetic Bogussing involves an analysis of synthetic data to describe the TC vortex.data to describe the TC vortex.

• Bogussing can significantly affect Bogussing can significantly affect the surrounding environment the surrounding environment • vertical shearvertical shear

Creating and inserting a bogus is not straight forwardCreating and inserting a bogus is not straight forward• Forecast can be very sensitive to small changes in the Forecast can be very sensitive to small changes in the

bogus stormbogus 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 Bogus retains warm core too long leading to poor

intensity and structure forecastsintensity and structure forecasts

Since the globally analyzed vortex does Since the globally analyzed vortex does not typically represent the structure of a not typically represent the structure of a true TC, “Bogussing” is often true TC, “Bogussing” is often employed.employed.

Bogussing involves an analysis of synthetic Bogussing involves an analysis of synthetic data to describe the TC vortex.data to describe the TC vortex.

• Bogussing can significantly affect Bogussing can significantly affect the surrounding environment the surrounding environment • vertical shearvertical shear

Creating and inserting a bogus is not straight forwardCreating and inserting a bogus is not straight forward• Forecast can be very sensitive to small changes in the Forecast can be very sensitive to small changes in the

bogus stormbogus 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 Bogus retains warm core too long leading to poor

intensity and structure forecastsintensity and structure forecasts

1616

The NCEP Global Forecast System (GFS)

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

AVN versus GFDL Initial Shear

0

5

10

15

20

25

30

21/0600 21/1800 22/0600 22/1800 23/0600 23/1800

Verti

cal S

hear

(kts

)

GFDL Initial (F00) Vertical Shear AVN Analysis Vertical Shear

1717

• Non-hydrostatic global modelNon-hydrostatic global model

• 4-D VAR analysis scheme with bogus TC4-D VAR analysis scheme with bogus TC

• Arakawa C-grid: east-west horizontal grid Arakawa C-grid: east-west horizontal grid spacing of 0.5° longitude and a north-south grid spacing of 0.5° longitude and a north-south grid spacing of 0.4° latitudespacing 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, Intermediate runs at 0600Z and 1800Z, but only produce forecasts to 48 hoursbut only produce forecasts to 48 hours

• Non-hydrostatic global modelNon-hydrostatic global model

• 4-D VAR analysis scheme with bogus TC4-D VAR analysis scheme with bogus TC

• Arakawa C-grid: east-west horizontal grid Arakawa C-grid: east-west horizontal grid spacing of 0.5° longitude and a north-south grid spacing of 0.5° longitude and a north-south grid spacing of 0.4° latitudespacing 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, Intermediate runs at 0600Z and 1800Z, but only produce forecasts to 48 hoursbut only produce forecasts to 48 hours

The U.K. Met. Office ModelThe U.K. Met. Office Model

1818

• Global spectral model Global spectral model • T382L64 (~ 35-km horizontal grid spacing with T382L64 (~ 35-km horizontal grid spacing with 64 vertical levels) through 180 hours. 64 vertical levels) through 180 hours. • T190L64 (~ 80-km grid spacing and 64 levels) T190L64 (~ 80-km grid spacing and 64 levels) 180-384 hours180-384 hours• Hybrid sigma-pressure vertical coordinate Hybrid sigma-pressure vertical coordinate system (May 2007)system (May 2007) • Simplified Arakawa-Schubert (SAS) convective Simplified Arakawa-Schubert (SAS) convective parameterization schemeparameterization scheme• PBL: First-order closure method PBL: First-order closure method • 3D-Var Gridpoint Statistical Interpolation 3D-Var Gridpoint Statistical Interpolation (GSI) (May 2007)(GSI) (May 2007)• Rather than bogussing, the GFS relocates the Rather than bogussing, the GFS relocates the first-guess TC vortex to the official NHC position.first-guess TC vortex to the official NHC position.

