1. TC Track Guidance Models

2. 2 Hierarchy of TC Track Guidance Models: Statistical –Forecasts based on established relationships between storm-specific information (i.e., location and time of year) and the behavior of previous storms –CLIPER Statistical-Dynamical –Statistical models that use information from dynamical model output –NHC91 still maintains skill in the eastern Pacific Simplified Dynamical –LBAR simple two-dimensional dynamical track prediction model that solves the shallow-water equations initialized with vertically averaged (850-200 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) Dynamical Models –Solve the physical equations of motion that govern the atmosphere –GFDL, GFDN, GFS, NOGAPS, UKMET, ECMWF, NAM, (HWRF) Statistical –Forecasts based on established relationships between storm-specific information (i.e., location and time of year) and the behavior of previous storms –CLIPER Statistical-Dynamical –Statistical models that use information from dynamical model output –NHC91 still maintains skill in the eastern Pacific Simplified Dynamical –LBAR simple two-dimensional dynamical track prediction model that solves the shallow-water equations initialized with vertically averaged (850-200 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) Dynamical Models –Solve the physical equations of motion that govern the atmosphere –GFDL, GFDN, GFS, NOGAPS, UKMET, ECMWF, NAM, (HWRF)

3. 3 CLIPER (CLImatology and PERsistence) Model Statistical track model developed in 1972, extended to 120 h in 1998 Required Input: –Current/12 h old speed/direction of motion –Current latitude/longitude –Julian Day, Storm maximum wind 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. Statistical track model developed in 1972, extended to 120 h in 1998 Required Input: –Current/12 h old speed/direction of motion –Current latitude/longitude –Julian Day, Storm maximum wind 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.

4. 4 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

5. 5 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

6. 6 LBAR (Limited-area BARotropic) 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 days Shallow water equations on Mercator projection solved using sine transforms Initialized with 850-200 mb layer average winds/heights from NCEP global model (GFS) Sum of idealized vortex and current motion vector added to large-scale analysis Boundary conditions from global model 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 days Shallow water equations on Mercator projection solved using sine transforms Initialized with 850-200 mb layer average winds/heights from NCEP global model (GFS) Sum of idealized vortex and current motion vector added to large-scale analysis Boundary conditions from global model

7. 7 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

8. 8 Bogussing 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 can significantly affect the surrounding environment –vertical shear Creating and inserting a bogus is not straight forward –Forecast can be very sensitive to small changes in the bogus storm Bogus storms tend to be too resilient during ET –Bogus 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. Bogussing involves an analysis of synthetic data to describe the TC vortex. Bogussing can significantly affect the surrounding environment –vertical shear Creating and inserting a bogus is not straight forward –Forecast can be very sensitive to small changes in the bogus storm Bogus storms tend to be too resilient during ET –Bogus retains warm core too long leading to poor intensity and structure forecasts

9. 9 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

10. 10 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

11. 11 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) 180-384 hours T190L64 (~ 80-km grid spacing and 64 levels) 180-384 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) 180-384 hours T190L64 (~ 80-km grid spacing and 64 levels) 180-384 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)

12. 12 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.

13. 13 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.

14. 14 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

15. 15

16. 16 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

17. 17 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

18. 18 HWRF Hurricane Wilma

19. 19 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”

20. 20 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

21. 21 GFS ENSEMBLE FOR RITA – 9/19/05 12Z

22. 22 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

23. 23 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

24. 24 2001-2003 Atlantic GUNA Ensemble TC Forecast Error (nm) 2001-2003 Atlantic GUNA Ensemble TC Forecast Error (nm) 619358176Number of Forecasts467229

25. 25 Excellent example of GUNA consensus: HURRICANE ISABEL, 1200 UTC 11 SEP 2003

26. 26 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 –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 model 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 model

27. 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

28. 28 Goerss Corrected Consensus CCON 120 h FSP: 36%CGUN 120 h FSP: 33% Small improvements of 1-3%, but benefit lost by 5 days.

29. TROPICAL CYCLONE INTENSITY FORECAST MODELS

30. Statistical Models: SHIFOR (Statistical Hurricane Intensity FORecast). Based solely on historical information - climatology and persistence. (Analog to CLIPER.) Statistical/Dynamical Models: SHIPS (Statistical Hurricane Intensity Prediction Scheme): Based on climatology, persistence, and statistical relationships to current and forecast environmental conditions. Dynamical Models: GFDL, GFS, UKMET, NOGAPS. Based on the present and the future by solving the governing equations for the atmosphere (and ocean).

31. TROPICAL CYCLONE INTENSITY STATISTICAL FORECAST MODELS SHIFOR (Statistical Hurricane Intensity FORecast): Based solely on climatology and persistence. SHIPS (Statistical Hurricane Intensity Prediction Scheme): Based on climatology, persistence, and current/predicted environmental conditions. DSHIPS (Decay SHIPS): same as SHIPS except when track forecast points are over land – when a decrease in intensity following an inland decay model is included. DSHIPS modified to include information about oceanic heat content and inner core convection (using infrared satellite imagery). SHIFOR (Statistical Hurricane Intensity FORecast): Based solely on climatology and persistence. SHIPS (Statistical Hurricane Intensity Prediction Scheme): Based on climatology, persistence, and current/predicted environmental conditions. DSHIPS (Decay SHIPS): same as SHIPS except when track forecast points are over land – when a decrease in intensity following an inland decay model is included. DSHIPS modified to include information about oceanic heat content and inner core convection (using infrared satellite imagery).

