Presentation on theme: "Joint OSSEs Michiko Masutani and Jack Woollen Lars Peter Riishojgaard"— Presentation transcript:
1 Joint OSSEs Michiko Masutani and Jack Woollen Lars Peter Riishojgaard Internationally collaborative Full OSSEs sharing the same Nature RunProgress in simulation of observation, Calibration, and OSSEsMichiko Masutani and Jack WoollenNOAA/NWS/NCEP/EMCLars Peter RiishojgaardJoint Center for Satellite and Data AssimilationTTISS Sep ,2009
2 International Collaborative Joint OSSE - Toward reliable and timely assessment of future observing systems -Participating InstitutesNational Centers for Environmental Prediction (NCEP)NASA/Goddard Space Flight Center (GSFC)NOAA/ NESDIS/STAR, ECMWF,Joint Center for Satellite and Data Assimilation (JCSDA)NOAA/Earth System Research Laboratory (ESRL)Simpson Weather Associates(SWA),Royal Dutch Meteorological Institute (KNMI)Mississippi State University/GRI (MSU)University of UtahOther institutes expressing interestNorthrop Grumman Space Technology, NCAR,NOAA/OAR/AOML, Environment of Canada,National Space Organization (NSPO,Taiwan),Central Weather Bureau(Taiwan),JMA(Japan), University of Tokyo, JAMSTEC,Norsk Institutt for Luftforskning (NILU,Norway), CMA(China), and more
3 Planned OSSEs (as of Sep. 2009) Future GPSRO constellation configuration and impactLidia Cucurull (JCSDA,NOAA/NESDIS), NCAR, CWB, NPSOGOES-R preparation experiments (NOAA/NESDIS)Tong Zhu,, Fuzhong Weng, T.J. Kleespies, Yong Han, Q. Liu, Sid Boukabara (NOAA/NESDIS),Jack Woollen, Michiko Masutani (NOAA/EMC), L. P Riishojgaard (JCSDA)),Wind Lidar (GWOS) impact and configuration experiments for NASAM.Masutani(NCEP), L. P Riishojgaard (JCSDA)Simulation of DWL planned from NASA and selected DWL from ESAG. David Emmitt, Steve Greco, Sid A. Wood,(SWA)Simulation of ADM-Aeolus and follow up missionG.J. Marseille and Ad Stoffelen (KNMI)Evaluation of Unmanned Aircraft SystemYuanfu Xie, Nikki Prive, Tom Schlatter, Steve Koch (NOAA/ESRL)Michiko Matsutani, Jack Woollen (NOAA/EMC)NPP (CrIS and ATMS) regional impact studies (NASA)C. M. Hill, P. J. Fitzpatrick, X. Fan, V. Anantharaj, and Y. Li (MSU)
4 Other OSSEs planned or considered Seeking funding but start with volunteersOSSE to evaluate data assimilation systemsRon Errico (GMAO)OSSEs for THORPEX T-PARCEvaluation and development of targeted observationZ. Toth, Yucheng Song (NCEP) and other THORPEX team membersAssimilation with LETKF possibly by 4D-varT. Miyoshi(UMD) and Enomoto(JEMSTEC)Analysis with surface pressureGil Compo, P. D. Sardeshmukh (ESRL)Data assimilation for climate forecastsH. Koyama, M. Watanabe (University of Tokyo)Data assimilation with RTTOVSEnvironment CanadaSensor Web Uses same Nature RunNASA/GSFC/SIVO, SWA , NGCVisualization of the Nature runO. Reale (NASA/GSFC/GLA),H. Mitchell(NASA/GSFC/SIVO)Regional DWL OSSEsZhaoxia Pu, University of Utah
5 Data Denial Experiments Simulated data (OSSE)Real data OSEObserving System Simulation ExperimentTypically aimed at assessing the impact of a hypothetical data type on a forecast systemSimulated atmosphere (“Nature Run”)Simulated reference observations (corresponding to existing observations)Simulate perturbation observations (object of study)Verify simulated observationSimulate observational errorControl run (all operationally used observations)Perturbation run (control plus candidate data)Compare!Costly in terms of computing and manpowerObserving System ExperimentTypically aimed at assessing the impact of a given existing data type on a systemUsing existing observational data and operational analyses, the candidate data are either added to withheld from the forecast system, and the impact is assessedControl run (all operationally used observations)Perturbation run (control plus candidate data)Compare!
