http://www.emc.noaa.gov/research/osseObserving System Simulation ExperimentsMichiko MasutaniThe New Nature runInternational CollaborationNOAA/NWS/NCEP/EMC

NCEP: Michiko Masutani, John S. Woollen, Yucheng Song, Stephen J. Lord, Zoltan Toth, Russ Treadon, John DerberJCSDA: Jim Yoe, Waymen BakerNESDIS: Thomas J. Kleespies, Haibing Sun, SWA: G. David Emmitt, Sidney A. Wood, Steven Greco, Chris OHandley 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 CompoECMWF:Erik Andersson KNMI: Gert-Jan Marseille, Ad StoffelenJapan: JMA, MRI and Earth Simulator Center

Need for OSSEs Quantitativelybased 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.

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

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

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.

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.

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: L913 hourly dumpInitial conditions: 12Z May 1st, 2005 Ends at: 0Z Jun 1,2006 Daily SST and ICE: provided by NCEPModel: Version cy31r1 Completed in July 2006, rerun October 2006High Resolution Nature Runfor a selected periodHurricane season is recommendedT799 resolution, 91 levels, one hourly dumpGet initial conditions from low resolution-NRNature Run home pagehttp://www.emc.ncep.noaa.gov/research/osse/NR

New Nature Run Data formatSupplemental 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 hPaData Format: Grib1

Model level: Reduced Gaussian in model resolution

Surface: Reduced Gaussian in model resolution

Modification done for OSSEs:

Geopotential height for model levelIncreased pressure level data (31 levels)

Potential temperature levelPrecipitation and radiance (change the units)Simulation of the data must be done from model levels and at full resolution.

New Nature Run DistributionTo be archived in the MARS systemon the THORPEX server at ECMWFAccessed by external usersexpver=etwuCopies for US are available to designated users & users known to ECMWF(Contact Michiko Masutani)http://www.emc.ncep.noaa.gov/research/osse/NR NASA/GSFC/SIVO (Potential accessfor 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.

Contacts for the New Nature Run

ECMWFErik AnderssonNCEPMichiko MasutaniNASA/GSFC Oreste Reale(GLA) Lars-Peter Riishojgaard(GMAO) Joe Terry (SIVO)JCSDA NESDIS Thomas J. KleespiesSWA Steven GrecoESRLTom SchlatterTHORPEX Pierre Gauthier (DAOS) David Person (USA)Zoltan Toth (GIFS) Met Office Richard Swinbank Roger SaundersMeteo France Jean PailleuxKNMI Gert-Jan Marseille Ad StoffelenEUMETSAT Jo SchmetzESA Eva OriolJMA Munehiko Yamaguchi, Kozo OkamotoMRITetsuo Nakazawa Masahiro HosakaESTakeshi 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

Nature Run home page

http://www.emc.ncep.noaa.gov/research/osse/NR OSSE activities using the New Nature RunBackgroundAccess to the nature runMeeting summarySummary of resultsEvaluation of the nature runDiscussion

JCSDA, NCEP, GMAOGLA, SIVO & NESDIS meet regularlyESRL, ECMWF, KNMI & Met Office join through teleconferences

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

Forecast run is used for the Nature RunPosted at http://www.emc.ncep.noaa.gov/research/osse/NR Discussion Posted

Discussions of representativeness errorThe 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 ErricoNote by Ad Stoffelen and Gert-Jan MarseilleESA techinical note by Lorenc, AC et al. 1992, Lorenc.1992.TIDCCR4129.pdf

Other ongoing discussions Evaluation of diurnal cycleStrategies of simulation of radiance dataEvaluation of LOS routine

Initial Diagnostics of the Nature runProgress (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 2006 Diagnostics performed by ECMWF to check resultsRerun completed in October 2006 (extra variables are saved)Selected time series provided through ftp The first disk (May 05 - Aug 05) arrived in November 2006All data saved at NCEP, NASA/SIVO in January 2007Fourth disk to be sent to ESRL

Very initial diagnosticsE-mail sent during the runThe 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

Tropical Cyclones in The Nature Run August 22-27

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

HL vortices: vertical structureVertical 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)

Area averaged precipitationTropicsSH mid-latitudesNH mid-latitudesConvective precipitationLarge Scale precipitationTotal precipitationIt takes about one month to settle tropical precipitation.- Michiko Masutani (NCEP/EMC)

Annual total cyclone tracksCyclone tracks in the Nature RunThomas Jung, ECMWF

FebruaryMayAugustNovember

Joe TerryNH Cyclones in January 2006Thomas Jung

Extratropical Cyclone StatisticsJoe Terry NASA/GSFC1) Extract cyclone information using Goddards 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