• Often leads to an incomplete representation Often leads to an incomplete representation of the true TC structureof the true TC structure

• Run four times per day (00, 06, 12, and 18 UTC) Run four times per day (00, 06, 12, and 18 UTC) out to 384 hoursout to 384 hours

• Global spectral model Global spectral model • T382L64 (~ 35-km horizontal grid spacing with T382L64 (~ 35-km horizontal grid spacing with 64 vertical levels) through 180 hours. 64 vertical levels) through 180 hours. • T190L64 (~ 80-km grid spacing and 64 levels) T190L64 (~ 80-km grid spacing and 64 levels) 180-384 hours180-384 hours• Hybrid sigma-pressure vertical coordinate Hybrid sigma-pressure vertical coordinate system (May 2007)system (May 2007) • Simplified Arakawa-Schubert (SAS) convective Simplified Arakawa-Schubert (SAS) convective parameterization schemeparameterization scheme• PBL: First-order closure method PBL: First-order closure method • 3D-Var Gridpoint Statistical Interpolation 3D-Var Gridpoint Statistical Interpolation (GSI) (May 2007)(GSI) (May 2007)• Rather than bogussing, the GFS relocates the Rather than bogussing, the GFS relocates the first-guess TC vortex to the official NHC position.first-guess TC vortex to the official NHC position.

• Often leads to an incomplete representation Often leads to an incomplete representation of the true TC structureof the true TC structure

• Run four times per day (00, 06, 12, and 18 UTC) Run four times per day (00, 06, 12, and 18 UTC) out to 384 hoursout to 384 hours

The Global Forecast System The Global Forecast System (GFS)(GFS)

1919

NOGAPS ModelNOGAPS 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.

2020

ECMWF ModelECMWF 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.

2121

THE GEOPHYSICAL FLUID DYNAMICS LABORATORY (GFDL) HURRICANE MODEL:

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

2222

2323

THE HURRICANE WEATHER RESEARCH & FORECASTING (HWRF) PREDICTION SYSTEM 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

2424

HWRF GFDLHWRF GFDL HWRF GFDLHWRF GFDL Grid configurationGrid configuration 2-nests (coincident)2-nests (coincident) 3-nests(not 3-nests(not

coincident)coincident)

NestingNesting Force-feedbackForce-feedback Interaction thru intra-Interaction thru intra-nest fluxesnest fluxes

Convective Convective parameterizationparameterization

SAS mom.mix.SAS mom.mix. SAS mom.mix.SAS mom.mix.

Explicit Explicit condensationcondensation

FerrierFerrier FerrierFerrier

Boundary layerBoundary layer GFS non-localGFS non-local GFS non-localGFS non-local

Surface layerSurface layer GFDL ..(Moon et. al.)GFDL ..(Moon et. al.) GFDL ..(Moon et. al.)GFDL ..(Moon et. al.)

Land surface modelLand surface model GFDL slabGFDL slab GFDL slabGFDL slab

Dissipative heatingDissipative heating Based on D-L ZhangBased on D-L Zhang Based on M-Y tke 2.5Based on M-Y tke 2.5

RadiationRadiation GFDL (cloud GFDL (cloud differences)differences)

GFDLGFDL

2525

HWRF HWRF

Hurricane WilmaHurricane Wilma

2626

• 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”“LATE” VS. “EARLY”

2727

Ensemble Forecasts(Classic Method)

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

2828GFS ENSEMBLE FOR RITA – 9/19/05 12Z

2929

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

3030



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.

FSSE: The FSSE is not a simple average of the member models. The FSSE is not a simple average of the member models. Rather, the FSSE is constantly learning by using the performance of Rather, the FSSE is constantly learning by using the performance of past member model forecasts along with the previous official NHC past member model forecasts along with the previous official NHC forecast in an effort to correct biasesforecast 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.

