Joint OSSE Progress: Simulation & Initial Results

1. Joint OSSE Progress Simulation of observation and initial results. Michiko Masutani August 2010 http://www.emc.ncep.noaa.gov/research/JointOSSEs 1

2. What is OSSE Observing System Simulation Experiments (OSSEs) are typically designed to use data assimilation ideas to investigate the potential impacts of prospective observing systems. In an OSSE, simulated rather than real observations are the input to a data assimilation system (DAS for short). Simulated observational values are drawn from the Nature Run, proxi truth atmosphere for OSSE. The Nature Run is a long, uninterrupted forecast by a NWP model whose statistical behaviour matches that of the real atmosphere Introductory presentation about data assimilation. Jones, Andrew  S. 2008:What is Data Assimilation? A Tutorial, Presentation at AMS data assimilation forum.  AMS annual meeting, New Orleans, LA, January 20-24,2008 http://www.emc.ncep.noaa.gov/research/JointOSSEs/references/Jones_Data_Assimilation_Ed_Forum.ppt

3. Real dataOSE Observing System Experiment Typically aimed at assessing the impact of a given existing data type on a system Using existing observational data and operational analyses, the candidate data are either added to withheld from the forecast system, and the impact is assessed Control run (all operationally used observations) Perturbation run (control plus candidate data) Compare! Data Denial Experiments Simulated data (OSSE) • Observing System Simulation Experiment • Typically aimed at assessing the impact of a hypothetical data type on a forecast system • Simulated atmosphere (“Nature Run”) • Simulated reference observations (corresponding to existing observations) • Simulate perturbation observations (object of study) • Verify simulated observation • Simulate observational error • Control run (all operationally used observations) • Perturbation run (control plus candidate data) • Compare! • Costly in terms of computing and manpower 3

4. Simulation of Existing data + Proposed data (DWL, CrIS, ATMS, UAS, etc) Verification of data Adding Observational error Nature Run Current observing system DATA DUMP OSSE DATA DUMP OSSE Quality Control (Simulated conventional data) Real Radiance data Quality Control (Real conventional data) Simulated Radiance data OSSE DA (Include new obs) DA GDAS OSSE NWP forecast NWP forecast NCEP system

5. DAS,OSE,OSSE Diagram Diagram by Lars Peter Riishojgaard JCSDA

7. Full OSSEs • Data 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 velification method are used for Full OSSEs. This will help development of DAS and verification tools. Masutani, M., T. W. Schlatter, R. M. Errico, A. Stoffelen, E. Andersson, W. Lahoz, J. S.. Woollen, G. D. Emmitt,L.-P. Riishøjgaard, S. J. Lord : Observing System Simulation Experiments. Chapter 24 of Data Assimilation: Making sense of observations, published from Springer in 2010. The final draft of the chapter which describe OSSE is posted at http://www.emc.ncep.noaa.gov/research/JointOSSEs/references/Springer_Data_Assimilation_Ch24_OSSE_Draft.pdf 7

8. Advantages of Full OSSEs • While “Rapid Response OSSEs” or “QuickOSSEs” may provide less expensive yet important and reliable insights to potential data impacts from proposed new observing systems, full OSSEs offer a more realistic representation of those data impacts on analyses and forecasts. • A Full OSSE can use the full extent of an existing operational forecast system and provide input to the preparation of that system for the ingestion of the new data set(s) in an operational setting (e.g. ADM). 8

9. Why an International Joint OSSE capability Full OSSEs are expensive Nature Run, entire reference observing system, additional observations must be simulated. Sharing one Nature Run saves $$. Calibration experiments, perturbation experiments must be assessed according to standard operational practices and using operational metrics and tools OSSE-based decisions have international stakeholders Decisions on major space systems have important scientific, technical, financial and political ramifications Community ownership and oversight of OSSE capability is important for maintaining credibility Independent but related data assimilation systems allow us to test robustness of answers 9

10. OSSE Calibration ● In order to conduct calibration all major existing observations must be simulated. ●The calibration includes adjusting observational error. ●If the difference can be 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 is performed and presented. Simulation of control data for calibration ● Simulation of control data require significant funding for specific OSSEs ● GMAO and NCEP are simulating control data