6 Need for OSSEs Benefit of OSSEs OSSEs help in understanding and formulating observational errorsDAS (Data Assimilation System) will be prepared for the new dataEnable data formatting and handling in advance of “live” instrumentOSSE results also showed that theoretical explanations will not be satisfactory when designing future observing systems.♦Quantitatively–based decisions on the design and implementation of future observing systems♦ Evaluate possible future instruments without the costs of developing, maintaining & using observing systems.Simulating observational data requires a significant amount of work. However, if we cannot simulate observations, how could we assimilate observations? (Jack Woollen)
7 Full OSSEsThere are many types of simulation experiments. Sometimes, we have to call our OSSE a ‘Full OSSE’ to avoid confusion.A Nature Run (NR, proxy true atmosphere) is produced from a free forecast run using the highest resolution operational model which is significantly different from NWP model used in DAS.For Full OSSE, all major existing observation have to be simulated and observational error have to be calibrated.Calibrations will be performed to provide quantitative data impact assessment.
8 OSSE Calibration● In order to conduct calibration all major existing observation have to be simulated.● The calibration includes adjusting observational error.● If the difference is explained, we will be able to interpret the OSSE results as to real data impact.● The results from calibration experiments provide guidelines for interpreting OSSE results on data impact in the real world.● Without calibration, quantitative evaluation data impact using OSSE could mislead the meteorological community. In this OSSE, calibration was performed and presented.
9 Full OSSE AdvantagesData impact on analysis and forecast will be evaluated.A Full OSSE can provide detailed quantitative evaluations of the configuration of observing systems.A Full OSSE can use an existing operational system and help the development of an operational system.Existing Data assimilation system and vilification method are used for Full OSSEs. This will help development of DAS and verification tools.
10 Why International Joint OSSE capability!! Full OSSEs are expensiveNature Run, entire reference observing system, additional observations must be simulated. Sharing one Nature Run save the costCalibration experiments, perturbation experiments must be assessed according to standard operational practice and using operational metrics and toolsOSSE-based decisions have international stakeholdersDecisions on major space systems have important scientific, technical, financial and political ramificationsCommunity ownership and oversight of OSSE capability is important for maintaining credibilityIndependent but related data assimilation systems allow us to test robustness of answers
11 Nature RunThe Nature Run is a long, uninterrupted forecast by a NWP model whose statistical behavior matches that of the real atmosphere.The ideal Nature Run would be a coupled atmosphere-ocean-cryosphere model with a fully interactive lower boundary. Our real Nature Run is a compromise according to current development and that will limit the OSSE capability.The advantage of using a long, free-running forecast to simulate the Nature Run is that the simulated atmospheric system evolves continuously in a dynamically consistent way. One can extract atmospheric states at any time. Analysis lacks dynamical consistency.It does not matter that the Nature Run diverges from the real atmosphere a few weeks after the simulation begins provided that the climatological statistics of the simulation match those of the real atmosphere.A Nature Run should be a separate universe, ultimately independent from but with very similar characteristics to the real atmosphere.Current choice of Nature Run: Long free forecast run forced by daily SST and ice from analysis
12 Low Resolution Nature Run Two High Resolution Nature Runs New Nature Run by ECMWFProduced by Erik Andersson(ECMWF) Based on discussion with JCSDA, NCEP, GMAO, GLA, SIVO, SWA, NESDIS, ESRL, and ECMWFLow Resolution Nature RunSpectral resolution : T511 , Vertical levels: L91, 3 hourly dumpInitial conditions: 12Z May 1st, , Ends at: 0Z Jun 1,2006Daily SST and ICE: provided by NCEPModel: Version cy31r1Two High Resolution Nature Runs35 days longHurricane season: Starting at 12z September 27,2005,Convective precipitation over US: starting at 12Z April 10, 2006T799 resolution, 91 levels, one hourly dumpGet initial conditions from T511 NRNote: This data must not be used for commercial purposes and re-distribution rights are not given. User lists are maintained by Michiko Masutani and ECMWF.