FSSE: The FSSE is not a simple average of the member models. The FSSE is not a simple average of the member models. Rather, the FSSE is constantly learning by using the performance of Rather, the FSSE is constantly learning by using the performance of past member model forecasts along with the previous official NHC past member model forecasts along with the previous official NHC forecast in an effort to correct biasesforecast in an effort to correct biases

3131

0

50

100

150

200

250

300

350

400

24 48 72 96 120

GFDIAVNIUKMINGPIGUNA

2001-2003 Atlantic GUNA Ensemble2001-2003 Atlantic GUNA EnsembleTC Forecast Error (nm)TC Forecast Error (nm)

2001-2003 Atlantic GUNA Ensemble2001-2003 Atlantic GUNA EnsembleTC Forecast Error (nm)TC Forecast Error (nm)

619 358 176 Number of Forecasts467 229

3232

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

3333

Florida State Super EnsembleFlorida State Super EnsembleFlorida State Super EnsembleFlorida State Super Ensemble

The limitation of such a The limitation of such a technique occurs when technique occurs when the past performance of the past performance of the member models does the member models does not accurately represent not accurately represent their present performancetheir present performance

• For example, the FSSE For example, the FSSE may have to “relearn” may have to “relearn” a particular model’s a particular model’s bias at the beginning of bias at the beginning of a season, after a season, after changes were made to changes were made to that member modelthat member model

The limitation of such a The limitation of such a technique occurs when technique occurs when the past performance of the past performance of the member models does the member models does not accurately represent not accurately represent their present performancetheir present performance

• For example, the FSSE For example, the FSSE may have to “relearn” may have to “relearn” a particular model’s a particular model’s bias at the beginning of bias at the beginning of a season, after a season, after changes were made to changes were made to that member modelthat member model

Corrected ConsensusCorrected 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 TracksHurricane Daniel – 00Z 20 July 2006CONU and CCON Forecast Tracks

Hurricane Daniel – 00Z 20 July 2006

3535

Goerss Corrected ConsensusGoerss 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 ErrorPredicting 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 72-h CONU Confidence circle, or “gypsy (GPCE)” Emily, 12Z 13 July 2005(GPCE)” Emily, 12Z 13 July 2005

48-h Predicted Consensus Error Hurricane Rita - 06Z 22 September 2005

48-h Predicted Consensus Error Hurricane Rita - 06Z 22 September 2005

3737

Using Extrapolation of Initial Using Extrapolation of Initial (Current) Motion(Current) Motion

Using Extrapolation of Initial Using Extrapolation of Initial (Current) Motion(Current) Motion

Initial motion typically computed using the 6, 12, or 18 hour Initial motion typically computed using the 6, 12, or 18 hour past motionpast 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 Use longer time intervals for uncertain motion and/or center

locationlocation Very important for short-term forecastVery important for short-term forecast

• 12-hr forecast is typically based heavily on extrapolated 12-hr forecast is typically based heavily on extrapolated initial motioninitial motion

Most difficult for systems with erratic motion and/or poorly Most difficult for systems with erratic motion and/or poorly defined centersdefined centers

Be wary of the “Wobble”Be wary of the “Wobble”• Don’t put too much stock in short-term motion (<6 hrs) unless Don’t put too much stock in short-term motion (<6 hrs) unless

you are sure it is not a wobbleyou are sure it is not a wobble

Initial motion typically computed using the 6, 12, or 18 hour Initial motion typically computed using the 6, 12, or 18 hour past motionpast 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 Use longer time intervals for uncertain motion and/or center

locationlocation Very important for short-term forecastVery important for short-term forecast

• 12-hr forecast is typically based heavily on extrapolated 12-hr forecast is typically based heavily on extrapolated initial motioninitial motion

Most difficult for systems with erratic motion and/or poorly Most difficult for systems with erratic motion and/or poorly defined centersdefined centers

Be wary of the “Wobble”Be wary of the “Wobble”• Don’t put too much stock in short-term motion (<6 hrs) unless Don’t put too much stock in short-term motion (<6 hrs) unless

you are sure it is not a wobbleyou are sure it is not a wobble

3838

ContinuityContinuityContinuityContinuity

Changes to the previous forecast are normally Changes to the previous forecast are normally made in small incrementsmade in small increments • Official forecast typically trends in a given direction Official forecast typically trends in a given direction