11. Experience from the past In 1993, ECMWF produced Nature Run in T213. Observations are simulated while the NR was produced The data were distributed with significant cost Problems Radiance data was not simulated correctly and cannot be used. SST was kept constant and that affect the OSSE results. Took long time to find fund to conduct OSSEs OSSE using ECMWF T213 Nature Run data Stoffelen, A., G. J. Marseille, F. Bouttier, D. Vasiljevic, S. De Haan And C. Cardinali  2006:ADM-Aeolus Doppler wind lidar Observing System Simulation Experiment,  Quar.J.Roy. Metorol. Soc. , 619, 1927-1948 Focused on NH due to the lack of radiance data and showed DWL impact over ocean. NOAA simulated observation including radiance data and conduct OSSEs to evaluation DWL. Masutani, M., J. S. Woollen, S. J. Lord, G. D. Emmitt, T. J. Kleespies, S. A. Wood, S. Greco, H. Sun, J. Terry, V. Kapoor, R. Treadon, and K. A. Campana, 2009. Observing System Simulation Experiments at the National Centers for Environmental Prediction. J. Geophys. Res.,114, doi:10.1029/2009JD012528. Results were mainly presented over NH, which has less affected by constant SST. (To be published from JGR)

12. New Nature Run by ECMWF Produced by Erik Andersson(ECMWF) Based on discussion with JCSDA, NCEP, GMAO, GLA, SIVO, SWA, NESDIS, ESRL, and ECMWF 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 Two High Resolution Nature Runs 35 days long Hurricane season: Starting at 12z September 27,2005, Convective precipitation over US: starting at 12Z April 10, 2006 T799 resolution, 91 levels, one hourly dump Get initial conditions from T511 NR Note: 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 ECMWF To access T511 NR, set expver = etwu • Copies are available to designated users for research purposes & users known to ECMWF • Saved at NCEP, ESRL, and NASA/GSFC • Complete data available from portal at NASA/NCCS portal • http://portal.nccs.nasa.gov/osse • Contacts: Michiko Masutani (michiko.masutani@noaa.gov), and • Password protected . Accounts arei arranged by Ellen Salmon (Ellen.M.Salmon@NASA.gov) • 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 (Arlindo.Dasilva@nasa.gov) 13

14. Supplemental low resolution regular lat lon data 1deg x 1deg for T511 NR Pressure level data:31 levels, Potential temperature level data: 315,330,350,370,530K Selected surface data for T511 NR: Convective precip, Large scale precip, MSLP,T2m,TD2m, U10,V10, HCC, LCC, MCC, TCC, Sfc Skin Temp T511 verification data is posted from NCAR CISL Research Data Archive. Data set ID ds621.0. Currently an NCAR account is required for access. (Also available from NCEP HPSS, ESRL, NCAR, NRL/MRY, Univ. of Utah, JMA, Mississippi State Univ., JPL and Environment of Canada as well as NASA/NCCS portal) 14

15. [Usage and credit] This data must not be used for commercial purposes and re-distribution rights are not given. ECMWF and Joint OSSEs must be given credit in any publications in which this data is used. If you are interested in using the data set it is necessary to send E-mail with the statement below. Please send following information to Michiko Masutani (michiko.masutani@noaa.gov). your name, Email address, Affiliation, Title of the project (or how NR will be used) Then your name will be added to the user list and sent to ECMWF. The usage permission will be given to individual not to the institute. Therefore, everyone has to submit above information. "I agree not to copy the ECMWF data for the use of other persons, and I agree not to use these data and/or software for commercial purposes. ECMWF will be given credit in any publications in which these data and/or software are used. I understand that if other persons in my organization wish to use these data and/or software, they must also sign a copy of this agreement."