13 Archive and Distribution To be archived in the MARS system at ECMWFTo access T511 NR, set expver = etwuCopies are available to designated users for research purposes & users known to ECMWFSaved at NCEP, ESRL, and NASA/GSFCComplete data available from portal at NASA/GSFCContacts: Michiko MasutaniHarper Pryor )Gradsdods access is available for T511 NR. The data can be downloaded in grib1, NetCDF, or binary. The data can be retrieved globally or for selected regions.Provide IP number to: Arlindo da Silva
14 Supplemental low resolution regular lat lon data 1deg x 1deg for T511 NRPressure level data: 31 levels,Potential temperature level data: 315,330,350,370,530KSelected surface data for T511 NR: Convective precip, Large scale precip, MSLP,T2m,TD2m, U10,V10, HCC, LCC, MCC, TCC, Sfc Skin TempT511 verification data is posted from NCAR CISL Research Data Archive. Data set ID ds Currently an NCAR account is required for access.(Contact(Also available from NCEP HPSS, ESRL, NCAR/MMM, NRL/MRY, Univ. of Utah, JMA, Mississippi State Univ.)
15 Nature Run validationPurpose is to ensure that pertinent aspects of meteorology are represented adequately in NRContributions from Reale, Terry, SWA, Prive, Masutani, Jusem, R. Tompkins, Jung, Andersson, R Yang, R. Errico and many othersExtratropical cyclones (tracks, cyclogenesis, cyclolosis)Tropical cyclones, tropical waveTransient Eddy kinetic energyMean circulationCloudsPrecipitationArctic boundary layerLow level JetRossby waveInappropriate Nature Runs will fail with calibration of OSSE.Identical twin, Fraternal twin Nature Run, sequence of analysis runs are not expected to pass calibration.
16 Evaluation of the Nature run Utilize Goddard’s cyclone tracking software. - By J. Terry(NASA/GSFC)Comparison between the ECMWF T511 Nature Run against climatology, exp=eskb, cycle=31r1Adrian Tompkins, ECMWFNRTHE SOUTH AMERICAN LOW LEVEL JET Juan Carlos Jusem (NASA/GSFC)MODISNR-MODISTropics by Oreste Reale (NASA/GSFC/GLA)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. Structure even moreimpressive than the system observed in August. Low-level wind speed exceeds 55 m/s.Time series showing the night intensification of the LLJ at the lee of the Andes in the simulation. Gridpoint at 18 S / 63 WM.Masutani (NOAA/NCEP)Seasonal mean zonal mean zonal wind jet maximum strength and latitude of the jet maxima for the ECMWF reanalysis ( , blue circles) and the Nature Run (), northern hemisphere. (N. Prive.)Evaluation of T511(1°) clouds by SWA
17 T511 Nature Run is found to be representative of the real atmosphere and suitable for conducting reliable OSSEs for midlatitude systems and tropical cyclones. (Note: MJO in T511 Nature Run is still weak.)There are significant developments in high resolution forecast models at ECMWF since 2006 and a more realistic tropics for T799 Nature Run is expected with a newer version of the ECMWF model.ECMWF agreed to generate a new T799 NR, when the Joint OSSE team has gained enough experience in OSSEs with T511NR and is ready to make the best use of the high resolution Nature Run.For the time being, the Joint OSSE team will concentrate on OSSEs using the T511 Nature Run.
18 Simulation of Observation For Full OSSE, all major existing observation have to be simulated and observational error have to be calibrated.This is additional work for OSSE to evaluate specific observation. Currently GMAO and NCEP-NESDIS are working on this task.Conventional data (prepbufr) have been simulated by NCEP for entire T511 NR period and ready to release.The first set of AIRS,HIRS2,HIRS3,AMSUA, AMSUB, GOES data have been simulated for entire T511NR period. Found problems. Need to be redone.
