1. Hurricane Forecast Improvement Project (HFIP): Where do we stand after 3 years? Bob Gall – HFIP Development Manager Fred Toepfer—HFIP Project manager Frank Marks – HFIP Research Lead Ed Rappaport – HFIP Operations Lead March 6, 2013

2. 2 The HFIP Project – Vision/Goals Vision o Organize the hurricane community to dramatically improve numerical forecast guidance to NHC in 5-10 years Goals o Reduce numerical forecast errors in track and intensity by 20% in 5 years, 50% in 10 years o Extend forecasts to 7 days o Increase probability of detecting rapid intensification at day 1 to 90% and 60% at day 5

3. HFIP Baselines and Goals: Track 3

4. HFIP Baselines and Goals: Intensity 4

5. HFIP Overall Strategy Use global models at as high a resolution as possible to forecast track out to 7 days Use regional models at 1-3 km resolution to predict inner core structure to meet intensity goals out to 5 days including rapid intensification Hybrid DA for both regional and global using as much satellite and aircraft data as possible Both regional and global models run as ensemblesBoth regional and global models run as ensembles Statistical post processing of model output to further increase forecast skill

6. Track Error of Models (2010-2011) (% Improvement over HFIP baseline)

7. Impact of Aircraft Data (% improvement over D-SHIFOR) 7

8. How are we doing? The HFIP goals are for model products delivered from NCEP to NHC. –The delivery date for these goals is hurricane season 2014 The following show the operational models (Global and Regional) performance for hurricane track and intensity in the Atlantic for latest hurricane season (2012) 8

9. Baseline skill 5-year skill goal GFS HWRF GFDL Comparison of 2012 NCEP Operational Models to the 5 Year HFIP Goal: Track

10. Baseline skill GFS HWRF Comparison of 2012 NCEP Operational Models to the 5 Year HFIP Goal: Intensity GFDL HFIP 5 year Goal

11. 11 Stream 1.5 Results for 2012

12. AHW HWRF FSU FIM GFS NOGAPS TVCA GFDL ECMWF UKMET Canadian Model

13. AHW HWRF FSU Intensity Consensus Wisconsin GFDL DSHP LGEM SPC3 TC-COAMPS

14. The upgrade to the 3km triple-nested HWRF is a result of multi-agency efforts under HFIP support –EMC - Computational tuning to speed up the model, nest motion algorithm, physics improvements, 3km initialization and pre-implementation T&E –HRD/AOML - multi-moving nest, nest motion algorithm, PBL upgrades, interpolation routines for initialization and others. –DTC - code management and maintain subversion repository –ESRL - Physics sensitivity tests and idealized capability –NHC - Diagnose the HWRF pre-implementation results –URI - 1D ocean coupling in Eastern Pacific basin 2012 HWRF Upgrades 14

15. 2012 3km HWRF Operational Upgrade Summary HOPS: oper. HWRF H212: 2012 HWRF ATL Tracks Significant Improvements of H212 –Track/intensity forecast skills for 2011/2010 seasons on Atlantic basin 20- 25% improvement against HOPS –Track forecast skills of H212 of Eastern Pacific basin maximum 25% over the HOPS in 2011 season, but little degradation at day 4 and 5 in 2010 season mainly due to Hurricane Frank –Intensity of 2011 EP basin with over 40% to HOPS. Significant improvements in intensity bias is noted for both Atlantic and Eastern Pacific, for both 2010-2011 seasons. –The storm structure in terms of storm size and PBL height significantly improved –Much improved wind-pressure relationship in high wind speed regime 15 ATL Intensity HOPS: oper. HWRF H212: 2012 HWRF 20-25% improvement

16. 16 Impact of Radar Data

17. Impact of TDR data assimilation to hurricane intensity forecast 2.2.2 (EMC) TDR assimilation OPR HWRF HWRF TDR Cross section at initial time 17

18. With TDR Impact of TDR Data In Operational HWRF Without TDR With TDR Track ErrorIntensity Error

19. Questions? 19

20. Extra Slides

21. Comparison of 2012 NCEP Operational Models to the 5 Year HFIP Goal: Track

22. Comparison of 2012 NCEP Operational Models to the 5 Year HFIP Goal: Intensity

23. Statistical Post Processing Statistical Post Processing can add skill to dynamical forecasts. There are a number of techniques based on ensembles or individual models. One method is shown in the following figure –From the FSU Multi-Model Ensemble (MMEN) which forms a weighted mean of the many global and regional models run both operationally and by HFIP in real time.

24. 2012 all storms

25. Genesis 25

26. Verification of model genesis for operational global models All models have a bias towards over-prediction, caused by both false alarms as well as genesis occurring in the forecast long (>>48h) before observed genesis. 4-ensemble consensus close to reliable up through 50-60%. 26

27. NHC Hurricane Genesis Statistics 27