Coordination in Simulation of Observation and Calibration January 2009

1. Coordination in Simulation of Observation and Calibration January 2009

3. New Nature Run by 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

4. Archive and Distribution To be archived in the MARS system at ECMWF Currently available internally as expver=etwu • Copies for US are available to designated users for research purpose& users known to ECMWF • Saved at NCEP, ESRL, and NASA/GSFC • Complete data available from portal at NASA/GSFC • Conctact:Michiko Masutani (michiko.masutani@noaa.gov), • Harper Pryor (Harper.Pryor@nasa.gov ) • Gradsdods access is available for T511 NR. The data can be down loaded  in grib1, NetCDF, binary. The data can be retrieved globally or selected region. • Provide IP number to :Arlindo da Silva (Arlindo.Dasilva@nasa.gov)

5. Supplemental low resolution regular lat lon data 1degx1deg for T511 NR, 0.5degx0.5deg for T799 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 Complete surface data for T799 NR T511 verification data is posted from NCAR CISL Research Data Archive. Data set ID ds621.0. Currently NCAR account is required for access. T799 verification data are available from NASA/GSFC portal (Contact Harper.Pryor@nasa.gov) (Also available from NCEP hpss, ESRL, NCAR/MMM, NRL/MRY, Univ. of Utah, JMA,Mississippi State Univ.)

6. Evaluation of the T511 Nature run Realistic Tropics Oreste Reale (NASA/GSFC/GLA) Improved cloud 20050601-20060531, exp=eskb, cycle=31r1 Adrian Tompkins, ECMWF NR MODIS NR-MODIS JJA SON 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.)   DJF MAM  

7. Simulation of Observation OBS91L Nature Run Model level profiles for simulating radiance obs NCEP, NESDIS For development purposes, 91-level mode level variables are processed at NCEP and interpolated to observational locations with all the information need to simulate radiance data (OBS91L). The OBS91L are also available for development of a Radiative Transfer Model (RTM) for development of other forward model. Conventional data, AMSUA, AMSUB, GOES data has been simulated for entire T511 NR period. Initial data will have no error added and quality control is not necessary. Considerations Data distribution depends on atmospheric conditions Cloud and Jet location, Surface orography, RAOB drift

8. GMAO Observation Simulator for Joint OSSE Cloud Motion Vectors SWA, GMAO and NCEP - Advised by Chris Velden - • Software for generating conventional obs (Observation type included in NCEP .prepbufr file) The codes are set up for raobs, aircraft, ships, vad winds, wind profilers, surface station data, SSMI and Quick scat surface winds, Cloud Motion Vector (CMV) • 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. • Software for generating random error. Observations are generated without errors but software to simulate error is provided. Initially CMV was simulated at locations of real observation. SWA studied the strategies of simulation of CMV Distribution The output of the data is saved in BUFR format which can be read by the Gridpoint Statistical Interpolation (GSI). GSI is a DAS used at NCEP, GMAO and ESRL. The codes are flexible and include many tunable parameters. Contact: GMAO (Ronald Errico: ronald.m.errico@nasa.gov) Joint OSSE (Michiko Masutani: michiko.masutani@noaa.gov).

9. Radiance Simulation System for Joint OSSERon Errico, Runhua Yang, Emily Liu, Meta Sienkiewicz,(NASA/GSFC/GMAO)Tong Zhu, Tom Kleespies,Haibing Sun, Fuzhong Weng, (NOAA/NESDIS) Jack Woollen, Michiko Masutani(NOAA/NCEP)Lars Peter Riishojgaard (JCSDA) Other possible resources and/or advisors David Groff , Paul Van Delst (NCEP) Yong Han,Walter Wolf, Cris Bernet, Mark Liu, M.-J. Kim, (NESDIS), Erik Andersson (ECMWF); Roger Saunders (Met Office) Initially, CRTM is used for simulation and assimilation. Alternative software to simulate radiance data using the Stand-alone AIRS Radiative Transfer Algorithm (SARTA) as well as the CRTM is also being developed at NESDIS. NESDIS software includes results from various research. This will be important to evaluate CRTM in Joint OSSEs. CRTM: Community Radiative Transfer Model Algorithm for determining cloud-cleared observation locations used at GMAO For each grid box where a satellite observation is given, use the cloud fraction to specify probability that it is a clear spot. Then use random number to specify whether pixel is clear. Use a functional relationship between probability and cloud fraction that we can tune to get a reasonable distribution. The GMAO simulation software was successfully installed at NCEP and initial simulation AIRS, HIRS2 and HIRS3 AMSU and GOES data has been simulated using OBS91L Further development of CRTM to use cloudy radiance

