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OSSEs: Observing Systems Simulation Experiments

OSSEs: Observing Systems Simulation Experiments. Expanding Collaboration in International Collaborative Joint Osses - Toward reliable and timely assessment of future observing systems - http://www.emc.ncep.noaa.gov/research/JointOSSEs Anthony Hollingsworth Symposium

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OSSEs: Observing Systems Simulation Experiments

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  1. OSSEs: Observing Systems Simulation Experiments Expanding Collaboration in International Collaborative Joint Osses - Toward reliable and timely assessment of future observing systems - http://www.emc.ncep.noaa.gov/research/JointOSSEs Anthony Hollingsworth Symposium Phoenix, AZ 11-15 January 2009 • Core members who have been involved from the early stage • M. Masutani[1,10,#], L.-P. Riishojgaard[10,2,$], R. Errico[2,$], • E. Andersson[4], J. S. Woollen[1,+], • Stoffelen[7], G.-J. Marseille[7], O. Reale[2,$], • Y. Xie[5] N. Prive[5,@], T. Zhu[3,@], F. Weng[3], • G. D. Emmitt[6], T. W. Schlatter[5], S. J. Lord[1], • S. Greco[6], S. A. Wood[6], R. Yang[2,&], • H. Son[3,##], T. J. Kleespies[3,10], Y. Song[1,%]

  2. OSSEs: Observing Systems Simulation Experiments Expanding Collaboration and Support C. Hill[8], V. Anantharaj[8], P. Fitzpatrick[8], X. Fan[8], H. Pryor[2], E. Salmon[2], H.- C. Liu[2,+], M. Sienkiewicz[2,+], A. da Silva[2], M. Govett[5],Z. Pu[11], L. Cucurull[10], D. Devenyi[5], D. L. Birkenheuer[5], T. Jung[4], A. Thompkins[4], D. Groff[1,+], D. Kleist[1,+], R. Treadon[1], K. Fielding[4], W. Lahoz[17], E. Brin[2], Z. Toth[1], Y. Sato[1,9], M. Hu[5], S. Weygandt[5], M. J. McGill[2], T. Miyosh[9], T. Enomoto[15], M. Watanabe[13],H. Koyama[13,] Y. Rochen[12] , M. Seablom[2], B. I. Hauss[14], R. Burn[2,14], G. Higgins[14], R. Atlas[18], S. Koch[5], H. Wang[16], Y. Chen[16], X.-Y. Huang[16] [1]NOAA/NWS/National Centeres for Environmental Prediction (NCEP), Camp Springs, MD, [2]NASA/Goddard Space Flight Center (GSFC), Greenbelt, MD, [3]NOAA/ NESDIS/STAR, Camp Springs, MD, [4]ECMWF, Reading, UK [5]NOAA/Earth System Research Laboratory(ESRL), Boulder, CO, [6]SWA, Charlottesville, VA [7]Royal Dutch Meteorological Institute (KNMI), DeBilt, Netherlands, [8]Mississippi State University/GRI (MSU), MS, [9]JMA, Tokyo, Japan, [10]JCSDA, MD [11]University of Utah, UT, [12]Environment of Canada, Ontario, Canada [13]University of Tokyo, Japan, [14]NGC, [15]JAMSTEC, Japan, [16]NCAR, Boulder CO, [17]Norsk Institutt for Luftforskning (NILU), Norway [18]NOAA/OAR/AOML, Miami, FL, [#]Wyle Information Systems Inc., VA [+]SAIC, MD, [$]GEST, University of Maryland, Baltimore, MD, [%]QSS, MD, [##I].IMSG, MD [&]SSAI.  MD, [@]CIRA/CSU, CO

  3. Benefit of OSSEs Need for OSSEs ●OSSEs help in understanding and formulating observational errors ●DA (Data Assimilation) system will be prepared for the new data ●Enable data formatting and handling in advance of “live” instrument ● OSSE 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. If we cannot simulate observations, how could we assimilate observations?

  4. Need for collaboration Need one good new Nature Run which will be used by many OSSEs, including regional data assimilation. Share the simulated data to compare the OSSE results from various DA systems to gain confidence in results. OSSEs require many experts and require a wide range of resources. Extensive international collaboration within the Meteorological community is essential for timely and reliable OSSEs to influence decisions.

  5. 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

  6. 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)

  7. 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.)