32. THE SHIPS MODEL (+) SST POTENTIAL (VMAX-V): Difference between the maximum potential intensity (depends on SST) and the current intensity. (-)VERTICAL (850-200 MB) WIND SHEAR: Current and forecast. (+) PERSISTENCE: If it’s been strengthening, it will probably continue to strengthen, and vice versa. (-) UPPER LEVEL (200 MB) TEMPERATURE: Warm upper-level temperatures inhibit convection (+) THETA-E EXCESS: Related to buoyancy (CAPE); more buoyancy is conducive to strengthening (+) 500-300 MB LAYER AVERAGE RELATIVE HUMIDITY: Dry air at mid-levels inhibits strengthening (+) SST POTENTIAL (VMAX-V): Difference between the maximum potential intensity (depends on SST) and the current intensity. (-)VERTICAL (850-200 MB) WIND SHEAR: Current and forecast. (+) PERSISTENCE: If it’s been strengthening, it will probably continue to strengthen, and vice versa. (-) UPPER LEVEL (200 MB) TEMPERATURE: Warm upper-level temperatures inhibit convection (+) THETA-E EXCESS: Related to buoyancy (CAPE); more buoyancy is conducive to strengthening (+) 500-300 MB LAYER AVERAGE RELATIVE HUMIDITY: Dry air at mid-levels inhibits strengthening Statistical multiple regression model relating tropical cyclone intensity change to various climatological, persistence, and environmental predictors.

33. THE SHIPS MODEL (cont.) (+)850 MB ENVIRONMENTAL RELATIVE VORTICITY: Vorticity is averaged over a large area, about 10° radius. Intensification is favored when the storm is in an environment of cyclonic low-level vorticity. (-)ZONAL STORM MOTION: Intensification is favored when TCs are moving west (-)STEERING LEVEL PRESSURE: intensification is favored for storms that are moving more with the upper level flow. This predictor usually only comes into play when storms get sheared off and move with the flow at very low levels (in which case they are likely to weaken). (+)200 MB DIVERGENCE: Divergence aloft enhances outflow and promotes strengthening (-)CLIMATOLOGY: Number of days from the climatological peak of the hurricane season (+)850 MB ENVIRONMENTAL RELATIVE VORTICITY: Vorticity is averaged over a large area, about 10° radius. Intensification is favored when the storm is in an environment of cyclonic low-level vorticity. (-)ZONAL STORM MOTION: Intensification is favored when TCs are moving west (-)STEERING LEVEL PRESSURE: intensification is favored for storms that are moving more with the upper level flow. This predictor usually only comes into play when storms get sheared off and move with the flow at very low levels (in which case they are likely to weaken). (+)200 MB DIVERGENCE: Divergence aloft enhances outflow and promotes strengthening (-)CLIMATOLOGY: Number of days from the climatological peak of the hurricane season Statistical multiple regression model relating tropical cyclone intensity change to various climatological, persistence, and environmental predictors.

34. Satellite/Oceanic Predictors have been added to SHIPS 1.GOES cold IR pixel count 3. Oceanic heat content from 2.GOES IR T b standard deviation satellite altimetry (TPC/UM algorithm)

35. A STATISTICAL TECHNIQUE TO AID IN THE FORECAST OF RAPID INTENSIFICATION: The 7 predictors used to estimate the probability of Rapid Intensification (defined as an increase in maximum wind speed of at least 25 kt over 24 h): PredictorDefinition PER Previous 12 h intensity change SHR 850-200 mb vertical shear SST Observed sea-surface temperature at T=0 POT Maximum Potential Intensity –Current Intensity RHLO 850-700 mb relative humidity STDIR Standard deviation of IR brightness temperature PIX Percentage of GOES pixels colder than -50°C

37. VERIFYING: 160 KNOTS

38. TROPICAL CYCLONE INTENSITY DYNAMICAL FORECAST MODELS GFDL, NCEP Global Model (GFS), UKMET (U.K. Met Office), NOGAPS (U.S. Navy), ECMWF (European) These models are of limited use, because of… –sparse observations –inadequate resolution (need to go down to a few km grid spacing; the GFDL, our highest-resolution operational hurricane model, is currently about 18 km) –incomplete understanding and simulation of basic physics of intensity change –biases in upper-level wind forecasts.

39. GFDL INTENSITY FORECASTS FOR KATRINA DID SHOW STRENGTHENING TO A MAJOR HURRICANE OVER THE GULF

40. Early on 19 October, Wilma deepened at a rate of ~ 10 mb/hr! GFDL FORECAST FROM 10/17/05 18Z OBSERVED GFDL MODEL DID CAPTURE SOME OF WILMA’S RAPID DEEPENING