1. An Overview of Tropical Cyclone Track Guidance Models Used by NHC Michael Brennan, Chris Landsea, James Franklin NCEP/NHC Mark DeMaria, NOAA/NESDIS/StAR Andrea Schumacher, CIRA/CSU Bernard Meisner, NOAA/NWS/Southern Region

2. 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 type of model Understand how track models are used in consensus forecasts Have an overview of future track forecast model improvements

3. Why Do Tropical Cyclones Move?

4. Primary Factors Affecting TC Motion Steering by deep-layer mean environmental wind NW drift due to interaction with N-S gradient of Coriolis parameter –“beta”-effect Depends on storm size Interaction with other environmental PV-gradients –Drift towards and left of PV gradient Interaction with land –Especially with mountainous terrain Small scale oscillations due to inner core asymmetries

5. 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

6. Statistical: CLIPER (CLImatology and PERsistence Model) Statistical track model developed in 1972 –Extended from 72 to 120 h in 2001 Required Input: –Current and12 h old speed/direction of motion –Current latitude/longitude –Julian Day, Storm maximum wind Used as a benchmark –Forecasts with errors greater than CLIPER are considered to have no skill

7. 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

8. 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 L L MEDIUM L L DEEP

9. 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

10. 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, synthetic 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)

11. 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 Extratropical Transition –Bogus retains warm core too long leading to poor intensity and structure forecasts

12. GFS, GFDL, HWRF Initial Vertical Shear Hurricane Bill Aug 2009

13. 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

14. Global Model Properties Global Dynamical Model Model Physics Horizontal Grid Spacing (or equivalent if spectral) Vertical Levels Vertical Coordinates Convective Parameterizati on Data Assimilation CMC GEM Hydrostatic Grid Point 0.30° latitude, 0.45° longitude (~33 km at 49° latitude) 80 Hybrid Sigma- Pressure Kain-Fritsch (deep) Kuo-transient (shallow) 4-D Var ECMWF Hydrostatic Spectral ~16 km91 Hybrid Sigma- Pressure Tiedtke4-D Var GFS Hydrostatic Spectral ~25 km (through FHR 192) ~80 km (FHR 192-384) 64 Hybrid Sigma- Pressure Simplified Arakawa- Shubert 3-D Var; GSI/GDAS Analysis NOGAPS Hydrostatic Spectral ~40 km42 Hybrid Sigma- Pressure Emmanuel 4-D Var; NAVDAS Analysis UKMET Non- Hydrostatic Grid Point 0.23° latitude, 0.35° longitude (~25 km in mid latitudes) 70 Hybrid Sigma- Pressure Gregory/ Rowntree 4-D Var

15. 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 one derived from an axisymmetric model vortex spun up and combined with asymmetries from a prior forecast 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

16. GFDL Model Nested Grids

17. 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; 42 vertical levels; ~75°x75°) Coupled with Princeton Ocean Model POM utilized for Atlantic systems, no ocean coupling in N Pacific systems Vortex initialized through use of modified 6-h HWRF first guess 3-D VAR data assimilation scheme But with more advanced data assimilation for hurricane core Use of airborne and land based Doppler radar data (run in parallel) Became operational in 2007 Under development since 2002 Runs in parallel with the GFDL

19. 2010 HWRF Upgrades Fixing known bugs in HWRF model (mostly related to radiation) –Led to definition of new baseline configuration for HWRF Improved initialization (Additional data in GSI near the storm environment) Surface physics changes (C d /C h coefficients calculated based on observations) Gravity wave drag parameterization

20. 2011-2013 HWRF UPGRADE PLAN Data assimilation : –Advanced initialization for hurricane core – assimilate airborne Doppler radar observations to define storm strength and storm structure (run in parallel in 2010) –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 gravity wave drag parameterizations, sea spray parameterization, atmospheric/ocean boundary layer (fluxes), microphysics, deep convection (cloud-resolving scales), radiation Ocean coupling –Replacement of POM with the HYCOM ocean model for 2011 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.