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 20050601-20060531, exp=eskb, cycle=31r1 Adrian Tompkins, ECMWF NR THE SOUTH AMERICAN LOW LEVEL JET Juan Carlos Jusem (NASA/GSFC) MODIS NR-MODIS Tropics by Oreste Reale (NASA/GSFC/GLA) Time series showing the night intensification of the LLJ at the lee of the Andes in the simulation.Gridpoint at 18 S / 63 W 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 more impressive than the system observed in August. Low-level wind speed exceeds 55 m/s. M.Masutani (NOAA/NCEP) Seasonal mean zonal mean zonal wind jet maximum strength and latitude of the jet maxima for the ECMWF reanalysis (1989-2001, blue circles) and the Nature Run (), northern hemisphere. (N. Prive.) Evaluation of T511(1°) cloudsby SWA 16

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. 17

18. Andersson, Erik and Michiko Masutani 2010: Collaboration on Observing System Simulation Experiments (Joint OSSE), ECMWF News Letter No. 123, Spring 2010, 14-16. http://www.ecmwf.int/publications/newsletters/pdf/123.pdf Reale O., J. Terry, M. Masutani, E. Andersson, L. P. Riishojgaard, J. C. Jusem (2007), Preliminary evaluation of the European Centre for Medium-Range Weather Forecasts' (ECMWF) Nature Run over the tropical Atlantic and African monsoon region, Geophys. Res. Lett., 34, L22810, doi:10.1029/2007GL031640. http://www.agu.org/pubs/crossref/2007.../2007GL031640.shtml

19. Simulated of radiance Michiko Masutani and Jack Woollen NOAA/NWS/NCEP/EMC Tong Zhu, Haibing Sun, Tom Kleespies, Yong Han, Fuzhong Weng NOAA/NESDIS Lars Peter Riishojgaard JCSDA For full OSSE, existing observation has to be simulated. This is an initial investment. Flexile Radiance data Simulation strategies at NCEP

20. Flexible Radiance data Simulation strategies at NCEP Experts for data handling and experts of RTM are different people. • Content of DBL91 • Nature Run data at foot print • 91 level 3-D data (12 Variables) • 2-D data (71 Variables) • Climatological data • All information to simulate Radiances DBL91 The DBL91 also used for development of RTM. DBL91 can be processed for other sampling such as GMAO sampling DBL91 can be processed for new observation 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

21. Nature Run (grib1 reduced Gaussian) 91 level 3-D data (12 Variables) 2-D data (71 Variables) Climatological data Observation template Geometry Location Mask Need complete NR (3.5TB) Random access to grib1 data Need Data Experts Decoding grib1 Horizontal Interpolation DBL91 Need large cpu Need Radiation experts Need Data Experts but this will be small program Running Simulation program (RTM) Post Processing (Add mask for channel, Packing to BUFR) Simulated Radiance Data 21

22. Compare Tb(NR) with Tb(OBS)AMSU-A 20050502 00Z Observation Old NCEP.v2 simulation

23. NOAA-17 HIRS3 Ch-07 CRTM Simulation Observation

24. IASI simulation Evolution at Windows channel • IASI simulation for 00Z May 2nd 2005 • Using template based on usage on 00Z may 2nd 2009 • Compared with observation at 00Z May 2nd 2005

25. IASI simulation over ocean( Clear atmosphere)

26. NCEP-NESDIS data posted in December 2009http://portal.nccs.nasa.gov/josse Dataset Originating Institute Contact NCEP Obs NOAA/NCEP Michiko Masutani (Michiko.Masutani@noaa.gov) NCEP-NESDIS NOAA/NCEP Michiko Masutani (Michiko.Masutani@noaa.gov) NOAA/NESDIS Subdirectories under each data set NCEP-NESDIS osbuvb.n_t511.v0906 prepbufr.n_t511.v0903 thinsats.n_t511.dbl91.v0909 NCEP_Obs thinsats_rad.gdas.mask.v0909 thinsats_rad.gdas.v0905

27. [Interpolation program by Jack Woollen] Horizontal interpolation code by Jack Woollen is posted at http://www.emc.ncep.noaa.gov/research/JointOSSEs/Manuals/NCEP_SimObs/JW-mkthinrad/  The directory contains software by Jack to do sampling and  horizontalinterpolation.  He is using random access to grib data and that made the program verysimple.   Please contact Jack Woollen Jack.Woollen@noaa.gov for the detail. [Libraries used in the programs] grib decorder from ECMWF can be downloaded from http://www.ecmwf.int/products/data/software/download/gribex.html Manual of gribx is posted from http://www.ecmwf.int/publications/manuals/libraries/gribex/subroutineGribex.html If you are interested in more recent version of ECMWF gribdecorder grib API is available from http://www.ecmwf.int/publications/manuals/grib_api/ w3lib and bacio lib are available from http://www.nco.ncep.noaa.gov/pmb/codes/GRIB2/