  8. 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  

  9. Simulation of Observation OBS91L Nature Run Model level profiles for simulating radiance obs NCEP, NESDIS For development purposes, 91-level ML 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

  10. 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).

  11. 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

  12. 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

  13. 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)

  14. 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

  15. 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

  16. 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

  17. 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 Presented at IOAS-AOLS 13.5 Thursday 2:30

  18. 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

  19. 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

  20. 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

  21. 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

  22. 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

  23. 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

  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. Dr. Anthony Hollingsworth was always an inspiration to OSSEs project and encouraging to the goals.

  26. End

  27. Evaluation of the Nature run • Utilize Goddard’s cyclone tracking software (Terry and Atlas, AMS conf, Aug 1996): • Identifies and tracks mostly extratropical cyclones (cutoff at 20 deg N/S latitude) • Interfaces with GrADS contouring algorithm • Uses SLP field at 4hPa contour interval • Finds centroid of inner-most closed isobar • Tracks the centers using extrapolation and 500hPa steering Cloud Cover Comparison between the ECMWF T511 Nature Run against climatology 20050601-20060531, exp=eskb, cycle=31r1 Adrian Tompkins, ECMWF NR MODIS NR-MODIS Tropics Oreste Reale (NASA/GSFC/GLA) • Cyclone tracks generated: • Nature run at one degree for Jun 2005 to May 2006 (each month and season) • NCEP operational analysis at one degree for 2000 to 2006 (each month, 68 of 84 months were available) 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

  28. Simulation GOES Radiance for OSSETong Zhu (CIRA/CSU), Fuzhong Weng (NOAA/NESDIS), Michiko Masutani (NOAA/EMC), Steve Load (NOAA/EMC), Jack Woollen (NOAA/EMC), Thomas J. Kleespies(NOAA/NESDIS), Yong Han(NOAA/NESDIS), Quanhua, Liu (QSS), Sid Boukabara (NOAA/NESDIS) Advanced Baseline Imager (ABI) will be flown on the next generation of NOAA Geostationary Operational Environmental Satellite (GOES)-R platform. The sensor will provide enhanced spatial, temporal information for atmospheric moisture, wind and many surface properties. A joint Observation System Simulation Experiments (OSSE) project was started recently to study the impacts of GOES-R ABI measurements on numerical weather prediction. In this poster, we will present some results of the simulation of GOES radiances based on OSSE nature run output and the evaluation against observations. A case study will be performed to analysis ECMWF T511 natural run results. ABI instrument properties and geometry factors are simulated based on current GOES and MSG SEVIRI sensors. The JCSDA Community Radiative Transfer Model (CRTM) is used to simulate ABI radiances with the natural run atmospheric profiles. The simulated radiances are evaluated by comparing with current GOES observations. Current GOES Imager IR band has 4 km horizontal resolution (FOV), GOES Sounder has 10 km resolution. A full disk scan has total 10,080,910 observation points, and takes about 26 min. GOES-R ABI sensor will has 1km/2 km resolution. Steps: 1. The OSSE Nature Run data is come from ECMWF T511 13-month simulation. The data set contains 91 vertical levels variables, which are then horizontally interpolated to observation points without vertical interpolation. Selected model level data and all surface data are included. 2. CRTM model is used to simulate GOES-12 Imager, Sounder, MSG SEVIRI, and GOES-R ABI measurements. 3. Perform validations of the simulated radiances with statistical analysis and comparison with real observations. Radiance Simulation Design Objective GOES data is simulated to test impact of GOES in simulation experiments in comparison with impact of real data. OSSE for GOES will serve as a calibration for GOESR OSSE. GOES-EAST Observation Locations Radiance Simulation Flow Chart A full disk scan of GOES-12 Imager has total 10,080,910 observation points with 4 km resolution. However, Current NCEP/GSI model only take a thinned 1x1 degree GOES-12 dataset. In this study, we extract GOES-12 Imager observation locations (lon/lat) at 20 km and 60 km resolutions. The Nature Run data is interpolated on to these two different resolution locations. Reduced to ~20 km, 403239 points Reduced to ~60 km, 40323 points