10. Calibration for Joint OSSEs Calibration using the adjoint technique has been conducted at GMAO Calibration using data denial experiments at ESRL, NCEP, and NESDIS (Nikki Prive: IOAS-AOLS 13.3) Discussion forum for observational errors Extensive discussion on simulation of observational error particularly representativeness error. To be published in “Data assimilation: Making sense of observation” (Springer) Presentation about Joint OSSEs at IOAS-AOLS 13.2 Thursday 1:45 Presentation about Joint OSSEs at IOAS-AOLS 13.2 Thursday 1:45 Presentation about Joint OSSEs at IOAS-AOLS 13.2 Thursday 1:45

11. Calibration for Joint OSSEs at NASA/GMAO Latest try Try 1 REAL OSSE Try 2 REAL OSSE Calibration using adjoint technique Try 1 Continue working on tuning parameter for cloud clearing Investigate problem in surface emissivity Improving simulation of Cloud motion vector. (Need to work with SWA)

12. Further plan at NOAA Uniform Observation Fibonacci Grid used in the uniform data coverage OSSE RAOB type data with 200km apart with 91 model levels Used to make initial condition. Further use to study observational impact test. Common data format to communicate with GMAO simulator - Fig by Yucheng Song OBS91L for the future observing system Orbit simulator to produce geometry of the satellite observation. Produce OBS91L with geometry, complete surface data, Complete vertical profiles in model levels. CRTM is making final adjustment for CrIS Cloudy radiance in CRTM is being improved.

13. ADM-Aeolus simulation for J-OSSE KNMI planG.J. Marseille and Ad Stoffelen TOGETHER Towards a Global observing system through collaborative simulation experiments • Spring 2008: ADM Mission Advisory Group (ADMAG) advices ESA to participate in Joint OSSE • KNMI writes TOGETHER proposal to ESA • Tools for retrieving nature run fields from ECMWF archive • Orbit simulator • Instrument error: LIPAS (Lidar Performance Analysis Simulator) • Representativeness error • Verification against SWA ADM simulation. Simulation consistency

14. Simulation of DWL at SWA Sid A. Wood, G. David Emmitt, Steve Greco Progress Sample data has been produced from T511 Nature run Complete T511 Nature run in model resolution has been transferred. Computing facilities has been set up Doppler Lidar Simulation Model, Version 4.2 Online Web-Based User’s Guide available Sid A. Wood, G. David Emmitt, L. S.Wood Steve Greco More details in Fourth Symposium on Lidar Atmospheric Applications P1.12

15. Simulation of DWL at NASA/GSFC Arlindo . Da Silva, Matthew. J. McGill, Michele Rienecker, Lars Peter Riishojgaard Focused on DWL developed at NASA/GSFC Simulation of aerosol for Joint OSSE Nature Run. The simulated aerosol will be available to Joint OSSEs Assimilation of DWL and testing the impact NCEP conducted DWL impact test using SSI. GSI is getting ready for simulation of lidar and being tested and compare with SSI. Evaluation of DWL simulated by SWA and KNMI at NCEP and NASA/GSFC