28. Preliminary simulated radiances are being updated  at ftp://ftp.emc.ncep.noaa.gov/exper/mmasutani/SIMOBS/NOAA_RAD/ The data need to be evaluated Plans The simulated radiance using CRTM1.2.2, at foot print based on usage by 2005 GDAS is reasonable. The simulated is continued to complete entire period of T511 Nature run. Observational error will be added based on method developed by T. J. Kleespies. GMAO also provided softwarre to add random error. Calibration will be performed for observational errors At NOAA currently OSSE experiments are conducted for July 2005 using data provided by GMAO. Preliminary calibrated data are produced for this period.

29. Remarks Simple description is provided but these are posted to help people who are interested in simulating observation and not design to be potable. Please contact Jack.Woollen@noaa.gov and Michiko.masutani@noaa.govfor the detail.We appreciate any comments and improvement to the code. Simulation of radiance is done using CRTM REL-1.2.2 but CRTM REL-2.0.2 is available from ftp://ftp.emc.ncep.noaa.gov/jcsda/CRTM/ We appreciate if anyone can upgrade these code to REL-2.0.2 and share with Joint OSSE. NCEP will post simulated observation as progress. First we post from NCEP ftp site to be evaluated. After the data is evaluated it will be transferred to NASA NCCS portal. We appreciate any help in evaluation of the simulated observations. GMAO is also working on simulation of radiance. Lars Peter Riishojgaard is the contact person for GMAO OSSE. Please contact him if you have any questions regarding GMAO OSSEs. We hope all efforts may be merged in some stage.

30. Simulated TC vital [Simulation of TC vital] TC vital was simulated using software originally written by Tim Marchock and currently developed by Guan Ping Lou of NCEP. The simulated observation has not been evaluated. TC-vital for 13 month http://www.emc.ncep.noaa.gov/research/JointOSSEs/Manuals/NCEP_SimObs/TC/ The software are posted from http://www.emc.ncep.noaa.gov/research/JointOSSEs/Manuals/NCEP_SimObs/TC/Software/ The simulated data is also posted from ftp://ftp.emc.ncep.noaa.gov/exper/mmasutani/SIMOBS/TC-vital/T511-v3

31. Testing Line-Of Sight (LOS) analysis code FY10 GDAS implemented on December 15, 2009 Jack Woollen, Michiko Masutani, Lars Peter Riishojgaard, Zaizhong Ma Acknowledgement Dave Emmitt, Sid Wood, Steve Greco, Daryl Kleist, John Derber

32. Back ground Single Obs test using (U,V) and two LOS are suggested by Dave Emmitt to test Early test was conducted in 2006 by Jack Woollen, Yuanfu Xie, Joe Terry, Yucheng Song, Genia Brin and M. Masutani but the results was not conclusive and the summary was not produced. Assimilation code for DWL was originally written by John Derber in 1997. Further development added by Weiyu Yang and other people at NCEP. Single obs test Observation exactly same as the Nature Run Date: July 3rd 6z ,2005 Position: 96W 29S 200mb height 11991m Wind: U=46.00 V=19.18

33. Shading: anal-ges U and V observation as pibal Position: 96W 29S 200mb height 11991m, Wind:U=46.00 V=19.18 Representativeness Error =3.0 V200 U200 Input as two LOS DATA(ExactNR) (96W 29S) Pressure level=200 HGT=11991 Representativeness Error =3.0 LOS 34.51 BEARAZ=90 ELEV= 41.38deg, and LOS 14.39 BEARAZ=0 ELEV=41.38deg U200 V200

34. Observing Systems Simulation Experiments For Global Wind Observing Sounder Global Wind Observing Sounder (GWOS) 34

35. Dual Technology Sampling The coherent subsystem provides very accurate (< 1.5m/s) observations when sufficient aerosols (and clouds) exist. The direct detection (molecular) subsystem provides observations meeting the threshold requirements above 2km, clouds permitting. When both sample the same volume, the most accurate observation is chosen for assimilation. The combination of direct and coherent detection yields higher data utility than either system alone. Note that in the background aerosol mode, the combination of the coherent and direct provide ~ 20 % more coverage near 3 -5 km than could either technology by itself.

