1. An Overview of Tropical Cyclone Track Guidance Models Used by NHC Jamie Rhome and Chris Landsea, NCEP/NHC Mark DeMaria, NOAA/NESDIS/StAR Andrea Schumacher, CIRA/CSU Bernard Meisner, NOAA/NWS/Central Region

2. An Overview of NHC Track Guidance Models GENERAL OBJECTIVES By the end of this module, you should be able to… Describe the spectrum of NHC TC track models Describe the general strengths and weaknesses of each model Give an example of each

3. Hierarchy of TC Track Models Statistical –Forecasts using relationships with storm-specific information (i.e., location, date) –CLIPER Statistical-Dynamical –Statistical models with input from dynamical model output –Best models through 1980s, overtaken by dynamical models in 1990s Last statistical-dynamical NHC track model retired in 2006 Simplified Dynamical –LBAR Barotropic model initialized with vertically averaged (850-200 hPa) winds from NCEP global model + vortex –BAMD, BAMM, BAMS -> Forecasts based on simplified dynamic representation of interaction with vortex and prevailing flow (Modified trajectory in NCEP global model) Dynamical Models –Solve the physical equations of motion that govern the atmosphere and ocean –GFDL, GFDN, HWRF, NAM, GFS, NOGAPS, UKMET, ECMWF Ensemble and Consensus Forecasts –Model combinations

4. Statistical: CLIPER (CLImatology and PERsistence Model) Statistical track model developed in 1972 –Extended from 72 to 120 h in 2001 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 (Atlantic 02- 06): –100, 216, 318, 419, and 510 nmi Used as a benchmark –Forecasts with errors greater than CLIPER are considered to have no skill.

5. Simplified Dynamical: Beta and Advection Model (BAM) Method: Follows a trajectory using global model wind fields Wind fields smoothed to T25 resolution Correction for so-called “Beta-Effect” (slow drift to the NW) Steering ~ 80-90%, Beta-effect ~ 10-20% of motion Three different layer averages: Shallow (850-700 hPa) - BAMS Medium (850-400 hPa) - BAMM Deep (850-200 hPa) - BAMD BAMS BAMM BAMD

6. 6 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

7. Simplified Dynamical: Limited- area BARotropic (LBAR) Barotropic spectral model –No temperature gradients Initialized with 850-200 hPa average winds/heights from NCEP global model (GFS) Idealized vortex and current motion vector added to GFS analysis Boundary conditions from GFS Still run due to simplicity and minimal computing costs Skill limited to ~2 days except in deep tropics

8. Primary Dynamical Models Used at NHC (global and regional) GFS: U.S. NWS Global Forecast System < relocates first-guess TC vortex UKMET: United Kingdom Met. Office global model < bogus (syn. data) NOGAPS: U.S. Navy Operational Global Atmospheric Prediction System global model < bogus (synthetic data) ECMWF: European Center for Medium-range Weather Forecasting global model (no bogus) GFDL: U.S. NWS Geophysical Fluid Dynamics Laboratory regional model <bogus (spin-up vortex) GFDN: Navy version of GFDL model < bogus (spin-up vortex) HWRF: NCEP Hurricane Weather Research and Forecast regional model (vortex relocation and adjustment)

9. A Note on Bogussing Since the globally analyzed vortex does not typically represent the structure of a true TC, “Bogussing” is often employed. –Involves an analysis of synthetic data to describe the TC vortex. –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 Extra-tropical Transition Bogus retains warm core too long leading to poor intensity and structure forecasts

10. GFS vs. GFDL Initial Vertical Shear Hurricane Debby August 2000

11. Horizontal Resolution in Global Spectral Models Horizontal fields expanded in 2-D wave function series –Associated Legendre functions in latitude –Fourier series (sines and cosines) in longitude T indicates Triangular truncation (e.g., T400) –Latitude and longitude series contain same number of terms –Uniform resolution over the sphere Rule of thumb for comparison to grid-point models –  x = 40,000 km/3N, N=truncation number

