1. ATMS 373C.C. Hennon, UNC Asheville Tropical Cyclone Forecasting Where is it going and how strong will it be when it gets there

2. ATMS 373C.C. Hennon, UNC Asheville Background TC forecasting is challenging –Very little known about smaller-scale TC physics –Little if any in situ data since TCs are over water most of the time –Bad forecast = lives lost Forecast centers –National Hurricane Center (NHC – Atlantic, EPAC) –Central Pacific Hurricane Center (Hawaii – Central Pacific) –Joint Typhoon Warning Center (Guam – WPAC) –India, Australia, China also maintain forecast centers

3. ATMS 373C.C. Hennon, UNC Asheville http://www.nhc.noaa.gov/gifs/tafb-20060109_3000x464.jpg Tropical Prediction Center (TPC/NHC)

4. ATMS 373C.C. Hennon, UNC Asheville Observation Sources Most will be discussed in more detail later –Geostationary Operational Environmental Satellite (GOES) Provides 24/7 coverage of tropics –Polar Orbiting satellites (QuikSCAT, AMSU) Provide more detail, less coverage than GOES –Buoys Little coverage –Ships –Aircraft recon

5. ATMS 373C.C. Hennon, UNC Asheville NHC Forecast Process http://www.nhc.noaa.gov/gifs/hps2.gif

6. ATMS 373C.C. Hennon, UNC Asheville TC Model Guidance For track forecasting (where the storm is going to go), dynamical models are best For intensity forecasting, statistical models do better There are also statistical-dynamical models, which usually blend output from a dynamical model with statistics More complete model descriptions: –http://www.srh.noaa.gov/ssd/nwpmodel/html/nhcmodel.htmhttp://www.srh.noaa.gov/ssd/nwpmodel/html/nhcmodel.htm

7. ATMS 373C.C. Hennon, UNC Asheville Track Models CLIPER (Climatology and Persistence) –Statistical –Analyzes all tracks from 1931-1970 –Least amount of skill – other models are graded against CLIPER for skill BAM (Beta Advection Model) –Uses average layer steering flow + “beta” effect –Three averaging levels (deep, medium, shallow)

8. ATMS 373C.C. Hennon, UNC Asheville

9. ATMS 373C.C. Hennon, UNC Asheville Beta Effect Describes the change in Coriolis Force with latitude (β = df/dy) Large TCs encounter different Coriolis accelerations across the storm circulation In the Northern Hemisphere, TCs will move northwest in the absence of any steering flow –Smaller TCs with strong steering currents not influenced by Beta effect

10. ATMS 373C.C. Hennon, UNC Asheville http://www.srh.noaa.gov/ssd/nwpmodel/images/bam.gif

11. ATMS 373C.C. Hennon, UNC Asheville Track Models NHC98 –Statistical/Dynamical –Combination of CLIPER, observed geopotential heights, and forecast geopotential heights (from GFS) LBAR (Barotropic model) –Simple dynamical model –Nested –Can be skillful but struggles with complex situations

12. ATMS 373C.C. Hennon, UNC Asheville

13. ATMS 373C.C. Hennon, UNC Asheville Simple Track Model Verification More skill Less skill ** Model performance varies with season

14. ATMS 373C.C. Hennon, UNC Asheville Dynamical Track Models GFDL (Geophysical Fluid Dynamics Laboratory model) –Full physics model –Coupled to the sea surface –Best track performance for 2003-2004 season GFS (Global Forecast System) –Full physics model (NCEP) –Usually above average for track

15. ATMS 373C.C. Hennon, UNC Asheville Consensus Models GUNS (GFDL,UKMET,NOGAPS) GUNA (GFDL,UKMET,NOGAPS,GFS) CONU (Average of at least two of the above) All show very good skill overall, but are generally worse than a single model for individual forecasts

16. ATMS 373C.C. Hennon, UNC Asheville Dynamical Model Verification ** Updated verification available at NHC website

17. ATMS 373C.C. Hennon, UNC Asheville TC Forecast Intensity Models Significantly less skill than track models SHIFOR (Statistical Hurricane Intensity Forecast) –Analogous to CLIPER –Based on storms from 1900-1972 –Predictors: julian day, initial intensity, intensity trend, and latitude

18. ATMS 373C.C. Hennon, UNC Asheville TC Forecast Intensity Models SHIPS (Statistical Hurricane Intensity Prediction Scheme) –Statistical/Dynamical –Uses simple regression on a variety of predictors –Most skillful intensity model currently –Used most frequently for guidance

19. ATMS 373C.C. Hennon, UNC Asheville SHIPS Predictors From DeMaria et al. 2005

20. ATMS 373C.C. Hennon, UNC Asheville TC Forecast Intensity Models GFDL –Usually more aggressive than SHIPS –Full physical model –Considered alongside SHIPS during forecast process FSU Superensemble –Experimental model developed at FSU –“Weighted Consensus” approach –Weighs errors in past forecast biases from numerous sources NHC forecast –Has been performing relatively well

21. ATMS 373C.C. Hennon, UNC Asheville More Skill Less Skill

22. ATMS 373C.C. Hennon, UNC Asheville NHC Forecast Track Errors

23. ATMS 373C.C. Hennon, UNC Asheville

24. ATMS 373C.C. Hennon, UNC Asheville NHC Intensity Forecast Error

25. ATMS 373C.C. Hennon, UNC Asheville Seasonal TC Prediction Pioneered by Bill Gray (Colorado State University) Use winter and springtime signals to predict activity for upcoming season Bill Gray

26. ATMS 373C.C. Hennon, UNC Asheville Gray’s Seasonal Predictors (Initial) Quasi-Biennial Oscillation (QBO) phase –Stratospheric wind (10-12 miles) that reverses phase every 13 months –TC activity suppressed in east phase, enhanced in west phase –Physical explanation – tied to locations in convective activity (more equatorial convection in east phase)

27. ATMS 373C.C. Hennon, UNC Asheville Gray’s Seasonal Predictors (Initial) West African Rainfall –Intense rainfall produced stronger easterly waves Sea level pressure anomaly in Caribbean –Lower = more activity 200 mb zonal wind anomaly in Caribbean –If positive, more vertical wind shear over area, less activity –Tended to persist from spring into hurricane season ENSO –Warm phase created more wind shear over Atlantic, less TC activity

28. ATMS 373C.C. Hennon, UNC Asheville Gray’s Performance Fairly good up until 1995 Since 1995, consistently underpredicted seasonal activity Predictors revised since then –ENSO, SLP anomaly, North Atlantic Oscillation phase 2005 June 1 prediction: 15 named storms –60% too low (others were low too)

29. ATMS 373C.C. Hennon, UNC Asheville NOAA Seasonal TC Prediction Consider ENSO phase, SST conditions, and phase of the Atlantic multi-decadal oscillation (AMO) –AMO is a set of atmospheric conditions that tend to occur together –Active season: warm SST, less vertical wind shear, favorable upper-atmosphere conditions –Theorized to be driven by decadal (20-30 year) shifts in the Atlantic ocean circulation

30. ATMS 373C.C. Hennon, UNC Asheville AMO http://www.cpc.noaa.gov/products/outlooks/figure2.gif

31. ATMS 373C.C. Hennon, UNC Asheville http://www.cpc.noaa.gov/products/outlooks/figure4.gifhttp://www.cpc.noaa.gov/products/outlooks/figure4.gif.