(left, right, slower, faster)(left, right, slower, faster) Significant changes in the TC track forecast Significant changes in the TC track forecast

should be avoided since:should be avoided since:• Models can shift back and forth from one cycle to the Models can shift back and forth from one cycle to the

nextnext• Credibility can be damaged by making big changesCredibility can be damaged by making big changes• Can confuse the public and/or generate over/under Can confuse the public and/or generate over/under

reactionreaction• Occasional exceptions (Katrina)Occasional exceptions (Katrina)

Changes to the previous forecast are normally Changes to the previous forecast are normally made in small incrementsmade in small increments • Official forecast typically trends in a given direction Official forecast typically trends in a given direction

(left, right, slower, faster)(left, right, slower, faster) Significant changes in the TC track forecast Significant changes in the TC track forecast

should be avoided since:should be avoided since:• Models can shift back and forth from one cycle to the Models can shift back and forth from one cycle to the

nextnext• Credibility can be damaged by making big changesCredibility can be damaged by making big changes• Can confuse the public and/or generate over/under Can confuse the public and/or generate over/under

reactionreaction• Occasional exceptions (Katrina)Occasional exceptions (Katrina)

3939

Model forecast

Model biasesData

Continuity

Bringing It Altogether Bringing It Altogether

4040

Piecing Together a ForecastPiecing Together a ForecastPiecing Together a ForecastPiecing Together a Forecast Evaluate the large-scale synoptic environment Evaluate the large-scale synoptic environment

• Analyze in-situ and remotely sensed data Analyze in-situ and remotely sensed data beforebefore looking at model looking at model outputoutput

• Assess the steering pattern Assess the steering pattern • Compare observations to model initial fields Compare observations to model initial fields • Look for areas where the model fields do not match the observations Look for areas where the model fields do not match the observations

“garbage in equals garbage out”“garbage in equals garbage out”

Compare the “conceptual model” with the numerical modelCompare the “conceptual model” with the numerical model• How might variations between the model analysis and current data How might variations between the model analysis and current data

affect the forecastaffect 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 When possible, try a “selected consensus” based on a thorough

analysis of all guidance 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 Analyze in-situ and remotely sensed data beforebefore looking at model looking at model

outputoutput• Assess the steering pattern Assess the steering pattern • Compare observations to model initial fields Compare observations to model initial fields • Look for areas where the model fields do not match the observations Look for areas where the model fields do not match the observations

“garbage in equals garbage out”“garbage in equals garbage out”

Compare the “conceptual model” with the numerical modelCompare the “conceptual model” with the numerical model• How might variations between the model analysis and current data How might variations between the model analysis and current data

affect the forecastaffect 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 When possible, try a “selected consensus” based on a thorough

analysis of all guidance analysis of all guidance

Always Honor Continuity Always Honor Continuity • Avoid the “WINDSHIELD WIPER” effectAvoid the “WINDSHIELD WIPER” effect

4141

BAD INITIALIZATION FOR TROPICAL STORM GORDON – 9/11/06 1200 UTC

BAD INITIALIZATION FOR TROPICAL STORM GORDON – 9/11/06 1200 UTC

4242

GFS track forecasts for Javier 12-15 Sep

Javier 130 kt

Ivan 140 kt

Isis 45 kt

Initial vortex too weakInitial vortex too weak

Incorrect initial Incorrect initial structure leads to structure leads to a west bias a west bias

4343

DataData

4444

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300tr

ac

k e

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au

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)

12 24 36 48 72 96 120

forecast period (hours)

without sondes

with sondes

TRACK FORECAST IMPROVEMENTS IN THE NCEP GLOBAL MODEL (GFS) DUE TO GPS DROPSONDES, 2000-2002

4545

Impact of Dropsondes on Model ForecastImpact of Dropsondes on Model Forecast

46

Jung and Zapotocny

JCSDAFunded by

NPOESS IPO

Satellite data ~ 10-15% impact

Impact of Removing AMSU, HIRS, GOES Wind, Quikscat Surface Wind Data on Hurricane Track Forecasts in the Atlantic Basin - 2003 (34 cases)