16. Regional DWL OSSEs at the University of Utah Zhaoxia Pu, University of Utah ( Zhaoxia.Pu@utah.edu) • Use WRF model and  NCEP GSI for data assimilation • Evaluate the global natural runs for regional OSSEs • Assess the impact of future DWL data on high-impact weather forecasting; focused on the hurricane intensity forecast. • Investigate the basic problems/challenges, such as boundary conditions and resolution issues  in regional OSSEs Conduct simple regional OSSE. Tested regional Nature Run Serious Lateral boundary problem Presentation at OSSE meeting on 1/30

17. OSSE capability for GNSS Radio-Occultation (RO) observationsLidia Cucurull (JCSDA) • There are several options for a COSMIC follow-on mission (different orbit configuration, number of satellites, etc) • What is the optimal “choice”? • CEOS action WE-07-03 on ‘evaluation of the requirements to conduct RO OSSEs’ • The action has recently been completed • International Joint OSSE project • 2-yr full time post-doctoral scientist • Hopefully, we will get funding soon Requirements for RO OSSE • Build the interface between the chosen RO simulator and the Nature Run • Choose the RO products to be simulated • Simulate the observations and tune the error covariance matrix for the selected constellations • Conduct the assimilation experiments • Evaluate the results • Choose the ‘optimal’ constellation

18. OSSEs to prepare for GOES-RTong 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), Simulation of GOES-12 Sounder Observed GOES-12 Sounder In nature Run, there is 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. Observed GOES-12 18 bands on 0230 UTC October 01, 2005 for North Atlantic Ocean section. Time Series of Mean Tb Time Series of Mean Tb Time Series of Mean Tb Time Series of Mean Tb

19. Progress Preliminary simulation of GOES from T511NR has completed for entire Nature Run period (13 month)Other preliminary basic data, Conventional data, AIRS, HIRS,AMSUA/B are also simulated for 13 month. Future Work Simulate GOES-R ABI radiances from Nature Run data,Perform NWP model simulations to investigate the impacts of GOES-12 and GOES-R measurements. Conduct impact test using data assimilation system at NCEP-NESDIS Time Series of Mean Tb Time Series of Mean Tb Time Series of Mean Tb Time Series of Mean Tb

20. Regional OSSEs to Evaluate ATMS and CrIS Observations • M. Hill, P. J. Fitzpatrick, X. Fan, V. Anantharaj, • M. Masutani, L. P. Riishojgaard, and Y. Li • GRI/Mississippi State Univ (MSU), JCSDA

21. Other OSSEs planned or considered Seeking funding but start with volunteers OSSE to evaluate UAS ESRL and NCEP Data assimilation for climate forecasts H. Koyama, M. Watanabe (University of Tokyo) Presented at IOAS-AOLS 13.3 Thursday 2:00 OSSEs for THORPEX T-PARC EMC, FSU, ESRL Assimilation with LETKF possibly by 4D-var T. Miyoshi(UMD) and Enomoto(JEMSTEC) Visualization of the Nature run O. Reale (NASA/GSFC/GLA), H. Mitchell(NASA/GSFC/SIVO) Analysis with surface pressure Gil Compo, P. D. Sardeshmukh (ESRL) Data assimilation with RTTOVS Environment Canada OSSE to evaluate data assimilation systems It is worthwhile to try identical twin experiments to understand model error. ECMWF and GMAO Sensor Web Uses same Nature Run NASA/GSFC/SIVO, SWA , NGC

22. Challenges in Regional OSSE There is great deal of interest toward regional OSSEs to study data impact on forecast of hurricanes and midlatitude storms. Even if using same global Nature run, regional OSSEs have to deal with handicaps. • Lateral boundary conditions eventually dominate the forecast inside the regional domain, obscuring any effect of the observation mix on forecast accuracy. This must be considered when evaluating the OSSE: • The size of the geographic region controls the length of forecasts that can be considered shorter for smaller regions. • Ideally, the same observation mix should be used in the regional model as in the global model that supplies the boundary conditions. • One is forced to execute two nature runs and coordinate two data assimilation and prediction systems. If regional Nature Runs with higher resolution is produced nesting within the global nature run, uncertainty in regional OSSE will become much more serious. Several groups in Joint OSSEs are investigating strategies for credible regional

