Progreee in Evaluation of Space Based DWL in Joint OSSEs

1. Progreee in Evaluation of Space Based DWL in Joint OSSEs Lars Peter Riishojgaard Joint Center for Satellite Data Assimilation (Director) Michiko Masutani(EMC,JCSDA), G. David Emmitt (SWA), Steven Greco(SWA), Sidney Wood(SWA) Ad Stoffelen(KNMI), Gert-Jan Marseille(KNMI) Jack Woollen(EMC) February 2009 http://www.emc.ncep.noaa.gov/research/JointOSSEs

2. Full OSSEs There are several types of Observing System Simulation Experiments. We refer to the “Joint OSSE” as 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 a “Full OSSE”, all major existing observations have to be simulated with both random and systematic observational errors. • Calibrations of the simulated observations must be performed to provide quantitative data impact assessment. In OSSE calibrations, real and simulated data impacts are compared. The results are used to evaluate data impacts in simulation experiments. 2

3. 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 and quantitative 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). Existing Data Assimilation System and verification methods are used for Full OSSEs. This will help in the development of DASs and verification tools. 3

4. 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 4

5. 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 Not recommended for OSSE. Better version will be provided when JOSSE is ready 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.

6. 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 6

7. 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. Simulation of control data for calibration ● Simulation of control data should be funded for OSSEs ● GMAO and NCEP are simulating control data

8. Data Simulation strategies at NCEP-NESDIS Progress and current plan : ► Ozone data from SBUV ► Conventional data based on NCEP reanalysis quality controlled distribution. (More complete data set compared to operational data) ► Satellite radiance data in 2005 distribution. AMSUA, AMSUB, GOES, HIRS2, HIRS3, AIRS,MSU are being generated at foot print used by NCEP operational analysis. ► Observational error is random error based on error table. ► Limited calibration and validation will be conducted by NCEP and NESDIS for their own use. However, users are expected to perform their own calibrations and validation. Future plan: ► Observational error based on correlated noise ► Simulation and assimilation of of cloudy radiance and let sampling done by assimilation. Cloudy radiance is still under development.

9. HIRS3 NOAA 16 Ch=4 May 2nd 00z (f12) Template data Observed radiance with horizontal thinning Horizontal and vertical thinning Simulation of HIRS3 radiance from NOAA16 Latest version of CRTM (1.2.2) is 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. Horizontal thinning

10. Progress in Calibration at ESRL- NCEP • Data denial tests are run for synthetic obs subsets of similar data types • Analysis impact (global RMS difference in control and data denial analysis) is calculated for synthetic obs and compared to analysis impact for data denial with real archived data from July 2005 • Standard deviation of synthetic errors are adjusted, errors are regenerated • New data denial case is run and compared to real data, errors adjusted, etc • Repeat until analysis impact matches real data analysis impact, or until satisfied that calibration is not possible • 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-NESDIS. • Focused on July-August 2005. • GSI version May 2007. • NCEP is working on upgrading OSSE system to newer GSI to accommodate DWL and flow dependent error covariances. Some calibrations will be repeated.

11. Concept for a U.S. Space-Based Wind Lidar Global Wind Observing Sounder (GWOS) 11

12. 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.

13. GWOS Sampling Hybrid Doppler Wind LidarMeasurement Geometry: 400 km 350 km/217 mi 53 sec Along-Track Repeat “Horiz. Resolution” 586 km/363 mi 13

14. Progress in GWOS DWL Simulations (SWA) 1) Acquired the ECMWF T511 Nature Run (NR) model level data in GRIB format 2) Developed software to read and unpack the T511 NR model level GRIB data 3) Unpacked the T511 NR model level GRIB data for July 1 - Aug 10, 2005 4) Created global atmospheric data sets for July 1 - Aug 10, 2005 to be used by the Doppler Lidar Simulation Model (DLSM) 5) Modified the LSM to work with the variables, levels and resolutions/grids of the T511 NR model level data 6) Conducted a test run of a GWOS DWL simulation using the T511 NR 7) Will continue the GWOS DWL simulations for July 1 - August 10, 2005 upon final quality check

15. Simulation of GWOS dataSimpson Weather Associates

16. Simmulation of ESA DWLKNMI Spring 2008: ADM Mission Advisory Group (ADMAG) advises ESA to participate in Joint OSSE KNMI submits TOGETHER proposal to ESA TOGETHER Towards a Global observing system through collaborative simulation experiments

17. ADM simulation ADM OSSE heritage, for details see Stoffelen et al., 2006 http://www.knmi.nl/~marseill/publications/fulltexts/osse.pdf Tools for retrieving nature run fields from ECMWF archive Orbit simulator Interpolation of model fields to ADM location “True” (HLOS) wind Instrument error: LIPAS (Lidar Performance Analysis Simulator) For details see Marseille and Stoffelen, 2003 http://www.knmi.nl/~marseill/publications/fulltexts/dwlsimul.pdf LIPAS is updated and compatible with L2B processor performance

18. DWL OSSE Progress and Plans at NCEP NCEP data assimilation system (GSI) has been upgraded to handle DWL data more efficiently. The code was successfully tested for idealized data. The system is being upgraded for 2010 operational version. Extensive calibration experiments have been conducted by ESRL for July-August 2005 using control data produced by GMAO with 2007 GSI system. NCEP will repeat calibration experiment for selected wind data (RAOB wind) with new GSI system. GWOS DWL data provided by SWA will be tested for July-August 2005. NCEP will produce further control data. GWOS OSSE will be extended to hurricane period and winter storm period. Data impact of SWA and KNMI product for ADM will be compared. Coordinate with GMAO OSSE to gain the confidence in results.

19. Data Sharing in Joint OSSEs Simulated observation and other useful data will be shared among Joint OSSE teams. NASA/NCCS provided dis space for Joint OSSE data sharing There is a entry created for Joint OSSE http://portal.nccs.nasa.gov/josse/index.pl Make entry to each data set and generating institute, and contact person. People use these data must contact generating institutes.

20. OSSEs are a cost-effective way to optimize the investment in future observing systems such as the proposed space-based Doppler wind lidar (GWOS) OSSE capability should be broadly based (multi-agency) Credibility Cost savings Simulation of basic control data should be conducted by instrument teams Exploratory or “Quick” OSSEs serve as a less expensive way to down-select observing system configurations as candidates for “full OSSEs”. However, the interaction of the full and exploratory OSSE efforts is critical to the proper interpretation and representation of the simulated impact results Summary 20

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22. 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 should be funded for OSSEs. If we do not assist other OSSEs they will produce damaging results Summary 22

23. 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. 23

24. Doppler Wind Lidar Simulation Model Simpson Weather Associates 24