19 Flexible Radiance data Simulation strategies at NCEP Experts for data handling and experts of RTM are different people.Content of DWL91Nature Run data at foot print91 level 3-D data (12 Variables)2-D data (71 Variables)Climatological dataAll information to simulate RadiancesDBL91The DBL91 also used for development of RTM.DBL91 can be processed for other sampling such as GMAO samplingDBL91 can be processed for new observationDWL91 with sampling based on GDAS usage will be posted from NASA portal.It is an option whether DBL91 to be saved and exchange among various project, or DBL91 to be treated as temporary file produced in simulation process. This depends on size of DBL91 compare to the Nature Run.
20 Running Simulation program (RTM) Nature Run(grib1 reduced Gaussian)91 level 3-D data (12 Variables)2-D data (71 Variables)Climatological dataObservation templateGeometryLocationMaskNeed complete NR (3.5TB)Random access to grib1 dataNeed Data ExpertsDecoding grib1Horizontal InterpolationDBL91Need large cpuNeed Radiation expertsNeed Data Experts but this will be small programRunning Simulation program (RTM)Post Processing (Add mask for channel, Packing to BUFR)Simulated Radiance Data
21 Simulation of radiance data at NCEP and NESDIS Step 1. Thinning of radiance data based on real useGOES and SBUV are simulated as they are missing from GMAO dataset.Prepbufr is simulated based on CDAS prepqc distribution.DBL91 for AMSUA, AMSUB, GOES, HIRS2, HIRS3, AIRS,MSU are generated at foot print used by NCEP operational analysis and will be posted from NASA portal.Some calibration and validation will be conducted by NCEP and NESDIS. However, users are expected to perform their own calibrations and validationStep 2. Simulation of radiance data using cloudy radianceCloudy radiance is still under development. Accuracy of GMAO data sampling will be between Step1 and Step2.
22 GMAO Observation Simulator for Joint OSSE • Software for generating conventional obs (observation type included in NCEP prepbufr file). Surface data are simulated at NR surface height.• Software for simulating radiances: code to simulate HIRS2/3, AMSUA/B, AIRS, MSU has been set up. Community Radiative Transfer Model (CRTM) is used for forward model. Random sampling-based uses High, Mid, Low level cloud cover, and precipitation to produce a realistic distribution of cloud clear radiance.• Software for generating random error.• Calibration is performed using Adjoint technique.DistributionSimulated observations will be calibrated by GMAO before becoming available.Limited data are now available for people who contribute to validation and calibration.Contact:Ron Errico:
23 Simulation of Observations for calibration All major observational data must be simulated for calibration.GMAO will provide calibrated simulated observations. Currently available to collaborators who participating calibration and validation.NCEP-NESDIS will provide additional data which are not simulated by GMAO.NCEP provide DBL91 to help simulation of radiance by other group.NCEP is planning to produce more radiance data from DBL91. However, users are expected to perform their own validation and calibrations. Selected data are simulated by both GMAO and NCEP-NESDIS to be compared.
24 GOES May 2 00z (12 hr fcst) Simulated Observed GOES10 CH=1 CH=6 GOES12 Real data are plotted for all foot prints. Simulated data are plotted for foot prints used at NCEP GDAS.ObservedSimulatedGOES10CH=1CH=6GOES12CH=11CH=16
25 AMSUA radiance data simulated by sampling based on GDAS usage
26 Simulation of SBUV ozone data Plot produced by by Jack Woollen Jack Woollen (NCEP)RealSimulatedPlot produced by by Jack Woollen
28 Progress in Calibration ESRL and NCEP are working on calibration using data denial method and fits to observation.Using simulated data by GMAO and additional data from NCEP.Focused on July-August 2005.GSI version May 2007.GMAO is conducting calibration using adjoint method.Focused on July August 2006 and December 2005-January 2006.NCEP is working on upgrading OSSE system to newer GSI to accommodate DWL and flow dependent error covariances. Some calibrations will be repeated.
