1. Hurricane Forecast Improvement Project (HFIP): Where do we stand after 3 years? NOAA Satellite Conference April 11, 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. 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. 3 Bolivar peninsula after Ike (2008)

4. 4 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

5. HFIP Baselines and Goals: Track 5

6. HFIP Baselines and Goals: Intensity 6

7. 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 7

8. 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

9. 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

10. 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) 10

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

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

13. 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) 13

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 (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 15 ATL Intensity HOPS: oper. HWRF (2011) H212: 2012 HWRF 20-25% improvement

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

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

18. 18 Impact of Radar Data

19. Impact of Aircraft Data (% improvement over D-SHIFOR) 19

20. Impact of TDR data assimilation to hurricane intensity forecast 2.2.2 (EMC) TDR assimilation OPR HWRF HWRF TDR Cross section at initial time 20

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

22. 22 Better Use of Satellite Data

23. Satellite Data The hurricane initialization problem HFIP has shown that high resolution data taken near the hurricane core and its near environment can significantly improve Intensity Forecasts. This is particularly true for high resolution wind data (Tail Doppler Radar, TDR) Flight level, dropsonde and SFMR surface wind data also important Currently available only when aircraft are within the Hurricane Roughly only 10% of hurricane NWP initialization times This is about 30% when storms are near the US coast Only other option for inner core data is Satellite data Lower horizontal and vertical resolution than TDR Hurricane core is a highly challenging environment for satellite measurements Heavy precipitation Often completely overcast Sharp gradients of cloudy and clear areas (e.g. eye wall) enhance probability of correlated errors and are often of the same horizontal scale as satellite footprints

24. Satellite Data The hurricane initialization problem Currently all influence of satellite data in hurricane regional models comes from the parent global model. –Data is generally (thinned) to resolutions considerably above hurricane scale –Tropical environment less challenging for using satellite data but there are many hurdles HFIP goal is to extract maximum information from satellite data in near- hurricane environment and inner core given the challenges –Use of cloud-impacted radiance data is very expensive (> 2X more) Implementation of new data assimilation systems in the hurricane models can improve use of all data in the hurricane environment –The Hybrid Data Assimilation has been implemented in the operational global forecast system (GFS) Resulted in large improvement in track forecasts from the GFS –HFIP is in the process of testing and evaluating a hybrid data assimilation for the HWRF system –4-Dimensional hybrid system is under development for GFS Can provide significant benefit in rapidly changing scenes like hurricanes Plan is to implement the satellite data sources that are easiest to implement first followed later by those requiring more development

25. Satellite Data Data Sources and Challenges Goes Atmospheric Motion Vectors (AMV) –Height assignment is crude and significantly impacts data quality –Hourly time resolution may be useful with emerging 4-D hybrid assimilation –Probably the easiest to implement –Available all the time Scatterometer Surface Winds –From polar orbiters, not always available –Relatively easy to implement Microwave (MW) Sounders –From polar orbiters, not always available –Problems in heavy rain areas (absorption and scattering) –Some development required IR Sounders –Higher horizontal and vertical resolution than MW sounders –Works well in cloud free areas and above highest clouds –Much of hurricane region has significant clouds –HFIP has been working on use of this data in cloudy regions –This is a very difficult problem and will require considerable development

26. Questions? 26

27. Backup Slides

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