12. ConfigurationGFSUKMETNOGAPSECMWF Model TypeSpectral/ HydrostaticGrid Point/non- hydrostatic Spectral/ Hydrostatic ResolutionT382L64 (~35 km 0- 180 hrs, ~80 km 180- 384 hrs), 64 vertical levels) 0.5 0 N/S, 0.4 0 E/W (~40 km at midlatitudes), 50 vertical levels T239L30 (~55 km), 30 vertical levels TL799L91 (~17 km), 91 vertical levels Convective parameterization SASMass flux scheme (Gregory and Rowntree 1990) Emanuelbulk mass flux scheme (Tiedke) Vortex initializationRelocationBogus None Vertical Coordinate System Hybrid (sigma/pressure) SigmaHybrid (sigma/ pressure) Data Assimilation3-D Var (GSI)4-D Var3-D Var4-D Var Runs per day4 (00, 06, 12, 18 UTC)2 full (00, 12 UTC) 2 intermediate (06,18 UTC) 4 (00, 06, 12, 18 UTC) 2 (00 and 12 UTC) Length of run384 hrs (16 days)Full: 144 hrs Intermediate:48 hrs 144 hrs (6 days)240 hrs (10 days) Global Model Properties

13. The Geophysical Fluid Dynamics Laboratory (GFDL) Hurricane Model Dynamical model capable of producing skillful intensity forecasts Coupled with 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 9 km

14. GFDL Model Nested Grids

15. 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 Atlantic only in 2008, HYCOM model for 2009 in Atlantic and East Pac 3-D VAR data assimilation scheme But with more advanced data assimilation for hurricane core Plans for use of airborne and land based Doppler radar data Became operational in 2007 Under development since 2002 Runs in parallel with the GFDL

16. *HWRF *GFDL Grid configuration 2-nests (coincident)3-nests(not coincident) Nesting Force-feedback Interaction thru intra-nest fluxes Ocean couplingPOM (Atlantic only) POM Convective parameterization SAS mom.mix. Explicit condensation Ferrier Boundary layer GFS non-local Surface layer GFDL..(Moon et. al.) Land surface model GFDL slab Dissipative heatingBased on D-L ZhangBased on M-Y TKE 2.5 Radiation GFDL (cloud differences) GFDL *Configurations for 2008 season

18. “Late” versus “Early” “Late Models” = models not available at synoptic time 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, NGPS  NGPI, etc) Late Models: All global models, GFDL, GFDN, HWRF “Early Models” = models available shortly after synoptic time Early Models: LBAR, BAM, CLIPER, Interpolated dynamical models

19. Ensemble Forecasts (Classic Method) A number of forecasts from 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 19

21. Ensemble Forecasts (Multi-Model Method) A group of forecast tracks from DIFFERENT PREDICTION MODELS 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 ensembles are used to create CONSENSUS forecasts. Consensus Model Types Fixed: All members must be present, linear average Variable: Some members can be missing, linear average Smart: Unequally weighted based on expected performance

22. Consensus Track Models for 2009 Fixed –TCON: GFS, UKMET, NOGAPS, GFDL, HWRF –GUNA: GFS, UKMET, NOGAPS, GFDL Variable –TVCN: GFS, UKMET, NOGAPS, GFDL, HWRF, GFDN, ECMWF Smart –TCCN: Corrected consensus version of TCON –TVCC: Corrected consensus version of TVCN –CGUN: Corrected consensus version of GUNA –FSSE (Florida State Super Ensemble, Experimental) Sophisticated “smart” consensus model developed at FSU

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

24. Track Model Verification Calculate distance from forecast to best track position Homogeneous sample –All models must be available for inclusion of a case NHC verifies tropical and subtropical stages –Extra-tropical stage excluded CLIPER model error is baseline for track forecast skill –Skill = 100*(E CLIPER -E model )/E CLIPER

25. Atlantic Simplified Track Model Verification 5-year Sample (2003-2007) GUNA and OFCL shown for comparison Mean Absolute Error Forecast Skill

26. Dynamical Model* Skill Atlantic Sample 2003-2007 *HWRF, ECMWF, most consensus models excluded due to sample size limitation

27. Track Model Performance Variability

28. Official forecast performance was very close to the consensus models. Best model was ECMWF, which was so good that it as good or better than the consensus. BAMD was similar to the poorest of the 3-D models (UKMET). AEMI excluded due to insufficient availability (less than 67% of the time at 48 or 120 h).

29. Best consensus model was TVCN, the variable member consensus that includes EMXI. It does not appear that the “correction” process was beneficial.