29 RMS (Forecast-Observation), 200hPa wind RealFirst one week is a spin up periodSimulatedGlobalNHGlobalNHSHTropSHTropN. AEuroN. AEuroAsiaAsiaData denial experiment conducted using NCEP GSIYuanfu Xie, Nikki Prive (NOAA/ESRL)Jack Woollen, M. Masutani, Y. Song(NCEP)29
30 RMS (Forecast-Observation), 500hPa Height Real Simulated First one week is a spin up periodGlobalNHGlobalNHSHTropSHTropN. AEuroN. AEuroAsiaAsiaData denial experiment conducted using NCEP GSIYuanfu Xie, Nikki Prive (NOAA/ESRL)Jack Woollen, M. Masutani, Y. Song(NCEP)30
31 Calibration using adjoint technique Calibration for Joint OSSEs at NASA/GMAOVersion 4Version 1REAL OSSEREAL OSSERealMost recentVersion 2OSSEREAL OSSECalibration using adjoint techniqueThis figure shows the mean change in E-norm of the 24-hour forecast error due to assimilating the indicated observation types at 00 UTC for OSSE (top) and real assimilation, or CTL (bottom) for the period of January 2006.Version 1Overall impact of simulated data seems realisticTuning parameter for cloud clearing(courtesy of Ron Errico and R. Yang)
32 SummaryOSSEs are a cost-effective way to optimize investment in future observing systemsOSSE capability should be broadly based (multi-agency)CredibilityCost savingsJoint OSSE collaboration remains only partially funded but appears to be headed in right directionGMAO software to calibrate basic data is ready for releaseAdditional software being developed at NCEP, NESDIS, ESRL and GMAODatabase and computing resources have been set up for DWL simulation and SWA; KNMI receiving ESA funding for DWL simulationsPreliminary versions of some basic datasets have been simulated for entire T511NR period
33 Using Full OSSE, various experiments can be performed and various verification metrics can be tested to evaluate data impact from future instruments and data distributions.It was noted that that while OSSEs can be overly optimistic about the impacts of new observations evaluated in the current data assimilation system, advances in data assimilation skill usually allow us to make better use of observations over time. These advances may, to some extent, be an offsetting factor in that they can help achieve greater impact from new observations in the long run. (From ECMWF Workshop summary)Theoretical predictions have to be confirmed by full OSSEs. The results are often unexpected. OSSE results also require theoretical back ups.OSSE capability should be broadly based (multi-agency) to enhance credibility and to save costs.
35 DWL OSSE using Old T213 Nature Run at NCEP NH V500 Zonal wave number D2+D3: Red: upperDWL + LowerDWLD1: Light Blue closed circle: Hybrid DWL (D1) with scan, rep error 1m/sR45: Cyan dotted line triangle:D1 with rep error 4.5m/s (4.5x4.5≈20)U20: Orange: D1 uniformly thinned for factor 20(Note this is technologically difficult)N4: Violet: D1 Thinned for factor 20 but in forward direction 45,135,225,315 (mimicking GWOS)S10: Green dashed: Scan DWL 10 min on, 90 min off. No other DWLD4 : Dark Blue dashed: non scan DWLHybrid-DWL has much more impact compared to non-scan-DWL with the same amount of data.If the data is thinned uniformly, 20 times thinned data (U20) produces 50%-90% of impact.20 times less weighted 100% data (R45) is generally slightly better than U20 (5% of data).Four lidars directed 90 deg apart (N4) showed significant improvement over D4 only at large scales over SH but is not much better over NH and at synoptic scales.Without additional scan-DWL, 10min on 90 off (S10)sampling is much worse than U20 (5% uniform thinning) with twice as much as data.NH V500Zonal wave number10-20The results will be very different with newer assimilation systems and a higher resolution model.
