Science and Technology Infusion Plan for Numerical Prediction

1. Science and Technology Infusion Plan for Numerical Prediction Jeff McQueen NWS S&T Committee September 17, 2002

2. Outline • Team Composition • Vision • Key Service Gaps • Key NP Solutions • Outstanding R&D Needs • Infusion Strategy • Summary

3. Jeff McQueen – NWS/OST Paul Dallavalle – NWS/OST Steve Koch – OAR/FSL Ralph Petersen – NWS/NCEP Dave Stensrud – OAR/NSSL Stan Benjamin – OAR/FSL Michael Smith – NWS/OHD Numerical PredictionTeam Composition

4. Vision Common Model Framework For Climate/Weather/Water • Improved Model Predictions: • Improved Initialization: • Increased Use of Remote Sensed Data • Improved Small-Scale Data Assimilation • More Realistic Physics: • Clouds, PBL, Radiation, Land & Water Interactions • Increased Resolution _ To Reach Operational Product & Service Improvement Goals Numerical Prediction Increased Probabilistic Forecasts thru Ensembling & Postprocessing • Drive Improved Applications • - Aviation, Marine, Hydro, Tropical, AQ…

5. Coupled atm-ocean global model Coupled (L/A/H/AQ) WRF Framework Numerical Prediction Pathway to Unified Common Model Framework Coupled Land-atm-ocean-ice global model SFM Global/Climate GFS Deterministic &Probabilistic Deterministic &Probabilistic ROFS Ocean Unified fully coupled framework for climate/weather/water Eta Coupled (L/A/H/AQ) WRF Framework RUC Regional NOAH/ AHPS Hydrologic Hurricane GFDL Hazards/AQ Hysplit 2002 2007 2012 2020 time

6. Linking Model Advances to Service Improvements Numerical PredictionKey Service Gaps • Improve Forecasts of Mesoscale Phenomena: • Severe Storm, Gravity Waves, Turbulence • Precip Types, Cloud, Surface Properties • Improve Confidence Levels and Range of Probabilities • More Accurate Warm and Cool Season QPF • Storm Track & Intensity Forecasts & Associated QPF • Improve Week Two to Seasonal Range Forecasting • Improved Forecasts of Global Ocean Conditions • Implement Air Quality Forecasts

7. Service Gap More Accurate Mesoscale Phenomena Forecasts • WRF(NA, HRW, RRW) , SREF • Advanced Data Assimilation • Remote Sensing Upgrades • Advanced Cloud Physics (WRF) • Improved Land Surface Models (NOAH) & hydrologic coupling • Improved Observations into Cloud Analyses & LDAS • Improved Low-Level Wind Forecasts by 20% • Improved Convection Forecasts by 15% • Improved Cloud Properties (Icing, C&V..) • Improved Visibility by 10% • Improved QPF by 10% Improved Confidence Levels and Range of Probabilities • Advanced Ensembles (SREF;GFS) • Targeted Obs Techniques • Ensemble PDFs, Neural Nets • Provide a Range of Forecasts & Uncertainty • Improved Forecasts Downstream of Data-Sparse Areas Numerical PredictionKey Solutions Projected Solution Impact

8. Numerical PredictionKey Solutions Service Gap Projected Solution Impact More Accurate Warm Season Precipitation • Hurricane WRF DA/Model • North Amer. WRF, GFS, HRW • Ensembles/ Targeted obs • 20% increase in Intensity • Marine: 20% Improvement More Accurate Cool Season Precipitation • North Amer. WRF, GFS • Ensembles/ Targeted obs • R/S Resolved w/in 30km • Mtn QPF Resolved to 30km Improve Long Range Forecasting • GFS, Global Ensembles • Seasonal Forecast Model • Coupled Ocean/Atm/Land • Improve Week Two to Seasonal Forecasts of Temp/Precip/Hazards Improve Forecasts of Global Ocean Conditions • Increased Accuracy of ENSO/SST Anomalies • 100% Ocean/Lake Coverage • Gulf stream position • Upgrade Global Ocean Data Assimilation System(GODAS) Coupled Land/Ocean/Ice GFS • Upgrade Wave Model (10 km) • Great Lakes System Implement Air Quality Forecasts • Implement IOC w/ Transition to Fully Coupled WRF-Chem • Provide Consistent Guidance • Support EPA Mission

9. DTE R&D 10 02 05 06 07 08 04 09 11 12 03 Numerical Prediction Key S&T Solutions Adv. Assim. * NPOESS Cloud/GODAS Radar Data Assimilation Explicit cloud* & Adv physics Existing cloud physics Coupled AQ Physics Common* SFM/GFS Coupled* ocean/atm/land Common* Global-30/ Regional-3 GFS T254 Global Global Ocean Model SFM T62 Deployment Climate cpld AQ WRF-4* Eta, RUC,HRW Ozone WRF: NA-8, RR, HRW OTE Regional/AQ Distributed basin* Hybrid veg/soil AHPS Hydrologic WRF SREF Global, SREF Multi-model* Probabilistic Ensemble PDF Neural Nets Eta/GFS MOS Super Computing 2x 9x 14x 36x 80x

10. Numerical PredictionOutstanding R&D Needs • Enhanced Methods to Assimilate Increasing Volume of Remote Sensed Data • Develop Small-scale Assimilation Techniques and Deploy Obs Database • Improve High Resolution Physics • Improved Hydrological-Ocean-Atmosphere coupling • Develop Mesoscale Verification Techniques • Improved Methods to Convey Confidence Levels and Range of Probabilities • Reduced Model Biases • Merge Ensembles with High-Resolution Models