Distribution of Extratropical Cyclones by Central Pressure

Lifespan Distribution of Extratropical Cyclones

Extratropical Cyclone Deepening Rates

Total precipitation By Juan Carlos Jusem (NASA/GSFC)JJASONDJFMAM

ObservedNature runJJA Precipitation anomaly

Zonal wind June 2006By Juan Carlos Jusem (NASA/GSFC)Nature RunNCEP reanalysis

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.pdfJung et al. (2005) TechMemo 471http://www.emc.ncep.noaa.gov/research/osse/NR/ECMWF_T511_diag/tm471.pdfPlot files are also posted athttp://www.emc.ncep.noaa.gov/research/osse/NR/ECMWF_NR_Diag/ECMWF_T511_diagThe description of the datahttp://www.emc.ncep.noaa.gov/research/osse/NR/ECMWF_T511_diag/climplot_README.html

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

**Total precipitation, against

GPCP, SSMI, and XieArkinTRMM, 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.

The African Monsoon Region and the Tropical Atlantic

Period: May August ; 1 degree fields

Oreste RealeNASA/GSFC

The AEJ and the African MonsoonThe 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.

African Easterly Waves (AEWs)AEWs in July/Aug show a realistic propagation speed of about 6-9 deg/dayRealistic change in wave speed and structure at transition (about 15W)Realistic amplitudes and scalesZonal wind over the continent subject to strong (perhaps excessive?) diurnal cycleJulyAugust60W60W40E40E

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

Tom Schlatter and Niki Prive of ESRL noted a strong a diurnal cycle in the height field at 500 mb. Diurnal cycleECMWF AnalysisNature runThe 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 Bechtold150hPa U 12Z-00Z

Z500 PV 350K Isentropic level data are available

More diagnostics are being conducted or considered at

NASA/GLA, SIVO, NCEP/CPC, NCEP/EMC, ECMWF, ESRL,

JMA,SUNY at Albany, FSU, Reading

and moreThe 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.

NCEP maintains the parallel structure between operational and OSSE Data assimilation system

Coordinated by Yucheng Song

Testing DWL routines Jack Woollen, Yuanfu Xie, Joe Terry, Yucheng Son, Dave Emmitt ,Michiko Masutani, Analysis increment in UAnalysis increment in VRAOB routineLOS routineCompare analysis increment by a single wind using RAOB routine and LOS routine

Simulation of DWLSWA simulates DWL planned by NASA KNMI simulates DWL from ESA

Use common BUFR table and definitions

KNMI will simulate ASCATSWA will simulate Cloud Motion Vectors- Advised by Chris Velden

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)Geolocation SimulatorSurface Property Simulator

Atmospheric Profile SimulatorRadiative Transfer ModelSimulated Radiances Orbit Simulator

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 footprintSurface 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 elevationAtmospheric 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 radianceSimulated Radiances Orbit SimulatorDeveloped based on the Science Data Processing ToolkitGeocentric-Equatorial Coordinate System (ECI)-J2000Six 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

Simulation of Conventional ObservationsCoordinators: Jack Woollen (NCEP/EMC) Joe Terry (NASA/SIVO)ConsiderationsData distribution depends on atmospheric conditions Cloud and Jet locationSurface orographyRAOB drift

OSSEs for UAS are also plannedYuanfu Xie(ESRL) and others

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. Data assimilation will be conducted atNCEP/EMC, NASA/GMAO, and NOAA/ESRLPossibly by JMA-JAMSTEC and more

Evaluation of the resultsVerification 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.

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. Comments on Verification

Impact of RAOB winds on analysisControl: Conventional+ TOVS RAOB Wind500hPa T500hPa U500hPa W500hPa V

Impact of non scan lidar winds on analysisControl: Conventional+ TOVS RAOB Wind500hPa T500hPa U500hPa W500hPa V

SummaryOSSE and its evaluation will become affordable to the universities and academic communities.The new Nature Run has been prepared with international team work: ECMWF, NOAA, NASA, ESAOSSEs have to be an ongoing work.

We need to present the levels of confidence in the results from OSSEs. Comparison of OSSEs with various DA systems will be very important.

OSSEs are expensive, but effective collaboration and effective distribution of resources will significantly reduce the cost.

This will also speed up the performance and enhance the credibility of the results.

http://www.emc.ncep.noaa.gov/research/osseEND

OSSE is a simulation experiments for Observing systems.This way we can provide quantitative evaluation of future instruments and observing systems. Same time we can prepare DA system in advance.

OSSE has been used to evaluate instruments but it could be used to test the observing systemsOSSE is a simulation experiments for Observing systems.This way we can provide quantitative evaluation of future instruments and observing systems. Same time we can prepare DA system in advance.

OSSE has been used to evaluate instruments but it could be used to test the observing systems