36 OSSE CalibrationCalibration of OSSEs verifies the simulated data impact by comparing it to real data impact.The data impact of existing instruments has to be compared to their impact in the OSSE.The calibration includes adjusting observational error.If the difference is explained, we will be able to interpret the OSSE results as to real data impact.The results from calibration experiments provide guidelines for interpreting OSSE results on data impact in the real world.Without calibration, quantitative evaluation data impact using OSSE could mislead the meteorological community. In this OSSE, calibration was performed and presented.
38 Simulation of DWL at SWA OSSE to evaluation space based DWLOSSE to evaluate DWLM.Masutani(NCEP), L. P Riishojgaard (JCSDA), Jack Woollen (NCEP)Simulation of DWL at SWAG. David Emmitt, Steve Greco, Sid A. Wood,ADM-Aeolus simulation for J-OSSE G.J. Marseille and Ad Stoffelen (KNMI)
39 ADM-Aeolus simulation for J-OSSE KNMI plan G. J ADM-Aeolus simulation for J-OSSE KNMI plan G.J. Marseille and Ad StoffelenVerification against SWA ADM simulation. Simulation consistency needed forCloudsLaser beam cloud hit from model grid box cloud cover. Random?Cloud backscatter and extinction from model cloudsMaximum overlap between clouds in adjacent (vertical) levelsAerosolsBackscatter and extinctionHorizontal variabilityalong track over 50 km accumulation lengthbetween adjacent observations (separated by 150 km)Vertical variability (stratification)DynamicsWind variability over 50 km accumulation lengthTOGETHERTowards a Global observing system throughcollaborative simulation experimentsSpring 2008: ADM Mission Advisory Group (ADMAG) advises ESA to participate in Joint OSSEKNMI writes TOGETHER proposal to ESAADM OSSE heritage, for details see Stoffelen et al., 2006Tools for retrieving Nature Run fields from ECMWF archiveOrbit simulatorInterpolation of model fields to ADM location− “True” (HLOS) windInstrument error: LIPAS (Lidar Performance Analysis Simulator)For details see Marseille and Stoffelen, 2003LIPAS is updated and compatible with L2B processor performanceRepresentativeness errorUnresolved model scales in nature run and ADM sampling determines representativeness error to be added to ADM HLOS wind observationADM continuous modeESA decision December 2008If continuous mode is selected then more funding will probably become available for additional simulation studiesSimulation of post-ADM scenariosEUMETSAT funding?
40 In Spring, 2008 Simpson Weather Associates, Inc In Spring, 2008 Simpson Weather Associates, Inc. established the Doppler Lidar Simulation Model version 4.2 on an Apple dual quad processor computer for the SensorWeb project. SSH, the network protocol that allows data to be exchanged over a secure channel between two computers, was installed and tested. SWA and SIVO were able to test the push/pull and communications functionality successfully. SIVO was able to push DLSM inputs to SWA and request model simulations. The DLSM was successfully executed and SIVO was able to retrieve DWL coverage and DWL line-of-sight wind products for a six hour simulation in less than 2 minutes.
41 OSSEs to investigate GOES data usage and prepare for GOES-R Tong Zhu (CIRA/CSU), Fuzhong Weng (NOAA/NESDIS), Jack Woollen (NOAA/EMC), Michiko Masutani (NOAA/EMC), Thomas J. Kleespies(NOAA/NESDIS), Yong Han(NOAA/NESDIS), Quanhua, Liu (QSS), Sid Boukabara (NOAA/NESDIS),Steve Load (NOAA/EMC),This project involves an OSSE to evaluate current usage of GOES dataSimulation of GOES-12 SounderObserved GOES-12 SounderObserved GOES bands on 0230 UTC October 01, 2005 for North Atlantic Ocean section.Nature Run hurricane generated on September 27. At 1200 UTC October 1, it is located at about 43 W, 20N. The high moisture air mass associated with the hurricane is shown clearly.by Tong Zhu
42 Evaluation of Unmanned Aircraft System Yuanfu Xie, Nikki Prive, Tom Schlatter, Steve Koch (NOAA/ESRL)Michiko Matsutani, Jack Woollen (NOAA/EMC)UAS consist of the aircraft, communications, and control/support systemsMany different platforms, each having different flight and payload capabilitiesNOAA UAS ProgramFill in existing data gapsImprove forecasting of tropical cyclones and atmospheric river eventsClimate monitoring in the Arctic and AtlanticFisheries monitoring and enforcement