11. Numerical PredictionInfusion Strategy • Emphasize Partnerships • WRF; ESMF (NOAA, NCAR, FAA, AFWA, Navy, NASA, Universities) • Develop Common Architecture • Common Model/Data Assimilation Framework • WRF, Radiative Transfer Models, ESMF • Multi-disciplinary Coupled Codes • Atmosphere, Hydrologic, Ocean, AQ • Teragrid Concept for Increased Data Nodes • Implement Modeling Testbed • JCSDA • NOAA/NCAR DTC Universities, Labs, Other Satellite Universities, Labs, Other Hurricane Climate Observations NWS NWP Severe Coastal Marine Aviation

12. Numerical PredictionTowards a Common Modeling Infrastructure • Weather Research and Forecast (WRF) Model • State-of-the-Science, Common Infrastructure System • Supporting Advanced Regional- to Local-Scale Research & Operational Forecasting • More Effective and Timely Transition of Research into Operations • Partners: NOAA, NCAR, USAF, USN, FAA • Unified Global, Climate and Mesoscale Numerical Forecast System • Improve Climate/Global/Weatherpredictions

13. Numerical Prediction WRF Framework Supports Deterministic & Probabilistic Forecasts Aviation Marine Tropical Severe Storms Winter Wx WRF Core 1 Initial Conditions & Ensemble Perturbations WRF Core 2 Data Assimilation SREF GLOBAL WRF Core 3 Hydrologic Air Quality Hazards Physics Options (ESMF)

14. Numerical PredictionInfusion Strategy: Testbed WRF Contributed Code NCAR Universities & Labs OAR Developmental Test Centers NRL WRF Reference Code NCEP Operational Test Centers FNMOC WRF Operational Code AFWA

15. Vision Common Model Framework For Climate/Weather/Water • Common Climate/ Global System • More Realistic Cloud Physics • Improve Use of Existing & New Observations • WRF Framework • Advanced Ensembling • Cloud Analysis • Adv. Small-scale Data Assimilation • Adv. Physics/ Coupled AQ Numerical PredictionSummary Supporting NWS Service missions • R&D Needs • Assimilation of Increasing Volume of Remote Sensed Data • Small-scale Assimilation Techniques • Improved Representation of Non-Hydrostatic Scale Physics • Probabilistic Approaches • Mesoscale Verification Techniques Increasing Performance 2002 2007 2012 2020

16. Roadmaps • Resolution Time-Series • Observational Needs Back Ups

17. Data Assimilation Roadmap

18. Numerical Prediction Roadmap 20002 2004

19. 10 02 05 06 07 08 04 09 11 12 03 Architecture Key S&T NP Timeline Cpld 80kL64 Annual AGCM 381k28L 7mo-1/mon SFM 95kL64 Climate (1/mon) GFS 45k64L 384h /1h GFS 55k64L 384h/3h Cpld 30kL100 Global (4x/day) WRF 8k70L 84h/1h Eta 12k64L 84h/3h WRF 4k/100L 96h/1h Deployment North America (4x/day) RRW 8kL70/18h 12x RRW 11kL60/12h RUC 20kL50/12h OTE RRW (8x/day) NMM 8kL64 48h/3 h WRF6kL70 48h/2 h WRF 2k/100L 48h/1 h DTE HRW (1x/day) ROFS 20k/Atl 48h/ 24h ROFS 20k/glb 5 day Cpld 20k/glb 2 week R&D Ocean Multi-model 70kL60 SREF 12kL64 GFS 90kL42 SREF 18kL60 GFS 95kL42 SREF 48kL50 Probabilistic

20. Numerical Prediction AdvancesResolution and Ensemble Members Global Regional Physics Dta Assim 2003 2007 2012

21. Numerical Prediction AdvancesSupercomputing Increases FY03: 1408 1.3 Ghz SP Processors

22. Numerical PredictionKey Observational Gaps and S&T Solutions

23. Ensemble Forecasts Ensembles can provide information on the likely range of forecast parameters and forecast uncertainty to users Ensemble spread can be used to determine how observational data influences neighboring regions in data assimilation schemes Mean value Range

24. Next-Generation Operational Models Cloud-scale Modeling (dx = dy = 2 km, dz = 500 m, dt = 12 s, 160 x 160 x 20 km domain ) Surface temperature, surface winds and cloud field at 2 hours Many Science and Technology Questions Remain

25. Moore, OK Tornadic Storm Moore, OK Tornadic Storm NEXRAD Radar Observations 2-Hour CAPS Computer Forecast Down to the Scale of Counties 12-hour NWS Forecast (unable to represent individual thunderstorms) Potential Cloud-scale model benefits

26. Numerical Prediction AdvancesDissemination Requirements2007

27. TO ANTICIPATE THIS USING THIS Current Operational Forecast Models Operational forecast models predict: • Mesoscale and synoptic flow patterns • Precipitation via parameterizations that are unable to resolve individual storms Important storm-scale weather cannot be resolved:

28. GLOBAL COUPLED OCEAN-ATMOS LAND MODEL 100 - 500 km REGIONAL UNCOUPLED LAND-HYDRO MODEL 1-10 km RUNOFF SNOWPACK STREAMFLOW SOIL MOIST Towards a Coupled Modeling System with downscaling to hydrology models GLOBAL OCEAN 4DDA GLOBAL ATMOS 4DDA GLOBAL LAND 4DDA SST PREDICTION REGIONAL LAND 4DDA PRECIP, Ts, LAND-SFC FORCING REGIONAL ATMOS 4DDA GLOBAL COUPLED ATMOS-LAND MODEL 30 - 100 km REGIONAL COUPLED ATMOS-LAND MODEL 10 - 30 km GENERAL CIRCULATION LATERAL B.C.