JCSDA Observation System Simulation Experiments (OSSE) Plan for GOES-R Series Sounding Mission

1. JCSDA Observation System Simulation Experiments (OSSE) Plan for GOES-R Series Sounding Mission Fuzhong Weng, STAR/SMCD Stephen Lord, NCEP/EMC Lars Peter Riishojgaard, NASA/GMAO July***, 2007 With Suggestion by Michiko

2. Outlines • Project Objectives • Methodology and System Design • Work Plan • Project Schedule • Project Milestones and Deliverables • Expected Outcomes

3. Project Objectives: • Use Full OSSEs as a Quantitative Tool for Observing System design and planning • OSSEs integrate new instruments into Data Assimilation System, thereby providing complete preparation for their use (JCSDA goal) • Apply OSSE system to GOES-R and other advanced instrument candidates to demonstrate the potential added value of • High resolution of vertical temperature and moisture structures from GOES-R hyperspectral infrared soundings for regional forecasting (e.g. flash flood and convection) • Atmospheric wind products (e.g. GOES-R infrared cloud and water vapor winds) versus a direct wind measurement • Geostationary microwave sounder

4. Project Objectives: • Provide quantitative evidence to guide future instrument initiatives for both atmosphere and oceans • There are many simulation experiments. Only full OSSEs are able to provide a full scenario of data impact. • Forecast impact as well as analysis impact • Testing configuration of observing systems

5. Real /OSSE Data Assimilation System Existing Real Observations With & Without Truth Observations Nature New & Existing Observations With & Without Nature Run Data Assimilation Analysis Verification Forecast Model Methodology and System Design Introduction to OSSE Basic Concepts

6. JCSDA OSSE Partnership • Interagency • NCEP, STAR, NASA (GMAO, GLA, SIVO), ESRL collaboration • 10 years of experience • International Effort • ECMWF, KNMI joined Joint OSSE studies last 2 years • European and Asian community interests growing • Universities and NESDIS Corporative Institutes • UWISC/CIMSS (mesoscale OSSE) • CSU/CIRA (mesocale OSSE) • MSU/GRI • Univ. of Utah • Initial Focus • Global forecast impacts of HES • HES regional impacts on hurricane and high-impact weather events

7. Methodology and System Design • Nature Run: provides observations and truth for OSSE • Global: Joint OSSE Nature run withT511 and T799 by ECMWF (Equivalent to approximately 25km and 15km grid models) • High resolution Nature Run with less than 5km grid model later • Assimilating Forecast Models: • Global Forecast System – GFS • GOES-5 at NASA/GMAO • Weather Research and Forecasting Model – WRF • Data Assimilation Systems • Gridded Statistical Interpolation (GSI) for GFS, GEOS-5, WRF • Advanced 4D-Var techniques in GSI (4DSV) • Case Selection • Hurricane and severe weather events in ECMWF Nature Run • Downscaled cases (globally) using Regional Models

8. Remark • Severe weather cases don’t have to be local to US in OSSEs. We can make GOES-R sounder data anywhere on the planet (one of the nice things about OSSEs). • While the hypothesis is that Geo IR soundings can improve prediction of severe weather events, one must not prejudge the result. Many have doubts as you might know. • Better stated as a hypothesis: “what type of observation(s) will improve the prediction of severe wx events? • Much fine resolution data will produce a larger scale impact by Super-Obbing effect.

9. Specific Events for GOES-R OSSE • GOES-R Series Sounder: Improve prediction of severe storms • GOES-R OSSE must • realistically simulate pre-convection environments (moisture features) • resolve cloud and precip structures • produce adequate temporal/spatial sampling

10. Special Requirements for GOES-R OSSE Design • Requirements for Nature Run Systems • High spatial/temporal resolution (realistic moisture features) • Advanced physics • Supercomputing environments • Synthetic radiance simulations and validation • Advanced radiative transfer models • Error models for sampled observations from Nature Run • Instrument noise properties • Data Assimilation • Advanced (4dvar) techniques to use more temporal information and adjoint NWP physics • Control run in which all the simulated data paralleling the current operational observational data stream are included • Perturbation run in which the simulated candidate observations under evaluation are added • Impacts Analysis • Comparison of forecast skill between the control and perturbation runs • Standard performance scores (focused on applications such as hurricane track and intensity, Aviation weather….) • Subjective evaluation