  29. Simulation of GOES-12 Imager Time Series of Mean Tb Simulation of GOES-12 Sounder Simulated GOES-12 Imager 4 bands with ECMWF Nature Run output data at 0300 UTC October 1, 2005. It is found that the water vapor band, 6.5 µm, is most accurate band simulated by CRTM model. Simulated Radiances Observed vs. simulated GOES-12 sounder for the mean Tb over North Atlantic Ocean region.Black lines are mean Tb from NR simulated, and the red lines are the mean Tb from observation. They should not be the same but similar statistical features are important. 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. Progress Time series of hourly, full disk mean brightness temperature of the simulated GOES-12 Imager 4 bands from September 28 to October 10, 2005.Under clear sky conditions, the brightness temperatures are warmer than that of cloudy conditions. The daily change under clear conditions is apparent. The observed GOES-12 Sounder 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. Observed GOES-12 18 bands on 0230 UTC October 01, 2005 for North Atlantic Ocean section. 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 Black lines are total points mean Tb, red lines are the mean Tb over clear sky condition, and the blue lines are the mean Tb over cloudy condition. Clear sky condition is defined as where total cloud coverage (TCC)< 0.1, and cloudy condition is where TCC > 0.1 Time Series of Mean Tb Time Series of Mean Tb Time Series of Mean Tb Time Series of Mean Tb

  30. 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 MSU OSSE plan • regional scale nature run (RSNR) using MM5 • one-way nested grid set, with ICs and BCs from ECMWF NR data • sensitivity experiments • use second model (WRF) to avoid similarity in model error (fraternal twin problem) • initial analyses tuned to model background errors • cold start simulation • assimilate synthetic surface, rawinsonde observations • assimilate data representing ATMS / CrIS • use RTM to simulate sensor-observed radiance, assimilate derived Tandqprofiles • investigate NR-simulated squall line events at the U.S. coast of the Gulf of Mexico General Overview • component sensors of NPOESS and NPP to include: • Advanced Technology Microwave Sounder (ATMS) • across track scanning microwave radiometer • cross-track resolution: 1.5 km • Cross-track Infrared Sounder (CrIS) • Fourier transform spectrometer • cross-track resolution: 14.0 km • vertical resolution: ~ 3.0 km • Combination of ATMS / CrIS designed to improve profiles of T and q • OSSE with mesoscale models needed to assess the impact of ATMS / CrIS For more detail and progress, visit Wed 2:30 JP 6.22 (16 SATMET,5GOESR-NPOESS)

  31. ADM-Aeolus simulation for J-OSSE KNMI planG.J. Marseille and Ad Stoffelen Verification against SWA ADM simulation. Simulation consistency needed on • Clouds • Laser beam cloud hit from model grid box cloud cover. Random? • Cloud backscatter and extinction from model clouds • Maximum overlap between clouds in adjacent (vertical) levels • Aerosols • Backscatter and extinction • Horizontal variability • along track over 50 km accumulation length • between adjacent observations (separated by 150 km) • Vertical variability (stratification) • Dynamics • Wind variability over 50 km accumulation length 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 • 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 • Representativeness error • Unresolved model scales in nature run and ADM sampling determines representativeness error to be added to ADM HLOS wind observation • ADM continuous mode • ESA decision December 2008 • If continuous mode is selected then more funding will probably become available for additional simulation studies • Simulation of post-ADM scenarios • EUMETSAT funding?

  32. LATEST RESULTS In Spring, 2008 Simpson Weather Associates, Inc. established the Doppler Lidar Simulation Model version 4.2 onto 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. NEAR FUTURE PLANS • Line of Sight wind operator for the assimlation models • Integrate Satellite Toolkit into the workflow tool to • provide satellite location and attitude inputs • Establish the T511 and T799 nature runs into DLSM • database format including generating aerosol, molecular • and cloud optical property databases • Build the slewing capability into the scanner model • Integrate into the Sensor Web the SWA cloud motion • wind model • Global OSSEs (maybe mesoscale OSSEs - hurricanes)

  33. LATEST RESULTS Simulation of DWL at SWA Sid A. Wood, G. David Emmitt, Steve Greco In Spring, 2008 Simpson Weather Associates, Inc. established the Doppler Lidar Simulation Model version 4.2 onto 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. NEAR FUTURE PLANS • Line of Sight wind operator for the assimlation models • Integrate Satellite Toolkit into the workflow tool to • provide satellite location and attitude inputs • Establish the T511 and T799 nature runs into DLSM • database format including generating aerosol, molecular • and cloud optical property databases • Build the slewing capability into the scanner model • Integrate into the Sensor Web the SWA cloud motion • wind model • Global OSSEs (maybe mesoscale OSSEs - hurricanes) 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