36. GWOS Sampling Hybrid Doppler Wind LidarMeasurement Geometry: 400 km 350 km/217 mi 53 sec Along-Track Repeat “Horiz. Resolution” 586 km/363 mi 36

37. First look of Impact of GWOS with Hybrid DWL Very Initial results Preliminary initial condition and control data Reduction of RMSE from NR compared to CTL Average between 00 ZJuly 3 -18Z July 3 Red: Positive impact Blue: negative impact V200 U200

38. U500 V500 U850 V850

39. Reduction of RMSE from the Nature run by GWOS Averaged between July 3rd 00Z and July 3rd 18z Area averaged between 60S to 60N U V

40. First look for three day experiments for three days. Hybrid GWOS showed positive strong impact in analysis The positive analysis impact is centered over tropics and spread to mid latitude. Negative impact over both poles. Further investigation to improve LOS data assimilation code may increase the impact of DWL data. Simulated observation require more realistic observational error. Further calibration is required to gain the confidence in results. These results do not guarantee positive impact GWOS DWL in forecast. Longer time experiments are required for forecast impact.

41. Further work The experiments are done using preliminary initial condition and simulated observation. Need to be repeated with clean initial condition and control data. Complete longer time period (minimum 6 week) to study forecast verification. The experiments for different season particularly NH winter More metric reduction of failed forecast (See slides 41-43) Cyclone track forecasts. Investigate the cause of the negative impact. Test using flow dependent error covariance being developed at NCEP.

42. Evolution of GFS Forecast Skill S. Lord and Fanglin Yang 5/20/2010

43. 500 hPa Anomaly Correlation • “But these are just ‘width of the line’ improvements…” • Why are these changes important to users? • How can you justify $M for new computing power based on these anticipated improvements?

44. Does It Make a Difference toHow Forecasters Use Product? Percent Poor Forecasts Percent Good Forecasts NCEP NCEP

45. Useful References

46. Basic guide lines for Full OSSE • Masutani, M., T. W. Schlatter, R. M. Errico, A. Stoffelen, E. Andersson, W. Lahoz, J. S. Woollen, G. D. Emmitt,L.-P. Riishøjgaard, S. J. Lord : Observing System Simulation Experiments. The final draft of the chapter which describe OSSE is posted at • http://www.emc.ncep.noaa.gov/research/JointOSSEs/references/Springer_Data_Assimilation_Ch24_OSSE_Draft.pdf • This is Chapter 24 of Data assimilation:Making Sense of Observation • Lahoz, William, Khattatov, Boris, Menard, Richard (Eds.) 2010: Data Assimilation, Making Sense of Observation.. Springer, 732 p., Hardcover ISBN: 978-3-540-74702-4 • http://www.springer.com/earth+sciences+and+geography/computer+%26+mathematical+applications/book/978-3-540-74702-4 Introductory presentation about data assimilation. Jones, Andrew  S. 2008:What is Data Assimilation? A Tutorial, Presentation at AMS data assimilation forum.  AMS annual meeting, New Orleans, LA, January 20-24,2008 http://www.emc.ncep.noaa.gov/research/JointOSSEs/references/Jones_Data_Assimilation_Ed_Forum.ppt

47. Publication about Joint OSSE Nature Run Andersson, Erik and Michiko Masutani 2010: Collaboration on Observing System Simulation Experiments (Joint OSSE), ECMWF News Letter No. 123, Spring 2010, 14-16. http://www.ecmwf.int/publications/newsletters/pdf/123.pdf Reale O., J. Terry, M. Masutani, E. Andersson, L. P. Riishojgaard, J. C. Jusem (2007), Preliminary evaluation of the European Centre for Medium-Range Weather Forecasts' (ECMWF) Nature Run over the tropical Atlantic and African monsoon region, Geophys. Res. Lett., 34, L22810, doi:10.1029/2007GL031640. http://www.agu.org/pubs/crossref/2007.../2007GL031640.shtml