21. *HWRF *GFDL *HWRF *GFDL Grid configuration2-nests3-nests NestingForce-feedback Interaction thru intra- nest fluxes Ocean couplingPOM (Atlantic only)POM Convective parameterization SAS mom.mix. Explicit condensationFerrier Boundary layerGFS non-local Surface layerGFDL (Moon et. al.) Land surface modelGFDL slab Dissipative heatingBased on D-L ZhangBased on M-Y TKE2.5 Gravity wave dragYESNO Radiation GFDL (cloud differences) GFDL *Configurations for 2010 season

22. 500 hPa Winds in Hurricane Ike Environment 05 Sept 2008 12 UTC

23. Satellite data used in NCEP’s operational data assimilation systems MODIS IR and water vapor winds GMS, Meteosat, and GOES cloud drift IR and visible winds GOES water vapor cloud top winds SSM/I wind speeds SSM/I precipitation estimates TRMM TMI precipitation estimates NOAA-17 HIRS 1b radiances AQUA AIRS 1b radiances NOAA-15, NOAA-16, NOAA-18 and AQUA AMSU-A 1b radiance NOAA-15, -16, and -17 AMSU-B 1b radiances GOES-12 5x5 cloud cleared radiances NOAA-16 and -17 SBUV ozone profiles Satellite data also used to help estimate initial storm position, motion, intensity and wind structure for “TC Vitals” file used to initialize regional models

24. Specialized Aircraft Data NOAA Gulfstream-IV Jet flies synoptic surveillance missions in tropical cyclones that may impact land 20 to 30 GPS dropwindsondes released in storm environment to improve analysis of steering flow –Wind, T, RH profiles –~200 hPa to surface Can be supplemented with U.S. Air Force reserve and NOAA P-3 data

25. G-IV Dropwindsonde Locations Hurricane Gustav 30 August 2008

26. “Late” versus “Early” Models “Late 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” = available shortly after synoptic time Early Models: LBAR, BAM, CLIPER, interpolated dynamical models

27. 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” Small spread among the member models may imply high confidence Large spread among the member models may imply low confidence GFS Ensembles –solid lines CMC Ensembles – dashed 192 hr forecasts from 10 June 2009

28. Ensemble Forecasts Hurricane Rita 9 Sep 2005 12z

29. 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 Corrected: Unequally weighted based on expected performance

30. Consensus Track Models for 2010 Fixed –TCON: GFS, UKMET, NOGAPS, GFDL, HWRF –GUNA: GFS, UKMET, NOGAPS, GFDL Variable –TVCN: GFS, UKMET, NOGAPS, GFDL, HWRF, GFDN, ECMWF Corrected –TCCN: Corrected consensus version of TCON –TVCC: Corrected consensus version of TVCN –CGUN: Corrected consensus version of GUNA –FSSE: Florida State Super Ensemble

31. Excellent example of TVCN consensus: HURRICANE IKE, 1800 UTC 6 SEP 2008 120 Hr Verifying Point

32. 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

33. Atlantic Simplified Track Model Verification 3-year Sample (2007-2009) OFCL shown for comparison Mean Absolute Error Forecast Skill

34. Dynamical Model Skill (Early) Atlantic 2007-2009

35. Best Performing Atlantic Track Model Variability Year48-hr96-hr 1988NHC83 1989NHC83 1990BAMD Statistical Dynamical 1991NHC90 1992BAMM 1993BAMM 1994BAMD Simplified Dynamical 1995GFDI 1996GFDI 1997GFDI 1998UKMI Global Dynamical 1999UKMI 2000GFSI 2001GFSI 2002NGPIBAMS Regional Dynamical 2003GFDIGFSI 2004GFDIGFSI 2005GFDIUKMI 2006GFSINGPI 2007UKMI 2008EMXI 2009GFSICMCI

36. Official forecast performance was very close to the consensus models. Good year for FSSE. First year of availability for CMCI. Competitive, and potentially better than that (small sample). Best dynamical models were ECMWF and GFS. UKMET and NOGAPS have been consistently weaker performers over the past few years. BAMD performed poorly (strong shear). GFNI had insufficient availability.

37. Official forecast performance was very close to the TVCN consensus model. OFCL beat TVCN at 12 and 72 h. EMXI best individual model. BAMD did about as well as any of the other 3-D models. HWFI competitive with GFDL (neither was outstanding).

38. FSSE was the best consensus model in 2009. TVCN (with GFNI and EMXI) did better than TCON. Corrected consensus models TCCN, TVCN, CGUN did not do as well as their uncorrected counterparts. This was also true in 2008

39. TVCN slightly better than FSSE. Single-model ensemble not nearly as effective as multi-model ensemble. Corrected consensus model TVCN did not do as well as its uncorrected counterpart.

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

41. Errors have been cut in half over the past 15 years. 2009 was best year ever at 24-72h. Smaller samples give more erratic trends at days 4-5. Atlantic Track Error Trends

42. E. Pacific Track Error Trends

43. 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.Radius of 12-ft seas (provided by NHC’s Tropical Analysis and Forecast Branch and the Ocean Prediction Center)

44. 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

45. 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.pdf http://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.html http://www.metoffice.gov.uk/research/nwp/numerical/unified_model/new_ dynamics.html 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