30. Serial Correlation of Track Model Forecasts Hurricane Dean Track Models 18 UTC 17 Aug 2007 06 UTC 18 Aug 2007 GFDI HWFI OFCL Best Track GFSI OFCL GFSI Best Track

31. Concluding Remarks on TC Track Model Guidance Dynamical models generally outperform simpler techniques No single model is always best Consensus forecasts generally better than individual models Year to year, storm to storm, forecast to forecast variability Serial correlation of same model for same storm HWRF and global models will continue to improve

32. 2008 HWRF Upgrades Upgrades to Hurricane Initialization –improve initial structure for weaker storms – improved balance – makes use of TPC observed intensity/structure Upgrades to Model –eliminate noise over topography due to moving nest (reformulate SLP) – remove erroneous residual Turbulent Kinetic Energy TKE (now consistent w/GFS physics) Preliminary results: –some improvement to track after day 3 – modest improvement to intensity reducing weak bias (particularly after day 2)

33. 2008-2012 HWRF UPGRADE PLAN Data assimilation: –Advanced initialization for hurricane core – assimilate airborne Doppler radar observations to define storm strength and storm structure –Continuous upgrades to HWRF hurricane core initialization through advanced 4-D data assimilation for winds and reflectivity Model resolution upgrades: –Increase resolution: Horizontal resolution 1-6km, Vertical resolution ~100 levels (dependent on results of current studies). Hurricane ensembles: High-resolution hurricane model ensembles. –Development of HWRF ensembles in progress Model Physics: –Continuous upgrades to atmospheric/ocean boundary layer (fluxes), microphysics, deep convection (cloud-resolving scales), radiation Ocean coupling –Replacement of POM with the HYCOM ocean model for 2009 –Coupling will be added for East/Central Pacific runs (currently Atlantic only) Other upgrades: –Coupling to land surface model with advanced surface physics for improved rainfall forecasts at landfall. Important input to hydrology and stream flow models which will address inland flooding. –Advanced Wave Model (WAVEWATCH III) to forecast waves up to the beach, i.e. improve non-linear interactions, surf-zone shallow water physics, wave interactions with currents.

34. Atlantic Track Error Trends Errors have been cut in half over the past 15 years. 2008 was best year ever.

35. Other Forecast Parameters in NHC Products 1.Intensity 2.Genesis (formation within 48 hr) 3.Size (radius of 34, 50, and 64 kt) 4.Storm Surge (within about 24 hr of landfall) 5.Rainfall (provided by the Hydrometeorological Prediction Center) 6.Tornadoes (provided by the Storm Prediction Center) 7.Wave height, direction and period (provided by NHC’s Tropical Analysis and Forecast Branch)

36. Additional Information Overview of NHC Models: http://www.nhc.noaa.gov/aboutmodels.shtmlhttp://www.nhc.noaa.gov/aboutmodels.shtml Operational Model Matrix (Comet Password Required): http://www.meted.ucar.edu/nwp/pcu2/ http://www.meted.ucar.edu/nwp/pcu2/ Global Forecast System Home Page: http://www.emc.ncep.noaa.gov/http://www.emc.ncep.noaa.gov/ HPC’s Subjective Model Biases Page: http://www.hpc.ncep.noaa.gov/mdlbias/biastext.shtml http://www.hpc.ncep.noaa.gov/mdlbias/biastext.shtml HWRF’s Main Page: http://www.emc.ncep.noaa.gov/HWRF/index.htmlhttp://www.emc.ncep.noaa.gov/HWRF/index.html GFDL Model Description (AMS Publication): http://ams.allenpress.com/archive/1520- 0493/135/12/pdf/i1520-0493-135-12-3965.pdfhttp://ams.allenpress.com/archive/1520- 0493/135/12/pdf/i1520-0493-135-12-3965.pdf UKMET Model Technical Specifications: http://www.metoffice.gov.uk/research/nwp/numerical/unified_model/new_dynamics.ht ml http://www.metoffice.gov.uk/research/nwp/numerical/unified_model/new_dynamics.ht ml User’s Guide to the ECMWF: http://www.ecmwf.int/products/forecasts/guide/Preface.html http://www.ecmwf.int/products/forecasts/guide/Preface.html NOGAPS Technical Specifications: http://www.nrlmry.navy.mil/metoc/nogaps/nogaps_char.html http://www.nrlmry.navy.mil/metoc/nogaps/nogaps_char.html