emc.noaa/research/osse

1. Observing System Simulation Experiments The New Nature run International Collaboration Michiko Masutani NOAA/NWS/NCEP/EMC http://www.emc.noaa.gov/research/osse

2. Contributors to NOAA-NASA and International OSSEs OSSEs: Observing Systems Simulation Experiments JCSDA: Joint Center for Satellite Data Assimilation SWA: Simpson Weather Associates ESRL: Earth System Research Laboratory(formerly FSL, CDC, ETL) NCEP: Michiko Masutani, John S. Woollen, Yucheng Song, Stephen J. Lord, Zoltan Toth, Russ Treadon, John Derber JCSDA: Jim Yoe, Waymen Baker NESDIS: Thomas J. Kleespies, Haibing Sun, SWA: G. David Emmitt, Sidney A. Wood, Steven Greco, Chris O’Handley NASA/GFSC:Lars Peter Riishojgaard,Oreste Reale, Joe Terry, Ron Errico, Emily Liu, Juan Juseum, Gail McConaughy, Runhua Yang NOAA/ESRL:Tom Schlatter, Yuanfu Xie, Nikki Prive,Steve Weygandt, Dezso Devenyi,Gil Compo ECMWF:Erik Andersson KNMI: Gert-Jan Marseille, Ad Stoffelen Japan: JMA, MRI and Earth Simulator Center

3. Need for OSSEs Quantitatively–based decisions on the design and implementation of future observing systems Evaluate possible future instruments without the cost of developing, maintaining & using observing systems. There are significant time lags between instrument deployment and eventual operational NWP use.

4. The current NCEP/JCSDA system has shown that OSSEs can provide critical information for assessing observational data impacts. The results also showed that theoretical explanations will not be satisfactory when designing future observing systems. OSSEs are expensive!!! How could we reduce the cost?

5. OSSEs will help the data assimilation system with the new data DA system will be different when the actual data become available If we cannot simulate observations, how could we assimilate observations?

6. Nature Run: Serves as a true atmosphere for OSSEs Preparation of the Nature Run and simulation of basic observations consume a significant amount of resources. If different NRs are used by various DAs, it is hard to compare the results. Need one good new Nature Run which will be used by many OSSEs. Share the simulated data to compare the OSSE results from various DA systems to gain confidence in results.

7. New Nature Run by ECMWF Based on Recommendations by JCSDA, NCEP, GMAO, GLA, SIVO, SWA, NESDIS, ESRL Low Resolution Nature Run Spectral resolution : T511 Vertical levels: L91 3 hourly dump Initial conditions: 12Z May 1st, 2005 Ends at: 0Z Jun 1,2006 Daily SST and ICE: provided by NCEP Model: Version cy31r1 Completed in July 2006, rerun October 2006 High Resolution Nature Run for a selected period Hurricane season is recommended T799 resolution, 91 levels, one hourly dump Get initial conditions from low resolution-NR Nature Run home page http://www.emc.ncep.noaa.gov/research/osse/NR

8. New Nature Run Data format Data Format: Grib1 Model level: Reduced Gaussian in model resolution Surface: Reduced Gaussian in model resolution Modification done for OSSEs: Geopotential height for model level Increased pressure level data (31 levels) Potential temperature level Precipitation and radiance (change the units) • Supplemental • 1degx1deg data • Pressure level data: 31 levels • Potential temperature level data: • 315,330,350,370,530K • Selected time series: • Convective precip, Large scale precip, MSLP, • T2m,TD2m, U10,V10, HCC, LCC, MCC, TCC, • Sfc Skin Temp • All variables for potential temperature levels • Variables: T,U,V,VO,D,Z, W, Q at • 1000,850,700,500,300,250,200,100,50,10 hPa Simulation of the data must be done from model levels and at full resolution.

