• Ajjaji, R., A. A. Al-Katheri, and A. Dhanhani, 2007: Tuning of WRF 3D-var data assimilation system over Middle-East and Arabian Peninsula. Extended Abstracts, Eighth WRF Users' Workshop, Boulder, CO, WRF. [Available online at www.mmm.ucar.edu/wrf/users/workshops/WS2007/abstracts/4-6_Ajjaji.pdf.]

    • Search Google Scholar
    • Export Citation
  • Anderson, J., T. Hoar, K. Raeder, H. Liu, N. Collins, R. Torn, and A. Avellano, 2009: The Data Assimilation Research Testbed: A community facility. Bull. Amer. Meteor. Soc., 90, 12831296.

    • Search Google Scholar
    • Export Citation
  • Auligné, T., A. P. McNally, and D. P. Dee, 2007: Adaptive bias correction for satellite data in a numerical weather prediction system. Quart. J. Roy. Meteor. Soc., 133, 631642.

    • Search Google Scholar
    • Export Citation
  • Barker, D. M., 1999: The use of synoptically-dependent background error structures in 3DVAR. Met Office VAR Scientific Documentation Paper 26, 11 pp. [Available from Met Office, Fitzroy Road, Exeter EX1 3PB, United Kingdom.]

    • Search Google Scholar
    • Export Citation
  • Barker, D. M., 2005: Southern high-latitude ensemble data assimilation in the Antarctic Mesoscale Prediction System. Mon. Wea. Rev., 133, 34313449.

    • Search Google Scholar
    • Export Citation
  • Barker, W. Huang, Y.-R. Guo, and A. Bourgeois, 2003: A three-dimensional variational (3DVAR) data assimilation system for use with MM5. NCAR Tech. Note NCAR/TN-453+STR, 68 pp. [Available online at www.mmm.ucar.edu/mm53dvar/docs/3DVARTechDoc.pdf.]

    • Search Google Scholar
    • Export Citation
  • Barker, W. Huang, Y.-R. Guo, and A. Bourgeois, and Q. N. Xiao, 2004: A three-dimensional (3DVAR) data assimilation system for use with MM5: Implementation and initial results. Mon. Wea. Rev.,132, 897914.

    • Search Google Scholar
    • Export Citation
  • Bishop, C., B. J. Etherton, and S. J. Majumdar, 2001: Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Mon. Wea. Rev., 129, 420436.

    • Search Google Scholar
    • Export Citation
  • Bloom, S., L. L. Takacs, A. Silva, and D. Ledvian, 1996: Data assimilation using incremental analysis updates. Mon. Wea. Rev., 124, 12561271.

    • Search Google Scholar
    • Export Citation
  • Chen, S.-H., 2007: The impact of assimilating SSM/I and QuikSCAT satellite winds on Hurricane Isidore simulations. Mon. Wea. Rev., 135, 549566.

    • Search Google Scholar
    • Export Citation
  • Chen, S.-H., F. Vandenberghe, G. W. Petty, and J. Bresch, 2004: Application of SSM/I satellite data to a hurricane simulation. Quart. J. Roy. Meteor. Soc., 130, 801825.

    • Search Google Scholar
    • Export Citation
  • Cucurull, L., F. Vandenberghe, D. M. Barker, E. Vilaclara, and A. Rius, 2004: Three-dimensional variational data assimilation of ground-based GPS ZTD and meteorological observations during the 14 December 2001 storm event over the western Mediterranean Sea. Mon. Wea. Rev., 132, 749763.

    • Search Google Scholar
    • Export Citation
  • Cucurull, L., Y.-H. Kuo, D. M. Barker, and S. R. H. Rizvi, 2006: Assessing the impact of COSMIC GPS radio occultation data on weather analysis and short-term forecast over the Antarctic: A case study. Mon. Wea. Rev., 134, 32833296.

    • Search Google Scholar
    • Export Citation
  • Daley, R., 1991: Atmospheric Data Analysis. Cambridge Atmospheric and Space Science Series, Cambridge University Press, 457 pp.

  • Dee, D. P., 2005: Bias and data assimilation. Quart. J. Roy. Meteor. Soc., 131, 33233343.

  • Demirtas, M., D. M. Barker, Y. Chen, J. Hacker, X.-Y. Huang, C. Snyder, and X. Wang, 2009: A hybrid data assimilation system (ensemble transform Kalman filter and WRF-VAR) based retrospective tests with real observations. Preprints, 19th Conf. on Numerical Weather Prediction, Omaha, NE, Amer. Meteor. Soc., 9A.5. [Available online at http://ams.confex.com/ams/pdfpapers/153340.pdf.]

