1. Interagency Strategic Research Plan for Tropical Cyclones: The Way Ahead Dr. Naomi Surgi NOAA NCEP/EMC March 6, 2007

2. Key Findings and Recommendations –NWP modeling and data assimilation Update on NCEP Hurricane Prediction System Collaborative Ventures Overview

3. Key Findings & Recommendations NWP modeling and data assimilation –Increased skill in forecasting intensity and structure, sea state and storm surge, and precipitation is now on the horizon, much as improving track forecast skill was two decades or so ago –To meet operational needs, the Nation must be committed to supporting the key following areas: Advanced observations Advanced data assimilation technologies Advanced NWP models Investment in human and infrastructure resources –Complimentary efforts in developing next-generation operational hurricane forecast systems should be a National priority NCEP Hurricane Prediction System Navy Tropical Cyclone System

4. Key Findings & Recommendations NWP modeling and data assimilation (continued) –Development efforts of next-generation hurricane forecast systems Should form basis for projects supporting hurricane research and collaboration among experts from: -- Other Federal Agencies –Academia –International NWP centers & research community –Private sector –Sufficient human / infrastructure resources should be provided for: Development of advanced data assimilation & NWP modeling systems Operational NWP computing

5. NCEP’s Advanced DA and Modeling Plans NCEP Data assimilation development strategy 2007-2010 NCEP Hurricane Modeling Next-generation NCEP Production Suite –Preparing for the future –Production Suite: conceptual prototype –Implications

6. Data Assimilation Development Strategy Three closely related efforts –Develop Situation-Dependent Background Errors (SDBE) and Simplified 4D-Var (S4DV) –“Classical” 4D-Var (C4DV) –Ensemble Data Assimilation (EnsDA) Partners –NCEP/EMC –NASA/GSFC/GMAO –THORPEX consortium NOAA/ESRL CIRES U. Maryland U. Washington NCAR

7. Advanced Data Assimilation Development Strategy Lead Org. EncouragingRisk Factors All: cost (computer+human) increase ~3-10x SDBE +S4DV NCEP/ EMC Evolutionary development path Experience through RTMA – SDBE critical for hurricanes GSI operational 2007:Q3 Definition of appropriate covariance uncertain Multiple approaches (incl. ensembles) C4DVNASA/ GMAO Positive impact at other WX centers (ECMWF, UKMO, CMC, JMA) Various approximations Cost + (3x code) Which forecast model will be used? EnsDAEMC/ UMd, CIRES, ESRL, UW NCAR Good results at low res & low data volumes Relatively simple algorithms Ens. DOF may not be sufficient (esp. hires) Data handling for large data volumes challenging Obs & model bias correction Covariance inflation req.

8. NCEP DA Development Strategy Adapt Hybrid (?) – combine best features of advanced techniques Flexible schedule due to advanced nature of work ~yearly upgrades of SDBE/S4DV from NCEP/EMC  S4DV + EnsDA –2007-2008 Prototype development –2008 Full parallel testing Transition decision (between 3 candidates) –2009-2010 Pre-implementation testing Operational implementation

9. Next-generation NCEP Production Suite Motivation Production Suite: conceptual prototype Implications

10. Motivation Support improved NWS forecast services –Greater focus on high-impact events –Additional environmental information service responsibilities –Provide more information to users and access to more info –Probabilistic and ensemble methods Respond to external (NRC) reports –“Completing the Forecast” –“Fair Weather” Respond to NOAA Science Advisory Board reviews –Ocean modeling (National “backbone”) –Hurricane intensity (ensemble-based system)

11. Motivation Observations (number and availability) –Advanced Polar and Geostationary sounders (~100 X greater) –< 60 minute delivery –Next-generation Doppler radar Advanced technologies for –Data assimilation Discussed earlier –Ensemble processing Bias and Ensembles (e.g. NAEFS) Quantify value-added for multi-model ensemble system (e.g. CPC “Consolidation”) –“Reforecast” data base (CFS, Week2 products) –Product delivery (e.g. NOMADS)

12. Production Suite: Conceptual Prototype Model Region 1 Model Region 2 Global/Regional Model Domain Concurrent execution of global and regional applications –More efficient execution of rapid updating In-core updating for analysis increments Regional (CONUS, Alaska, Hawaii, Caribbean & Puerto Rico, Hurricanes ) Global (if requirements and resources) –All ensemble members may exchange information during execution ESMF*-based Common Modeling Infrastructure Analysis * Earth System Modeling Framework (NCAR/CISL, NASA/GMAO, Navy (NRL), NCEP/EMC)