43 Regional OSSEs to Evaluate ATMS and CrIS Observations M. Hill, X. Fan, V. Anantharaj, P. J. Fitzpatrick,M. Masutani, L. P. Riishojgaard, and Y. LiGRI/Mississippi State Univ (MSU), JCSDAThe MM5 RSNR is performed for the period of 00 UTC 02 May to 00 UTC 04 May “2006”, with focus over the U.S. Gulf Coast and the squall line identified from the ECMWF NRs.Two different WRF control runs are performed, using the ECMWF T799 and T511 NR datasets as the initial conditions (IC) and boundary conditions (BC), respectively. In using the T511 NR data, the WRF experiments reflect the realistically imperfect nature of modeling the exact atmosphere, represented here by the MM5 RSNR forced by the T799 NR dataset.Steps and Results of OSSE ProcedureThe MM5 RSNR is performed for the period of 00 UTC 02 May to 00 UTC 04 May “2006”, with focus over the U.S. Gulf Coast and the squall line identified from the ECMWF NRs.Two different WRF control runs are performed, using the ECMWF T799 and T511 NR datasets as the initial conditions (IC) and boundary conditions (BC), respectively. In using the T511 NR data, the WRF experiments reflect the realistically imperfect nature of modeling the exact atmosphere, represented here by the MM5 RSNR forced by the T799 NR dataset.A series of 12-h WRF simulations are conducted throughout the T799 NR period of 12 April to 17 May “2006” for the purpose of calculating background error statistics, based on the Parrish and Derber (1992) method. The background error statistics are used to calibrate the WRF experiments simulated the squall line event.Synthetic upper-air soundings are extracted from the MM5 RSNR and used in a WRF assimilation experiment.Available synthetic ATMS / CrIS orbit data overlying the WRF grid are retrieved for the period of 0730 UTC to 1000 UTC on 02 May.Points of the scan area derived from the synthetic orbit data are thinned to closely match the grid points of the WRF 3-km domain.T and Td data are extracted from the 3-km grid of the MM5 RSNR and matched to the thinned scan area across the WRF 3-km grid, and are assigned satellite retrieval errors of ±1 K and ±2 K, respectively.The T and Td data, representing ATMS / CrIS synthetic observations, are assimilated into the WRF 3DVAR at 0900 UTC 02 May for another WRF experiment.Steps and Results of OSSE ProcedureThe MM5 RSNR is performed for the period of 00 UTC 02 May to 00 UTC 04 May “2006”, with focus over the U.S. Gulf Coast and the squall line identified from the ECMWF NRs.Two different WRF control runs are performed, using the ECMWF T799 and T511 NR datasets as the initial conditions (IC) and boundary conditions (BC), respectively. In using the T511 NR data, the WRF experiments reflect the realistically imperfect nature of modeling the exact atmosphere, represented here by the MM5 RSNR forced by the T799 NR dataset.A series of 12-h WRF simulations are conducted throughout the T799 NR period of 12 April to 17 May “2006” for the purpose of calculating background error statistics, based on the Parrish and Derber (1992) method. The background error statistics are used to calibrate the WRF experiments simulated the squall line event.Synthetic upper-air soundings are extracted from the MM5 RSNR and used in a WRF assimilation experiment.Available synthetic ATMS / CrIS orbit data overlying the WRF grid are retrieved for the period of 0730 UTC to 1000 UTC on 02 May.Points of the scan area derived from the synthetic orbit data are thinned to closely match the grid points of the WRF 3-km domain.T and Td data are extracted from the 3-km grid of the MM5 RSNR and matched to the thinned scan area across the WRF 3-km grid, and are assigned satellite retrieval errors of ±1 K and ±2 K, respectively.The T and Td data, representing ATMS / CrIS synthetic observations, are assimilated into the WRF 3DVAR at 0900 UTC 02 May for another WRF experiment.