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WRFDA 2013 Overview Hans Huang NCAR

WRFDA 2013 Overview Hans Huang NCAR. Acknowledgements: WRFDA team and many visitors, NCAR, CWB, AirDat , NRL, BMB, NSF-OPP, AFWA, NSF-AGS, USWRP, NASA, PSU, CAA, KMA, EUMETSAT . Goal: Community WRF DA system for regional/global research/operations

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WRFDA 2013 Overview Hans Huang NCAR

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  1. WRFDA 2013 OverviewHans HuangNCAR Acknowledgements: WRFDA team and many visitors, NCAR, CWB, AirDat, NRL, BMB, NSF-OPP, AFWA, NSF-AGS, USWRP, NASA, PSU, CAA, KMA, EUMETSAT

  2. Goal: Community WRF DA system for • regional/global • research/operations • deterministic/probabilistic applications • Techniques: • 3D-Var • 4D-Var (regional) • Ensemble DA • Hybrid Variational/Ensemble DA • Model: WRF (ARW, NMM, Global) • Observations: • Conv.+Sat.+Radar • (+Bogus) • Supported by NCAR/ESSL/MMM/DAS WRFDA

  3. www.mmm.ucar.edu/wrf/users/wrfda

  4. WRFDA tutorials The next: 22-24 July 2013 WRFDA online tutorial and user guide http://www.mmm.ucar.edu/wrf/users/wrfda

  5. WRFDA v3.5 • Release date: April 2013 • New features: • Support for data from additional satellite instruments: • METOP: Infrared Atmospheric Sounding Interferometer (IASI) • Suomi NPP: Advanced Technology Microwave Sounder (ATMS) • FY3: Microwave Temperature Sounder (MWTS) and Microwave Humidity Sounder (MWHS) • Direct wind speed/direction assimilation • ETKF code updated and documentation added • Support for new ECMWF cloud detection scheme • Allowing variable number of inner-loop cost function minimizations for each outer-loop

  6. Bogus: • TC bogus • Global bogus WRFDA Observations • In-Situ: • Surface (SYNOP, METAR, SHIP, BUOY) • Upper air (TEMP, PIBAL, AIREP, ACARS, TAMDAR) • Remotely sensed retrievals: • Atmospheric Motion Vectors (geo/polar) • GPS refractivity (e.g. COSMIC) • Ground-based GPS Total PrecipitableWater (PW)/Zenith Total Delay (ZTD) • SATEM thickness • Scatterometer oceanic surface winds • SSM/I oceanic surface wind speed and TPW • Radar radial velocities and reflectivities • Satellite temperature/humidity/thickness profiles • Stage IV precipitation/rain rate data (4D-Var) • Wind profiler wind profiles • Satellite radiances (using RTTOV or CRTM): • HIRS NOAA-16, NOAA-17, NOAA-18, NOAA-19, METOP-A • AMSU-A NOAA-15, NOAA-16, NOAA-18, NOAA-19, EOS-Aqua, METOP-A • AMSU-B NOAA-15, NOAA-16, NOAA-17 • MHS NOAA-18, NOAA-19, METOP-A • AIRS EOS-Aqua • SSMIS DMSP-16, DMSP-17, DMSP-18 • IASI METOP-A • ATMS Suomi-NPP • MWTS FY-3 • MWHS FY-3 New, v3.5

  7. Planned and ongoing work (I) (after WRFDA v3.5) • WRFPLUS and 4DVAR optimization • Revision of the energy norm used by FSO • Background error covariance improvements • Smoothing options at various stages of GEN_BE • Use of u, v, T, psand RH as control variables for convective scale DA • GEN_BE 2.0 development and documumentation • More satellite platform/instruments • CrIS • GOES sounder and GOES imager • MSG SEVIRI • GCOM-W1 AMSR2 • Update RTTOV and CRTM interfaces for their latest release versions • GPS RO DA • More options for vertical data thinning • Nonlocal GPS RO parallelization

  8. Planned and ongoing work (II)(after WRFDA v3.5) • Radar data assimilation development • An indirect radar reflectivity DA scheme • Radar DA with WRFDA 4DVAR • Dual-resolution hybrid DA • WRFDA online thinning for conventional data • Blending backgrounds or analyses using a spatial filter • Displacement analysis • Multivariate cloud analysis • Ensemble Variational Integrated Lanczos (EVIL)

  9. Assimilation of Wind Speed and Direction Observations Xiang-Yu Huang1, Feng Gao1,2, Neil A. Jacobs3, Hongli Wang1 1National Center for Atmospheric Research, Boulder, Colorado, USA 2Nanjing University of Information Science & Technology, Nanjing, Jiangsu, China 3AirDat LLC, Morrisville, North Carolina, USA

  10. Introduction • All current DA systemsturn sp and dir to u and v before assimilating them, assuming observation errors in u and v are known and not correlated. • Many observing systems observe wind speed (sp) and direction (dir); should be more straightforward to derive obssp and dir errors and assume them uncorrelated. • Wind direction observation errors vary significantly in space and time (e.g. small at the jet level, large close to surface) and should influence the analysis results. • WRFDA is the only DA system which has an option to assimilate wind sp and dir observations taking the sp and dir observation errors into account.

  11. A simple illustration (probably simplified too much) • (Over Simplified) Assumptions: • Wind component (sp,dir) or (u,v) can be analyzed univariately; • The background error and observational error are the same. • Assimilate u and v (asm_uv): • Assimilate sp and dir (asm_sd):

  12. From the same background and observations, asm_uv and asm_sd produce very different analyses!

  13. From the same background and observations, asm_uv produces weaker winds than asm_sd does!

  14. Single observation experiments using WRFDA and observation errors estimated from a previous data study (without the over simplified assumptions)

  15. Again, now using WRFDA, asm_uv produces weaker winds than asm_sd does! From the highly simplified solutions From WRFDA single ob experiments

  16. WRFDA analysis increments asm_uv V U Vector asm_sd V Vector U

  17. Observing system simulation experiments • Nature run: 6 km grid space with 57 vertical levels • Observations – simulated wind observations • at every 30x30th grid • across 12 standard pressure levels • 0000 UTC 15 Dec. – 0000 UTC 20 Dec. 2011 • Cycle run: 18 km grid space with 43 vertical levels • 6 h full-cycle from 0000 UTC 16 Dec. 2011 • 48 h forecasts are generated every 6 h • Boundary condition: • FNL for nature run • GFS forecast for cycle experiments • Background Errors – cv5 by NMC • Verification: against nature run

  18. Verification scores sp dir v u

  19. Concluding remarks for the wind DA study • A new formulation for assimilating wind speed and direction observations is presented and compared with the traditional formulation for assimilating u and v. • Simple illustrations are used to show the differences and single pair observation experiments with WRFDA qualitatively confirm the differences. • OSSEs show potential benefits of the new formulation. • Real observation experiments are ongoing. Huang, X.-Y., Gao, F., Jacobs, N. and Wang, H. 2013. Assimilation of wind speed and direction observations: a new formulation and results from idealized experiments. Tellus A, 65, 19936, http://dx.doi.org/10.3402/tellusa.v65i0.19936.

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