9. New Nature Run Distribution To be archived in the MARS system on the THORPEX server at ECMWF Accessed by external users expver=etwu Copies for US are available to designated users & users known to ECMWF (Contact Michiko Masutani) • NASA/GSFC/SIVO • (Potential access for users outside of NASA/GSFC)  • Proposed subset of the data: • The complete surface data in reduced Gaussian (N256), • Complete 1x1 pressure level data (0.16TB), • Complete 1x1 isentropic data (0.018TB), • Data sorted as verification date and forecast time. (easy access for Grads) • Time series of selected variables, • A few days worth model level data to be posted for online access,  • The complete model level data (2.4TB) must be sent using hard disks, • Simulated observations. http://www.emc.ncep.noaa.gov/research/osse/NR

10. Contacts for the New Nature Run • ECMWF Erik Andersson • NCEP Michiko Masutani • NASA/GSFC Oreste Reale(GLA) • Lars-Peter Riishojgaard(GMAO) Joe Terry (SIVO) • JCSDA • NESDIS Thomas J. Kleespies • SWA Steven Greco • ESRL Tom Schlatter • THORPEX Pierre Gauthier (DAOS) • David Person (USA) • Zoltan Toth (GIFS) • Met Office Richard Swinbank • Roger Saunders • Meteo France Jean Pailleux • KNMI Gert-Jan Marseille • Ad Stoffelen • EUMETSAT Jo Schmetz • ESA Eva Oriol • JMA Munehiko Yamaguchi, • Kozo Okamoto • MRI Tetsuo Nakazawa • Masahiro Hosaka • ES Takeshi Enomoto Extended international collaboration within the Meteorological community is essential for timely and reliable OSSEs JCSDA NCEP, NESDIS, ESRL GMAO, GLA, SIVO ECMWF KNMI THORPEX IPO ESA EUMETSAT

11. Nature Run home page http://www.emc.ncep.noaa.gov/research/osse/NR JCSDA, NCEP, GMAOGLA, SIVO & NESDIS meet regularly ESRL, ECMWF, KNMI & Met Office join through teleconferences • OSSE activities using the New Nature Run • Background • Access to the nature run • Meeting summary • Summary of results • Evaluation of the nature run • Discussion

12. Discussion Posted Forecast run is used for the Nature Run Because the real atmosphere is a chaotic system governed mainly by conditions at its lower boundary, it does not matter that the Nature Run diverges from the real atmosphere. The Nature Run should be a separate universe, ultimately independent from but parallel to the real atmosphere. The Nature Run must have the same statistical behavior as the real atmosphere in every aspect relevant to the observing system under scrutiny. A succession of analyses is a collection of snapshots of the real atmosphere. Each analysis marks a discontinuity in model trajectory. Considering a succession of analyses as truth seems to be a serious compromise in the attempt to conduct a “clean” experiment. I favor a long, free-running forecast as the basis for defining “truth” in an OSSE. -- from Tom Schlatter Posted at http://www.emc.ncep.noaa.gov/research/osse/NR

13. Discussions of representativeness error • The results depend on the representativeness error assigned. • Record of discussion • (Gert-Jan Marseille, Ad Stoffelen, Tom Schlatter, Ron Errico, Erik Andersson, Jack Woollen, Dave Emmitt, Lars-Peter Riishojgaard, Michiko Masutani) • Summary Lecture by Ron Errico • Note by Ad Stoffelen and Gert-Jan Marseille • ESA techinical note by Lorenc, AC et al. 1992, Lorenc.1992.TIDCCR4129.pdf Other ongoing discussions Evaluation of diurnal cycle Strategies of simulation of radiance data Evaluation of LOS routine

14. Initial Diagnostics of the Nature run Progress (as of 2/7/2007) Precursor run completed in early July 2006 with T159L91 model (This run was to test the scripts) The first run completed in mid-July 2006Diagnostics performed by ECMWF to check results Rerun completed in October 2006 (extra variables are saved) Selected time series provided through ftpThe first disk (May 05 - Aug 05) arrived in November 2006 All data saved at NCEP, NASA/SIVO in January 2007 Fourth disk to be sent to ESRL

15. Very initial diagnostics E-mail sent during the run The SST, ice and Ts fields look OK, with the expected seasonal variations. The Z 500 also looks OK. Looking quickly at daily 1000 hPa Z maps for the Caribbean, I've been able to spot nine hurricanes between June and November. One made landfall in Florida (see attached ps-file). There might be some more hurricanes visible in the wind field? -- Erik Andersson

16. Tropical Cyclones in The Nature Run August 22-27

17. SLP-SKEWNESS FOR EVERY MONTH JUNE 2005 TO MAY 2006By Juan Carlos Jusem (NASA/GSFC/GLA)

18. HL vortices: vertical structure Vertical structure of a HL vortex shows, even at the degraded resolution of 1 deg, a distinct eye-like feature and a very prominent warm core. -- Oreste Reale (NASA/GSFC/GLA)