23. Potential Future Nature Runs ECMWF: 17 km NICAM: 14km NOAA/ESRL, NASA/GMAO-GFDL NCEP/NMM-UMD NCAR/MMM JAMSTEC

24. Summary ●GMAO Software to calibrated basic data of is ready for release. ● Further development and more software are being developed in NCEP, NESDIS, and ESRL as well as at GMAO. ● Data base and computing resources has been set up for DWL simulation and SWA, and KNMI received funding. ● Preliminary version of basic data set has been simulated for entire T511NR period. ● OSSEs are expensive, but can be a cost-effective way to optimize investment in future observing systems ● OSSE capability should be multi-agency, community owned to avoid conflict of interest ● Independent but related data assimilation systems allows us to test robustness of answers ● Joint OSSE collaboration remains only partially funded but appears to be headed in right direction

25. Preprints • 13th (IOAS-AOLS) • 13.2 • Expanding collaboration in Joint OSSEs • Hollingsworth Symposium • P1.2 • International collaborative Joint OSSEs • Toward reliable and timely assessment of future observing systems - • http://www.emc.ncep.noaa.gov/research/JointOSSEs • Meeting summary • Discussion forums • References • FAQ for OSSE, GSI, and CRTM Dr. Anthony Hollingsworth was always an inspiration to OSSEs project and encouraging to the goals. Dr. Anthony Hollingsworth was always an inspiration to OSSEs project and encouraging to the goals. Dr. Anthony Hollingsworth was always an inspiration to OSSEs project and encouraging to the goals. Dr. Anthony Hollingsworth was always an inspiration to OSSEs project and encouraging to the goals. Dr. Anthony Hollingsworth was always an inspiration to OSSEs project and encouraging to the goals. Dr. Anthony Hollingsworth was always an inspiration to OSSEs project and encouraging to the goals. Dr. Anthony Hollingsworth was always an inspiration to OSSEs project and encouraging to the goals.

26. Nature Run cloud diagnostics (A. Tompkins) One year mean total cloud cover T511NR and MODIS Red: NR Black:MODIS T511 NR This has improved greatly in recent cycles of the model. In particular, the stratocumulus regions have improved. - The apparent underestimation relative to ISCCP over the Sahara is because this product is thought to overestimate cloud cover there. MODIS shows better agreement with the model over the deserts. - The MODIS product over sea ice is unreliable lat MODIS lon NR-MODIS lon

27. Tropics in T511 NR 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 more impressive than the system observed in August. Low-level wind speed exceeds 55 m/s 2degree HL vortices: vertical structure • These findings, albeit preliminary, are suggestive that the ECMWF NR simulates a realistic meteorology over tropical Africa and nearby Atlantic and may prove itself beneficial to OSSE research focused over the AMMA or the Atlantic Hurricane regions. • 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. Oreste Reale

28. TC in T799 Nature run 4 degree Eye-like feature extremely unrealistic, scale resembles diluted vortices typical of much lower resolution models 4 degree The system is strong, however there is a perplexing mid-tropospheric wind max. The scale is good: the system appears very compact as to be expected at such resolution. Oreste Reale

29. Quick look using 1degree data Convective Precipitation 3 hour mean 12z-15Z Oct05 2005 T511 T799 By Michiko Masutani

30. Sensitivity to Horizontal Resolution • Short-range and medium-range forecasts suggest that T799, if anything, produces stronger hurricanes than T511. • Resolution studies, however, suggests, that some aspects of the tropical climate (i.e., beyond the medium-range) of 31R1 deteriorate when increasing horizontal resolution (T159->T511). So it may be possible that T799 performs worse than T511. • In the extratropics the largest changes occur when going from T95 to T159. Rather little changes occur beyond T159 (T159->T511). Hence, it seems reasonable to assume that T511 and T799 perform similar. - Thomas Jung, ECMWF

31. End