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Coordination of the work. Planned OSSEs and OSSE interest, In Joint OSSEs

Coordination of the work. Planned OSSEs and OSSE interest, In Joint OSSEs. December 2009. http://www.emc.ncep.noaa.gov/research/JointOSSEs. Advantage of collaboration was well demonstrated in evaluation of the Nature Run. Evaluation of the Nature run.

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Coordination of the work. Planned OSSEs and OSSE interest, In Joint OSSEs

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  1. Coordination of the work. Planned OSSEs and OSSE interest, In Joint OSSEs December 2009 http://www.emc.ncep.noaa.gov/research/JointOSSEs

  2. Advantage of collaboration was well demonstrated in evaluation of the Nature Run

  3. 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

  4. 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high resolution forecast models at ECMWF since 2006 and a more realistic tropics for T799 Nature Run is expected with a newer version of the ECMWF model.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.

  5. 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.

  6. 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

  7. Possible template for radiance data Sampling for template for Calibration A. Foot print used by NCEP GDAS B A with mask for channel Let DAS to select channel C Simple thinning 100km etc and simulate cloudy radiance Let DAS to select foot print. D. Sampling based on nature run cloud GMAO can provide template file. save undef or 0 for unused channel does not have to run CRTM • Template for OSSE experiment • A. Instrument designer have to simulate foot print location using orbit generators • Sampling based on Nature Run has to be designed • This is major work to be performed by instrument designers

  8. Simulation of HIRS3 radiance from NOAA16 CRTM 1.2.2 posted from JCSDA web site was used for simulation DBL 91 was generated at foot print used by NCEP GDAS All information in GDAS bufr files are copied to simulated radiance file. Channel which are not used by GDAS was marked in diag file. Masked out to generate masked radiance data.

  9. Template data Radiance data are not used for simulation. Long wave

  10. Water Vapor

  11. Short wave

  12. Further Considerations in simulation of observation • Data distribution depends on atmospheric conditions. • Aircraft data are heavily affected by Jet Stream location. Location of Jet in NR must be considered. • Scale of RAOB drift becomes larger than model resolution. • Cloud Motion Vector is based on Nature Run Cloud. • Microwave Radiative Transfer at the Sub-Field-of-View Resolution (Tom Kleespies and George Gayno) • The ability to integrate high resolution databases within a given field-of-view, and perform multiple radiative transfers within the field of view, weight according to the antenna beam power, and integrate

  13. Planned OSSEs • Future GPSRO constellation configuration and impact • Lidia Cucurull (JCSDA,NOAA/NESDIS), NCAR, CWB, NPSO • Wind Lidar (GWOS) impact and configuration experiments for NASA • M.Masutani(NCEP), L. P Riishojgaard (JCSDA) • Simulation of DWL planned from NASA and selected DWL from ESA • G. David Emmitt, Steve Greco, Sid A. Wood,(SWA) • GOES-R preparation experiments (NOAA/NESDIS) • Tong Zhu,, Fuzhong Weng, T.J. Kleespies, Yong Han, Q. Liu, • Sid Boukabara(NOAA/NESDIS), Michiko Masutani (NOAA/EMC), • Jack Woollen(NOAA/EMC),L. P Riishojgaard (JCSDA) • OSSE to evaluate data assimilation systems • Ron Errico, Runhua Yang (GMAO) • T

  14. Planned OSSEs (Cont.) • Simulation of ADM-Aeolus and follow up mission • G.J. Marseille and Ad Stoffelen (KNMI • Regional OSSE to evaluation DWL on TC forecasts • Zhaoxia Pu, University of Utah • Evaluation of Unmanned Aircraft System • Yuanfu 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) • PCW and PREMIRE • Richard Menard, Louis Garand, Yves Rochen (Environment of Canada) • PCW (The Polar Communications and Weather) OSSE • Lars Peter Riishojgaard (JCSDA), Mike Kalb (NESDIS)

  15. Other OSSEs (Cont.) • Evaluation and development of targeted observation • Z. Toth, Yucheng Song (NCEP) and other THORPEX team members • Assimilation with LETKF possibly by 4D-var • T. Miyoshi(UMD) and Enomoto(JEMSTEC) • Data assimilation for climate forecasts • H. Koyama, M. Watanabe (University of Tokyo) • Analysis with surface pressure • Gil Compo, P. D. Sardeshmukh (ESRL) • Data assimilation with RTTOVS • Yves Rochon (Environment Canada) • Sensor Web Uses same Nature Run • NASA/GSFC/SIVO, SWA , NGC

  16. Other OSSEs (Cont.) • Geostationary microwave simulations • University of Colorado, Boulder • Albin Gasiewski, Srikumar Sandeep • JPL interest • Bjorn H Lambrigtsen • Research related to Simulation of GoesStar • Joao Teixeira     Subgrid scale structures of land in radiance data Kevin Bowman     Working on Tropospheric emission spectrometer         CLARREO Amy Braverman     Interested in NR to test retrieval • Hirricane OSSE for GPSRO • Purdue University • Jeniffer Haase

  17. OSSE at Environment Canada Yves J. Rochon The NR and resulting simulated observations will be used in (1) contributing to the assessment of the potential benefit of different observation sources (e.g. stratospheric wind observations) and (2) evaluating the performance impact of changes in aspects related to methodology. Locally simulated observation sets will be created using an existing capability in addition to any Joint OSSE observation sets which may be used. The locally simulated observation sets could possibly be made available if there is interest. University of Coloerado, Boulder Albin GasiewskiGeostationary microwave simulationsI 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.

  18. Summary • OSSEs are a cost-effective way to optimize investment in future observing systems • OSSE capability should be broadly based (multi-agency) • Credibility • Cost savings • Timing All OSSEs are funded unrialistic time scale. People are forced to do shortcut. Simulation of basic data will not be funded for OSSEs. If we do not assist other OSSEs they will produce damaging results

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