19. Area averaged precipitation Tropics NH mid-latitudes SH mid-latitudes Convective precipitation Large Scale precipitation Total precipitation It takes about one month to settle tropical precipitation. - Michiko Masutani (NCEP/EMC)

20. Cyclone tracks in the Nature Run Thomas Jung, ECMWF Annual total cyclone tracks

21. May February November August

22. NH Cyclones in January 2006 Joe Terry Thomas Jung

23. Extratropical Cyclone StatisticsJoe Terry NASA/GSFC 1) Extract cyclone information using Goddard’s objective cyclone tracker • Nature Run • One degree operational NCEP analyses (from several surrounding years) • NCEP reanalysis for specific years (La Nina, El Nino, FGGE) 2) Produce diagnostics using the cyclone track information (comparisons between Nature Run and NCEP analyses for same month) • Distribution of cyclone strength across pressure spectrum • Cyclone lifespan • Cyclone deepening • Regions of cyclogenesis and cyclolysis • Distributions of cyclone speed and direction

24. Distribution of Extratropical Cyclones by Central Pressure

25. Lifespan Distribution of Extratropical Cyclones

26. Extratropical Cyclone Deepening Rates

27. Total precipitation By Juan Carlos Jusem (NASA/GSFC) SON JJA MAM DJF

28. JJA Precipitation anomaly Nature run Observed

29. Zonal wind June 2006 By Juan Carlos Jusem(NASA/GSFC) NCEP reanalysis Nature Run

30. Comparison between the ECMWF T511 Nature Run against climatology 20050601-20060531, exp=eskb, cycle=31r1 Adrian Tompkins, ECMWF TechMemo 452 Tompkins et al. (2004) http://www.emc.ncep.noaa.gov/research/osse/NR/ECMWF_T511_diag/ tm452.pdf Jung et al.  (2005) TechMemo 471 http://www.emc.ncep.noaa.gov/research/osse/NR/ECMWF_T511_diag/tm471.pdf Plot files are also posted at http://www.emc.ncep.noaa.gov/research/osse/NR/ECMWF_NR_Diag/ECMWF_T511_diag The description of the data http://www.emc.ncep.noaa.gov/research/osse/NR/ECMWF_T511_diag/climplot_README.html

31. Quickscat SFC wind SSMI 10m wind - Quikscat does not provide winds in rainy areas - Shows known bias in the W Pacific - model winds are too low in deep convective areas.

32. **Total precipitation, against GPCP, SSMI, and XieArkin TRMM, NASDA and RSS - These comparisons confirm the lack of rainfall over the tropical land masses. - We have an overestimation of precip over the high-SST regions in the tropics. - There is a tendency for deep convection to become locked in with the highest SSTs, which in the east Pacific results in a narrow ITCZ. - The TRMM NASDA-3b43 algorithm is presumed to be the most accurate of the two TRMM retrieval products.

33. The African Monsoon Region and the Tropical Atlantic Period: May – August ; 1 degree fields Oreste Reale NASA/GSFC

34. The AEJ and the African Monsoon The overall representation of the AEJ is realistic and a number of important well-known observed features are observed, such as the axis of the AEJ core slightly tilted northward and westward, a clear separation from the low-level Harmatthan flow, and a stronger low-level monsoonal flow on the western side.

35. African Easterly Waves (AEWs) August July 60W 60W 40E 40E AEWs in July/Aug show a realistic propagation speed of about 6-9 deg/day Realistic change in wave speed and structure at transition (about 15W) Realistic amplitudes and scales Zonal wind over the continent subject to strong (perhaps excessive?) diurnal cycle

36. These findings, albeit preliminary, are suggestive that the ECMWF NR simulates a realistic meteorology over tropical Africa and the nearby Atlantic and may be proof itself beneficial to OSSE research focused over the AMMA or the Atlantic Hurricane regions. - Oreste Reale

37. Diurnal cycle Tom Schlatter and Niki Prive of ESRL noted a strong a diurnal cycle in the height field at 500 mb. 150hPa U 12Z-00Z ECMWF Analysis Nature run The mechanism for the diurnal variation in the jet is quite understandable. It is present in operational analyses and in the NR, but appears to be a little too strong in the NR. Its amplitude is dependent on the location/distribution of deep convection in the model, and its variation during the day. There could also be a sensitivity to where in the vertical the parameterisation deposits the heating. - Erik Andersson and Peter Bechtold

38. Isentropic level data are available Z500 PV 350K

39. More diagnostics are being conducted or considered at NASA/GLA, SIVO, NCEP/CPC, NCEP/EMC, ECMWF, ESRL, JMA,SUNY at Albany, FSU, Reading and more The diagnostics and evaluations of the Nature Run demonstrated that OSSEs can be sped up by close collaboration among many institutes and scientists with their resources, facilities and skills.