13. Analysis -------------- Other Forecast Systems Physics (1,2,3) ESMF Utilities (clock, error handling, etc) Post processor & Product Generator Verification Resolution change 1-1 1-2 1-3 2-1 2-2 2-3 ESMF Superstructure (component definitions, “mpi” communications, etc) Multi-component ensemble + Stochastic forcing Coupler Dynamics (1,2) Application Driver ESMF* Compliant Component System * Earth System Modeling Framework ( Navy (NRL), NCAR/CISL, NASA/GMAO, NCEP/EMC) 2, 3 etc: institutionally (non-NCEP) supported EMC is exploring with NRL development of a mutually beneficial ESMF system

14. 08 09 10 11 12 Radial vel./ reflectivity Adv. DA SDBE Mesoscale Data Assimilation for Hurricane Core Advancing HURRICANE System Atm. Model physics and resolution upgrades (continuous) Atm/ocean boundary layer: wave drag, enthalpy fluxes (sea spray) Atm/ocean boundary layer: wave drag, enthalpy fluxes (sea spray) Microphysics, radiation Microphysics, radiation Incr. resolution (6km/>64L) (6km/>64L) Waves: surf-zone physics implement Waves: surf-zone physics implement Ocean: 4km. - continuous upgrades in RTOFS Ocean: 4km. - continuous upgrades in RTOFS ENSEMBLES??? ENSEMBLES??? Prototype Ens w/Navy Prototype Ens w/Navy Transition to ESMF Storm surge???

15. GDAS GFS anal NAM anal CFS RTOFS SREF NAM AQ GFS HUR RDAS Current (2007) GENS/NAEFS Current - 2007 NCEP Production Suite Weather, Ocean, Land & Climate Forecast Systems

16. CFS MFS WAV CFS & MFS GENS/NAEFS GFS Next Generation Prototype Phase 4 - 2015 Regional Rap Refresh Global HUR SREF Reforecast Hydro / NIDIS/FF Hydro NAM GDAS RDAS RTOFS AQ NCEP Production Suite Weather, Ocean, Land & Climate Forecast Systems AQ Computing factor: 81 Concurrent GFS * NAM SREF Hourly GDAS RDAS Rapid Refresh Expanded Hurricane capability (hires) Hydro/NIDIS Reforecast * Earlier delivery of GFS  concurrent combined products from NAM, GFS, SREF

17. CFS & MFS CFS MFS WAV GFS Regional Rap Refresh Global SREF Reforecast Hydro NAM GDAS RDAS RTOFS CFS & MFS AQHydro / NIDIS/FFAQ GENS/NAEFS >100% of 2015 computing Next Generation Prototype Final – 2017+ NCEP Production Suite Weather, Ocean, Land & Climate Forecast Systems GLOBAL NGATS HUR Computing factor: > 240 ECOSYSTEMS SPACE WEATHER HENS

18. Principals for moving forward 1.Data assimilation advances Major factor in improved forecast performance Provide return on investment in costly observing systems Require greater fraction of NCEP’s Production Suite 2.Maturing, ensemble-based, probabilistic systems offer the most potential benefits across wide spectrum of forecast services 3.Product delivery Time is critical (perishable product) Information availability must be maximized Conclusion

19. Summary Comprehensive Data Assimilation (DA) development strategy –2007-2010 Phased evolution of the NCEP Production Suite –2009-2015 –Results in Improved services for high impact weather Application of advanced data assimilation techniques for improved model initial conditions More efficient –Use of computing –Incorporation of new product lines for improved services Earlier product delivery More uniform and informative product stream –Advanced ensemble suite including components supported outside NCEP –Improved statistical post-processing –Reforecast and Reanalysis become operationally supported –Consistent with ESMF DA development strategy and interagency collaborations (current and anticipated)

20. Resources Improving intensity/structure, etc. - complex problem – not only scientifically Requires resources for science, obs, modeling systems, computing and infrastructure (in correct proportions) Collaborations are integral to effort Will only prove beneficial IFF collaborative efforts have sufficient resources (both $$$ and human)