-20.0

-15.0

-10.0

-5.0

0.0

5.0

10.0

15.0

12 24 36 48 72 96 120

Forecast Hour

% Im

prov

emen

t NOAMSU

NOHIRS

NOGOESW

NOQuikscat

Impact of Removing AMSU, HIRS, GOES Wind, Quikscat Surface Wind Data on Hurricane Track Forecasts in the East Pacific Basin - 2003 (24 cases)

-60.0

-50.0

-40.0

-30.0

-20.0

-10.0

0.0

10.0

20.0

30.0

12 24 36 48 72

Forecast Hour

% Im

pro

vem

ent

NOAMSU

NOHIRS

NOGOESW

NOQuikscat

Better

Worse

Worse

Better

EPAC

ATL

Impact ofREMOVING

SatelliteData

4747

Think ConceptuallyThink ConceptuallyThink ConceptuallyThink Conceptually

Ask yourself what is happening in Ask yourself what is happening in reality?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 What are possible error mechanisms of a model (error mechanisms always a model (error mechanisms always exists)?exists)?

• How might these error mechanisms How might these error mechanisms affect the forecast?affect the forecast?

Ask yourself what is happening in Ask yourself what is happening in reality?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 What are possible error mechanisms of a model (error mechanisms always a model (error mechanisms always exists)?exists)?

• How might these error mechanisms How might these error mechanisms affect the forecast?affect the forecast?

4848

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).

Compared differences in hurricane and non-hurricane integrations of the

GFDL for Gloria (1985).

The hurricane modified environment influenced intensity and storm

motion

The hurricane modified environment influenced intensity and storm

motion

Hurricane influence was more extensive in the upper levels

Hurricane influence was more extensive in the upper levels

Stronger upper-level anticyclone developed closer to the storm for the

hurricane simulation

Stronger upper-level anticyclone developed closer to the storm for the

hurricane simulation

Hurricane

No Hurricane

4949

1) A model cyclone which is too intense (weak) leads to enhanced (limited) heating

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

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

3) Modified upper-level pattern changes steering pattern and/or shear pattern which can indirectly affect steering

The Power of Latent HeatingThe Power of Latent Heating

Understand the convective parameterization schemeUnderstand the convective parameterization scheme

5050

Up shear Down shear

Diabatic Heating

5151

Interpreting the TracksInterpreting the TracksInterpreting the TracksInterpreting the Tracks Understand the trackerUnderstand 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 trackerUnderstand 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)

5252

5353

5454

5555

5656

5757

5858

5959



How has each model’s track changed as compared to previous How has each model’s track changed as compared to previous cycles?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?



6060

GFS TRACK FORECASTS FOR IVAN

FROM 9/7/04 12Z – 9/11/04 12Z

GFS TRACK FORECASTS FOR IVAN

FROM 9/7/04 12Z – 9/11/04 12Z

6161

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

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

6262



How has each model’s track changed as compared to previous How has each model’s track changed as compared to previous cycles?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 has each model’s track changed as compared to previous How has each model’s track changed as compared to previous cycles?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?

6363CROSS SECTION OF GFDL MODEL RUN FOR HURRICANE PAUL 10/23/06 0000 UTC (Hour 00)

CROSS SECTION OF GFDL MODEL RUN FOR HURRICANE PAUL 10/23/06 0000 UTC (Hour 00)

6464

HOUR 12

CROSS SECTION OF GFDL MODEL RUN FOR HURRICANE PAUL 10/23/06 0000 UTC

6565

HOUR 24


6666

HOUR 36


6767

HOUR 48


6868




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 Spread is often indicative of complex steering and

ultimately uncertaintyultimately uncertainty• Why is there spread? What are the differences in the model Why is there spread? What are the differences in the model

steering? Is it due to a single feature?steering? Is it due to a single feature?