40. NCEP maintains the parallel structure between operational and OSSE Data assimilation system Coordinated by Yucheng Song

41. Testing DWL routines Jack Woollen, Yuanfu Xie, Joe Terry, Yucheng Son, Dave Emmitt ,Michiko Masutani, Compare analysis increment by a single wind using RAOB routine and LOS routine Analysis increment in U Analysis increment in V RAOB routine LOS routine

42. Simulation of DWL SWA simulates DWL planned by NASA KNMI simulates DWL from ESA Use common BUFR table and definitions KNMI will simulate ASCAT SWA will simulate Cloud Motion Vectors - Advised by Chris Velden

43. Radiance Simulation System for OSSELars-Peter Riigeojgaard, Emily Liu, NASA/GSFC/GMAOOther resources and/or advisorsTom Kleespies, Haibing Sun (NSDIS); Paul Van Delst, Yong Han (JCSDA);Erik Andersson (ECMWF); Roger Saunders (Met Office) Orbit Simulator Geolocation Simulator Surface Property Simulator Atmospheric Profile Simulator Radiative Transfer Model Simulated Radiances

44. Orbit Simulator • Developed based on the Science Data Processing Toolkit • Geocentric-Equatorial Coordinate System (ECI)-J2000 • Six classical orbit elements (Keplerian) • Provide satellite ephemeris and attitude data (e.g. AIRS) • Orbit is near -polar and sun-synchronous with an inclination of 98.2°. • PM satellite, each time crossing the equator going north at 1:30 P.M. • Altitude is 705 km • Geolocation Simulator • Instrument scan (navigation) pattern simulation (e.g. AIRS) • Generate geodetic latitude and longitude of each FOV • Compute zenith and azimuth of the look vector • Compute solar zenith and solar azimuth angle at the look point • Determine land fraction and surface elevation by sampling • digital elevation model (DEM) within a footprint • Surface Property Simulator • Surface properties (Ts, Ps) from Nature Run • Surface emissivity and reflectivity are obtained • based on the surface material properties • Seven surface materials are considered: water, ice, • two types of soils, and three types of vegetations • The contribution of each material is determined by: • IGBP land use surface classification for • vegetation types • AVHRR NDVI imagery for vegetation • and water amounts • DEM for land fraction and elevation • Atmospheric Profile Simulator • Validated high resolution atmospheric • profiles from ECMWF (Nature Run) • Radiative Transfer Model • Currently RTTOV V.7 is used • Will be updated to RTTOV V.8 • Can simulate a more realistic • multilevel infrared cloud • radiance Simulated Radiances

45. Simulation of Conventional ObservationsCoordinators: Jack Woollen (NCEP/EMC) Joe Terry (NASA/SIVO) Considerations Data distribution depends on atmospheric conditions Cloud and Jet location Surface orography RAOB drift OSSEs for UAS are also planned Yuanfu Xie(ESRL) and others

46. Data assimilation will be conducted at NCEP/EMC, NASA/GMAO, and NOAA/ESRL Possibly by JMA-JAMSTEC and more Calibration coordinator: Michiko Masutani (NCEP/EMC) In calibrations of the OSSE, similarity in the amount of impact from existing data in the real and simulated atmosphere needs to be achieved. The difference needs to be explained based on the characteristics of the Nature Run. Calibrations to be performed every time the DA system is changed. Need to select sets of experiments to be used for calibration and standard verification.

47. Evaluation of the results Verification will be performed by the institutes where data assimilation is performed. DWL impact will be evaluated by KNMI as well. Universities and others in the research community can participate in verification.

48. Comments on Verification The results from data impact depend on variables and verification methods. The date impact depends on how the data is handled in the DA system.

49. Impact of RAOB winds on analysis Control: Conventional+ TOVS – RAOB Wind 500hPa T 500hPa W 500hPa U 500hPa V

50. Impact of non scan lidar winds on analysis Control: Conventional+ TOVS – RAOB Wind 500hPa T 500hPa W 500hPa U 500hPa V