11. Work Plan: Phase I • Preparation of Nature Runs • Low Res Joint OSSE Global NR: 13 month T511 (ECMWF) • High Res Joint OSSE Global NR: two 5 week T799 (ECMWF) • Regional: CSU/RAMS (Candidate) • Super high res Global NR • Validation of Nature Runs • Temperature/moisture/wind • Cloud coverage: GOES imagery vs.simulations • Cloud liquid/ice statistics: Cloudsat/Calipso vs. Simulations • Initial OSSE • Experiment demonstrating the assimilation of modeled temperature and humidity fields extracted from the Nature Run

12. Work Plan: Joint OSSE Nature Runs • Joint OSSE T511 Nature Run • Produced by ECMWF • Equivalent approximately 25km grid point model • 40 km resolution in physics • 91 vertical layers • 3 hourly output from May 2005 to June 2006 integration • Realistic extratropical storm frequency and statistics, hurricane and tropical waves • Improved cloud • Suitable for global OSSEs • Joint OSSE T799 Nature Run • Produced by ECMWF • Equivalent approximately 15km grid point model • 25 km resolution in physics • 91 vertical layers • Hourly outputs for two 35day periods • Better hurricane and severe storm seasons • Suitable for most mesoscale OSSEs and to test synoptic and mesoscale impacts of GOES-R Lifespan distribution of extratropical cyclones during February 2006 in Northern Hemisphere. Red bars are for NR. Green bars are for NCEP analysis

13. Need for higher resolution Nature Run • Need for a Nature Run with higher resolution mesoscale OSSEs • Hurricanes, lake snow effects, severe storms • Less than 5km model (without cloud parameterization) • Frequency of output : 5min • Candidates • Global cloud resolving model • GFDL-ESRL (Planned delivery time 2012) • NICAM • Local high resolution global model • Using Fibonacci grid • Nested regional model • CSU RAMS (regional atmospheric modeling system) • RUC • WRF

14. Work Plan: Possibility for Regional OSSEs • Possibilities for Regional Nature run • Performance of high resolution regional models needs to be evaluated • Noise from boundary conditions must be evaluated • A 5 day Nature Run with resolution of 1-4 km and 500x500x35 • Possibly using the RAMS model (Other candidates: RUC, WRF and more) • Waiting for a Global high resolution model is a possibility • Validation of Regional Nature Run • A major challenge • Nature Run must produce statistically representative atmospheric state • Major validation effort needed • Cloud coverage: GOES imagery vs. Simulations • Cloud liquid/ice statistics: Cloudsat/Calipso vs. Simulation • Adequate regional data assimilation system beyond current state of science • Non-hydrostatic atmosphere • Analysis balance constraints • Time dependency (4D-Var) still under development • “5-year” development??

15. Work Plan: Ocean OSSE • Ocean OSSE • State of science less developed than atmosphere • Realistic real-time ocean models just coming on line • Navy to take leading role??

16. Work Plan: Phase II • Conducting the OSSE • Complete validation for ECMWF Nature Run • Construct • Conventional observations • RAOB, Air Craft, Cloud Track Wind • Satellite radiances for all existing instruments • AQUA, IASI, ASCAT • Observations from future instruments • Radiances AND • Retrieved temperature and humidity profiles • Observation errors generated by the retrieval method • Doppler Wind Lidar • Calibration process • Demonstrate impact of known instruments is statistically comparable in both Real and Nature Run worlds

17. Work Plan: Phase III • Conducting the OSSE • Direct radiance assimilation • Forward model and instrument errors • Uses of temporal information • Low and high spatial resolution data assimilation system • Demonstrate the potential importance of all information derived from GOES-R instrument suite

18. Expected Outcomes: Benefits to High-Impacts Events from Uses of GOES-R Sounders in NWP • Reduced errors in predicting hurricane Intensity • Reduced errors for predicting hurricane track • Improved prediction of surface precipitation • Reduced RMSE of temperature and water profiles at different forecast times

19. Expected Outcomes: NWP Operational Readiness for GOES-R • Provide the research environment and computational infrastructure necessary to assess operational and research computing needs for effectively transitioning GOES-R data into operational use • Establish an end-to-end process for testing and ingesting GOES-R data in NWP models • Develop a GOES-R VAR system that will use high temporal/temporal information • State-of-the art GOES-R Community Radiative Transfer Model 4DVar is performed to assimilate the most recent observations, using a segment of the previous forecast as the background. This updates the initial model trajectory for the subsequent forecast