48. Publication for OSSEs with T213 Nature Run Masutani, M, John S. Woollen, Stephen J. Lord, G. David Emmitt, Thomas J. Kleespies, Sidney A. Wood, Steven Greco, Haibing Sun, Joseph Terry, Vaishali Kapoor, Russ Treadon, Kenneth A. Campana (2010), Observing system simulation experiments at the National Centers for Environmental Prediction, J. Geophys. Res., 115, D07101, doi:10.1029/2009JD012528. http://www.agu.org/pubs/crossref/2010/2009JD012528.shtml Masutani, M. K. Campana, S. Lord, and S.-K. Yang 1999: Note on Cloud Cover of the ECMWF nature run used for OSSE/NPOESS project. NCEP Office Note No.427 http://wwwt.ncep.noaa.gov/officenotes/NOAA-NPM-NCEPON-0006/01408B76.pdf Becker, B. D., H. Roquet, and A. Stoffelen 1996: A simulated future atmospheric observation database including ATOVS, ASCAT, and DWL. BAMS, 77, 2279-2294. http://ams.allenpress.com/archive/1520-0477/77/10/pdf/i1520-0477-77-10-2279.pdf Errico, R.M., R. Yang, M. Masutani, M., and J. Woollen, 2007: Estimation of some characteristics of analysis error inferred from an observation system simulation experiment. Meteorologische Zeitschrift, 16, 695-708. http://www.emc.ncep.noaa.gov/research/JointOSSEs/references/Errico_GB_071005.ps Stoffelen, A., G. J. Marseille, F. Bouttier, D. Vasiljevic, S. De Haan And C. Cardinali 2006:ADM-Aeolus Doppler wind lidar Observing System Simulation Experiment, Quar.J.Roy. Metorol. Soc. , 619, 1927-1948

49. Other references related to OSSE and simulation experiments for DWL Arnold, C. P., Jr. and C. H. Dey, 1986: Observing-systems simulation experiments: Past, present, and future. Bull. Amer., Meteor. Soc.,67, 687-695. Atlas, R. 1997: Atmospheric observations and experiments to assess their usefulness in data assimilation.  Journal of the Meteorological Society of Japan, 75, 111-130. http://www.emc.ncep.noaa.gov/research/JointOSSEs/references/Beesley_ECMWF_SHEBA_Arctic_2000JD900079.pdf Halem, H. and R. Dlouhy, 1984: Observing system simulation experiments related to space-borne lidar wind profiling. Part 1: Forecast impacts of highly idealized observing systems. Preprints, Conference on Satellite Meteorology/ remote Sensing and Applications, Clearwater, Fla., Americal Meteorological Society, 272-279. Kalnay, E, Jusem, J C and J Pfaendtner, 1985. The relative importance of mass and wind data in the FGGE observing system. Proceedings of the NASA Symposium on Global Wind Measurements, Clumbia, MD, NASA, 1-5. Lorenc,A.C., Graham,R.J., Dharssi,I., MacPherson,B., Ingleby,N.B., Lunnon,R.W.,1992, Preparation for the use of a Doppler wind lidar information in meteorological assimilation systems, ESA-CR(P)-3454 http://www.emc.ncep.noaa.gov/research/osse/NR/references/Lorenc.1992.TIDCCR4129.pdf Lord, S.J., E. Kalnay, R. Daley, G.D. Emmitt, R. Atlas, 1997: Using OSSEs in thedesign of future generation integrated observing systems.  Preprints, 1st Symposium on Integrated Observing Systems, Long Beach, CA, AMS, 45-47. http://www.emc.ncep.noaa.gov/research/osse/NR/references/Lord_ams97.html Marseille, G.J., Stoffelen, A., Barkmeijer J. , 2008a, Sensitivity Observing System Experiment (SOSE) - A New Effective NWP-based Tool in Designing the Global Observing System, Tellus, 60A, 216–233. Marseille, G.J., Stoffelen, A., Barkmeijer J. , 2008b, Impact Assessment of Prospective Space-Borne Doppler Wind Lidar Observation Scenarios, Tellus, 60A, 234-248. Marseille, G.J., Stoffelen, A., Barkmeijer J. , 2008c, A Cycled Sensitivity Observing System Experiment on Simulated Doppler Wind Lidar Data during the 1999 Christmas Storm "Martin" , Tellus, 60A, 249-260. Tan, D.G.H., E. Andersson, M. Fisher and L. Isaksen 2007: Observing system impact assessment using a data assimilation ensemble technique: Application to the ADM-Aeolus wind profiling mission .  Q.J.Roy.Met.Soc, 133, 381-390.