  34. Simulation of DWL in Cloudy conditions at SWA Recommendations Utility of Doppler Wind Lidars in cloudy conditions • There are concerns expressed that airborne lidars may be marginally useful in cloudy conditions (same for space-based lidars) • Airborne and space-based data suggests otherwise. • An airborne wind lidar combined with dropsondes makes a powerful combination • The DWL can provide direct measure of representativeness of the dropsonde observations enabling dynamic assignment of representativeness in the total observation error (σo) assigned for DA. • Recommend hybrid wind lidar for research from high altitude aircraft such as ER2, WB-57, Proteus, Global Hawk.. • Hybrid Doppler wind lidar (DWL) includes a molecular subsystem for aerosol weak regions • A coherent sub system for cloudy situations and aerosol rich regions (PBL, elevated dust layers..) • Co-fly hybrid DWL with dropsondes as will be done in TPARC using the NAVY P3 and the DLR Falcon. Current expectations • From space, current laser technology will detect cloud on 80% of individual laser shots; however, 80% of all laser shots will provide a ground return (i.e. 75% of the shots that intercept cloud will also provide a ground return (based upon analyses of GLAS data). • Difficult to generalize for airborne lidars since mission objectives could be targeting cloudy phenomena. However, experience by those flying airborne lidars is that laser shots penetrate clouds far more frequently than initially expected. Thus clouds are seen as optically porous at near infrared wavelengths. Hurricane Bonnie, 1998 Data from an airborne water vapor DIAL flown by NASA Vertical cross section below Cloud Lidar System flown on ER2 • SPOT: Satellite Pour l'Observation de la Terre • CFLOS: Cloud Free Line of Sight • TODWL: Twin Otter Doppler Wind Lidar • HRDL: High Resolution Doppler Lidar • DIAL: Differential Absorption Lidar • LITE: Lidar Technology Experiment • GLAS: Geoscience Laser Altimeter System Figure from Vertical cross section of CALIPSO returns illustrating performance in very Cloudy areas. Note the returns below clouds.

  35. DWL Operations within a Sensor Web Modeling and Data Assimilation System:Recent Results2G. D. Emmitt, 2S. Greco, 2S. A. Wood, M. Seablom, 5E. Brin, 5,6,$L. P. Riishojgaard • Sensor Web Simulator Design • During 2007 most elements of the lidar use case (1-5) were executed “by hand” to help aid in the design of the simulator prototype • Five separate Observing System Simulation Experiments (OSSEs) were conducted that concluded: • Under certain situations1, the lidar duty cycle may be reduced 30% without impacting forecast skill • Under certain situations, having the model task the lidar to perform a roll maneuver improves detection of features of interest 30% (tropical cyclones, jet streaks, rapidly changing atmospheric conditions) SENSOR WEB A model-driven sensor web is an Earth observing system that uses information derived from data assimilation systems and numerical weather prediction models to drive targeted observations made from earth-orbiting spacecraft as well as from atmospheric- and ground-based observing systems. Project Goal Demonstrate the value of implementing sensor web concepts for meteorological use cases Quantify cost savings to missions Quantify improvement in achieving science goals Design and Build an integrated simulator with functional elements that will allow multiple “what if” scenarios in which different configurations of sensors, communication networks, numerical models, data analysis systems, and targeting techniques may be tested Adaptive Targeting

  36. Occulting GPS Ionosphere Neutral atmosphere LEO Earth Radio Occultation concept • An occultation occurs when a GPS (GNSS) satellite rises or sets across the limb wrt to a LEO satellite. • A ray passing through the atmosphere is refracted due to the vertical gradient of refractivity (density). • During an occultation (~ 3min) the ray path slices through the atmosphere Raw measurement: change of the delay (phase) of the signal path between the GPS and LEO during the occultation. (It includes the effect of the atmosphere). GPS transmits at two different frequencies: ~1.6 GHz (L1) and ~1.3 GHz (L2).

  37. Choice of RO products Raw measurements of phase of the two signals (L1 and L2) s1, s2, Bending angles of L1 and L2 a1, a2 (neutral) bending angle Clocks correction, orbits determination, geometric delay Refractivity a Ionospheric correction Atmospheric products N Abel transfrom T, Pw, P Hydrostatic equilibrium, eq of state, apriori information

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