20. VIS IR JCSDA Community Radiative Transfer Model fullly Upgraded for GOES-R

21. Expected Outcomes: Fully Validated Nature Global and Mesoscale Nature Runs • Used for other future GOES-R instrument OSSEs • Used for NOAA satellite recapitalization plan Simulated GOES-R ABI 10.35 micron band at 2-km grid spacing Simulated GOES-R ABI 3.9 micron band at 2-km grid spacing

22. Remarks • Schedule is too ambitious • Not attainable without additional computing resources • Augment NOAA R&D computer • Disk • Cpu • Increase funds for March 2008 upgrade

23. Project Schedule August 1 2007 – GOES-R OSSE Kick-off meeting, NOAA Science Center December 8, 2007 – Preliminary Results Review, NESDIS HQ September, 2008 – Mid-term Review, NOAA September, 2009 – Final Review, NOAA

24. Project Milestones and Deliverables • 2007 – Complete preparations for global OSSEs and prepare for regional OSSE • Create synthetic observations for conventional and all current satellite instruments • Run calibration experiments for conventional and current satellite instruments • Create synthetic IASI observations • Apply ECMWF T799 Nature Run for preliminary OSSE results for IASI • Create 1 mesoscale Nature Run with validation • 2008 – Complete additional 1 mesoscale Nature Run • 2009 – Document impacts of GOES-R HES on both regional and global NWP forecast skill

25. Remarks • Need to mention FY10 request, part of which supports an expanded OSSE capability

26. Budget • 2007 – 400K • 2008 – 500K • 2009 – 500K

27. Backslides: On-going Research on Validation of Joint OSSE global nature runs at NCEPOpportunities for Regional OSSE

28. Generation of OSSE Synthetic Observations ECMWF 799 RAMS Nature runs Instrument Forward model (RTA) Atmosphere simulation Merge Field of view simulation Orbit simulation Instrument Noise model dataset IGBP land cover map AVHRR NDVI Digital Elevation Model Surface property simulation Simulated observation

29. Simulated CrIS Observation (NPOESS)

30. OSE using Real Data Withdraw or Add data for existing instruments Experimental data Real observed data Control data Real observed data Data assimilation Data assimilation Analysis Analysis Analysis impact test Forecast impact test Forecast Forecast OSSE Flowchart and Validation

31. Nature Run OSSE using simulated data Simulation of data Withdraw or add data for existing instruments Experimental data Control data + Simulated data for future instruments Control data Simulated data Simulated data Calibration Evaluation of new Instruments Data assimilation Data assimilation Data assimilation Analysis Analysis Analysis Analysis impact test Analysis impact test Nature Run Forecast impact test Forecast impact test Forecast Forecast Forecast Simulated analysis and forecast are also evaluated against the Nature Run Simulated analysis and forecast impacts are compared with real impact

32. Real Nature Run Simulation Simulation of data Withdraw or add data for existing instruments Experimental data Real observed data Control data + Simulated data for future instruments Control data Real observed data Simulated data Simulated data Calibration Evaluation of new Instruments Data assimilation Data assimilation Data assimilation Analysis Analysis Analysis Analysis Analysis Analysis impact test Analysis impact test Nature Run Forecast impact test Forecast Forecast impact test Forecast Forecast Forecast Forecast Simulated analysis and forecast are also evaluated against the Nature Run Simulated analysis and forecast impacts are compared with real impact

33. Validation of ECMWF 511 nature Run

34. Work Plan: Phase 2 • Preparation • A period of severe weather in T511 NR will be identified • Model (GFS, WRF) and data assimilation scheme (GSI and WRF-Var) • Nature run • T799 NR: Two 35 days long • Capable of producing realistic hurricanes and frontal zones • Validation of Nature Run • Cloud statistics and radiance • Tropical easterly waves • Hurricane tracks • Cyclone statistics • Rossby waves • Initial OSSE • Data impact on synoptic waves • Data impact on Hurricane tracks and development • Data impact on frontal zones

35. Work Plan: Phase 3 • Preparation • A period of 5 days for the OSSE will be identified • A short description of the nature model, forecast model (WRF) and data assimilation scheme (WRF-Var) • Nature run • A 5 day nature run with 4 km resolution and 100 levels • Grid points nonhydrostatic cloud resolving model • Validation of nature run • Cloud coverage: GOES imagery vs. Simulations • Cloud liquid/ice statistics: Cloudsat/Calipso vs. simulation • Initial OSSE • Experiment of assimilating modeled temperature and humidity fields extracted from the nature run