1. Hurricane Forecast Improvement Project (HFIP): Where do we stand after 3 years? NOAA Satellite Science Week March 21, 2013 Fred Toepfer—HFIP Project Manager

2. HFIP OVERVIEW 10-year program with ambitious forecast improvement goals – to reduce evacuations costs Designed and run by NOAA with non-NOAA collaborators Began in fiscal year 2009 Focus on improving numerical weather prediction model forecast guidance provided to the National Hurricane Center 2

3. 3 Track forecasting today National Hurricane Forecast System 50% improvements to hurricane track and intensity forecasts out to 7 days50% improvements to hurricane track and intensity forecasts out to 7 days Reduce cone of uncertaintyReduce cone of uncertainty Track forecasting after HFIP Improvements 50%reducedforecasterrors50%reducedforecasterrors Goals

4. Drivers for an HFIP Program Lives: More than 50% of U.S. population lives within 50 miles of coast; Number of people at risk increasing along coast and inland; 180 million people visit the coast annually Property: Value of coastal infrastructure and economy rising… now > $3 trillion; annual U.S. tropical-cyclone-related damage losses averaged about $10 billion circa 2008; averaged losses double about every ten years Forecasts: Hurricane track forecasts have improved greatly; intensity forecasts have not Research: Tropical cyclone research has been under-resourced and not well-coordinated within the meteorological community Courtesy: Ed Rappaport There is a great need and potential for substantial improvements above and beyond current research efforts in hurricane forecasting. 4 Bolivar peninsula after Ike (2008)

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

6. HFIP Baselines and Goals: Track 6

7. HFIP Baselines and Goals: Intensity 7

8. HFIP Charter and Leadership NOAA-wide HFIP Charter signed August 1, 2007 Hurricane Executive Oversight Board –Jointly chaired by AA for National Weather Services and AA for Oceanic and Atmospheric Research – Cross-NOAA Membership Current HFIP Management – Project Manager: Fred Toepfer, NWS/OST – Development Manager: Robert Gall, UCAR – Research Lead: Frank Marks, OAR/AOML/HRD – Operations Lead: Ed Rappaport, NWS/NHC 8

9. Technical Team Structure 2013 FY 2013 TeamsFY 2013 Team Leads 1. HFIP Model StrategyVijay Tallapragada, Stan Benjamin 2. Model PhysicsBrad Ferrier, Jian-Wen Bao 3. Data Assimilation/InitializationJohn Derber, Xuguang.Wang 4. Ensemble DevelopmentJeff Whitaker, Jiayi Peng 5. Post Processing and Verification Development TeamMark DeMaria, David Zelinski, Tim Marchok 6. Societal ImpactsJennifer Sprague, Rick Knabb FY 2013 TeamsStrategic TeamFY2013 Team Leads 1. Web Page Design5Paula McCaslin, Laurie Carson 2. 3 KM Physics Package2Joe Cione, Chan Kieu 3. Regional Hybrid DA System3John Derber, Jeff Whitaker 4 Use of Satellite Data in Hurricane Initialization3Tomi Vukicevic, John Knaff, Emily Liu 5. Stream 1.5 and Demo System Implementation1James Franklin, Barb Brown 6. Recon Data Impact Tiger Team.1James Franklin (NHC), Vijay Tallapragada (EMC) FY 2013 Tiger Teams FY 2013 Strategic Planning Teams

10. 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

11. 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) 11

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

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

14. Experimental Model Results for 2012 Operational HWRF (Stream 1.0) Real-time delivery to NHC of Experimental Models (Stream 1.5) Experimental Research models (Stream 2.0) 14

15. 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 15

16. 2012 3km HWRF Operational Upgrade Summary HOPS: oper. HWRF (2011) 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 16 ATL Intensity HOPS: oper. HWRF (2011) H212: 2012 HWRF 20-25% improvement

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

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

19. 19 Impact of Radar Data

20. Impact of Aircraft Data (% improvement over D-SHIFOR) 20

21. Impact of TDR data assimilation to hurricane intensity forecast 2.2.2 (EMC) TDR assimilation OPR HWRF HWRF TDR Cross section at initial time 21

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

23. 23 Better Use of Satellite Data

24. 24 Use of Satellite Data in Hurricane Initialization Develop a system using high resolution satellite data near the vicinity of the hurricane core (regional scale) Improve the capability for assimilation of satellite observations using hybrid DA for basin scale HWRF Operational Regional DA system under development Develop and test use of various satellite data sets for initialization Cloudy radiances assimilation – Development underway for Global Data Assimilation System Upper Level Outflow Environment Atmospheric Motion Vectors (AMV) (GOES winds) Best combination of satellite data

25. Initial Joint HFIP/JCSDA Workshop held in 2010 – Recommended focus on Global Cloudy Radiance Assimilation 10 th JCSDA Workshop on Satellite Data Assimilation – October 2012 Recommends: Research on better use of rapid-scan AMVs at the storm canopy level; continued to investigate if cloudy radiance assimilation can help in the thinner outflow cirrus regimes. Investigate the expansion of the TC vitals (output from imagery analysis or derived products) to provide information to the assimilation i.e. eyewall structure/strength/radius, rainbands/asymmetries/shear, system depth. Development of a tool so forecasters can articulate their perception of the current state of the storm and to translate into something objective data assimilation can use. JCSDA Workshop on Satellite Data Assimilation 25

26. Questions? 26

27. Backup Slides

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

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

30. 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.

31. 2012 all storms

32. Genesis 32

33. 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%. 33

34. NHC Hurricane Genesis Statistics 34

35. HFIP Appropriation History (2009-2013) 35 FY09FY10FY11FY12FY13 WCOSS PAC6.000M3.000M 4.000M (OMB increase) 2.000M* (OMB 1yr reduction) NWS ORF15.040M14.040M ( $6.5M NWS reduction) 13.640M* (OMB restored less $400K ) OAR ORF6.100M 6.000M TOTAL $27.140M$23.140M$23.144M$24.040M$21.640M *Anticipate Restoration in FY14 *Senate proposed full restoration of $400K

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