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 Spread is often indicative of complex steering and

ultimately uncertaintyultimately uncertainty• Why is there spread? What are the differences in the model Why is there spread? What are the differences in the model

steering? Is it due to a single feature?steering? Is it due to a single feature?

6969

How to resolve the difference between guidance models?

How to resolve the difference between guidance models?

7070

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.

7171

Model PerformanceModel PerformanceModel PerformanceModel Performance

No single model No single model stays on top long stays on top long • Performance can vary Performance can vary

from year to yearfrom year to year Consensus models Consensus models

typically outperform typically outperform individual modelsindividual models

7272

CONCLUDING REMARKS (TRACK CONCLUDING REMARKS (TRACK FORECASTING)FORECASTING)

CONCLUDING REMARKS (TRACK CONCLUDING REMARKS (TRACK FORECASTING)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.

7373

TC Track ExerciseTC Track ExerciseTC Track ExerciseTC Track Exercise

Forecast ScenarioForecast Scenario• Strong hurricane (90 kt) off the southern Strong hurricane (90 kt) off the southern

coast of Mexicocoast of Mexico• Significant divergence in track guidanceSignificant divergence in track guidance• Possible land interactionPossible land interaction• Possible binary (Fujiwara) interactionPossible binary (Fujiwara) interaction

Forecast ScenarioForecast Scenario• Strong hurricane (90 kt) off the southern Strong hurricane (90 kt) off the southern

coast of Mexicocoast of Mexico• Significant divergence in track guidanceSignificant divergence in track guidance• Possible land interactionPossible land interaction• Possible binary (Fujiwara) interactionPossible binary (Fujiwara) interaction

7474

7575

Extra SlidesExtra Slides

7676

7777

Extra SlidesExtra SlidesExtra SlidesExtra Slides

7878

Up shear Down shear

Diabatic Heating

7979

Rita Track Forecasts Rita Track Forecasts 1200 UTC 21 September1200 UTC 21 September

Rita Track Forecasts Rita Track Forecasts 1200 UTC 21 September1200 UTC 21 September

Severe left bias in track modelsSevere left bias in track models

8080

Rita Track ForecastsRita Track Forecasts1200 UTC 22 September1200 UTC 22 September

Rita Track ForecastsRita Track Forecasts1200 UTC 22 September1200 UTC 22 September

Remarkable improvement in track guidance: Likely the impact of surveillance data from the NOAA G-IV jet?

Remarkable improvement in track guidance: Likely the impact of surveillance data from the NOAA G-IV jet?

8181EXCELLENT EXAMPLE OF GUNS & GUNA CONSENSUS --HURRICANE ISABEL, 1200 UTC 11 SEP 2003 FORECASTEXCELLENT 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

8282

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

8383

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

8484

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

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

VERIFYING POSITION

8585

TRACK GUIDANCE OUT TO 5 DAYS – TROPICAL STORM ERNESTO, 8/26/06 1200 UTC

OBSERVED 5-DAY POSITION

OBSERVED 3-DAY POSITION

8686

2006 vs. 5-Year Mean2006 vs. 5-Year Mean

8787

Models diverge, official forecast slow, consensus Models diverge, official forecast slow, consensus very accuratevery accurate

8888

Successive GFS 7 day forecastsSuccessive GFS 7 day forecasts

Valid 23 Sep. 18z Valid 24 Sep. 00z

Valid 24 Sep. 06z Valid 24 Sep. 12z

8989

9090GFS TRACK FORECASTS FOR IVAN FROM 9/13/04 12Z – 9/15/04 12Z WERE EXCELLENT IN SPECIFYING IVAN’S LANDFALL LOCATION ON GULF COAST.

Documents

1 Jamie Rhome WMO Workshop National Hurricane Center Jamie Rhome WMO Workshop National Hurricane Center NATIONAL CENTERS FOR ENVIRONMENTAL PREDICTION WHERE