    • Search Google Scholar
    • Export Citation
  • Desroziers, G., and S. Ivanov, 2001: Diagnosis and adaptive tuning of observation-error parameters in a variational assimilation. Quart. J. Roy. Meteor. Soc., 127, 14331452.

    • Search Google Scholar
    • Export Citation
  • Etherton, B., and C. Bishop, 2004: Resilience of hybrid ensemble 3DVAR analysis schemes to model error and ensemble covariance error. Mon. Wea. Rev., 132, 10651080.

    • Search Google Scholar
    • Export Citation
  • Faccani, C., and R. Ferretti, 2005a: Data assimilation of high-density observations. I: Impact on initial conditions for the MAP/SOP IOP2b. Quart. J. Roy. Meteor. Soc., 131, 2142.

    • Search Google Scholar
    • Export Citation
  • Faccani, C., and R. Ferretti, 2005b: Data assimilation of high-density observations. II: Impact on the forecast of the precipitation for the MAP/SOP IOP2b. Quart. J. Roy. Meteor. Soc., 131, 4361.

    • Search Google Scholar
    • Export Citation
  • Gu, J., Q.-N. Xiao, Y.-H. Kuo, D. M. Barker, J. Xue, and X. Ma, 2005: A case study of Typhoon Rusa (2002) on its analysis and simulation using WRF 3DVAR and the WRF modeling system. Adv. Atmos. Sci., 22, 415425.

    • Search Google Scholar
    • Export Citation
  • Guo, Y.-R., H. Kusaka, D. M. Barker, Y.-H. Kuo, and A. Crook, 2005: Impact of ground-based GPS PW and MM5-3DVAR background error statistics on forecast of a convective case. SOLA, 1, 7376.

    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., and C. Snyder, 2000: A hybrid ensemble Kalman filter–3D variational analysis scheme. Mon. Wea. Rev., 128, 29052919.

  • Han, Y., P. van Delst, Q. Liu, F. Weng, B. Yan, R. Treadon, and J. Derber, 2006: JCSDA Community Radiative Transfer Model (CRTM)—Version 1. NOAA/NESDIS/Tech. Rep. 122, 33 pp. [Available online at ftp://ftp.emc.ncep.noaa.gov/jcsda/CRTM/CRTM_v1-NOAA_Tech_Report_NESDIS122.pdf.]

    • Search Google Scholar
    • Export Citation
  • Harris, B. A., and G. Kelly, 2001: A satellite radiancebias correction for data assimilation. Quart. J. Roy. Meteor. Soc., 127, 14531468.

    • Search Google Scholar
    • Export Citation
  • He, W., Z. Liu, and H. Cheng, 2011: Influence of surface temperature and emissivity on AMSU-A assimilation over land. Acta Meteor. Sin., 25, 545557.

    • Search Google Scholar
    • Export Citation
  • Houtekamer, P. L., and Herschel L. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126, 796811.

    • Search Google Scholar
    • Export Citation
  • Huang, C.-Y., Y.-H. Kuo, S.-H. Chen, and F. Vandenberghe, 2005: Improvements in typhoon forecasts with assimilated GPS occultation refractivity. Wea. Forecasting, 20, 931953.

    • Search Google Scholar
    • Export Citation
  • Huang, X.-Y., and Coauthors, 2009: Four-dimensional variational data assimilation for WRF: Formulation and preliminary results. Mon. Wea. Rev., 137, 299314.

    • Search Google Scholar
    • Export Citation
  • Hunt, B., E. Kosterlich, and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble Kalman filter. Physica D, 230, 112126.

    • Search Google Scholar
    • Export Citation
  • Kalnay, E., 2003: Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press, 364 pp.

  • Lee, M.-S., and D. M. Barker, 2005: Preliminary tests of first guess at appropriate time (FGAT) with WRF 3DVAR and WRF model. J. Korean Meteor. Soc., 41, 495505.

    • Search Google Scholar
    • Export Citation
  • Lee, M.-S., Y.-H. Kuo, and D. M. Barker, 2006: Incremental analysis updates initialization technique applied in 10-km MM5 3DVAR and model. Mon. Wea. Rev., 134, 13891404.

    • Search Google Scholar
    • Export Citation
  • Liu, C., Q. Xiao, and B. Wang, 2008: An ensemble-based four-dimensional variational data assimilation scheme. Part I: Technical formulation and preliminary test. Mon. Wea. Rev., 136, 33633373.

    • Search Google Scholar
    • Export Citation
  • Liu, C., Q. Xiao, and B. Wang, 2009: An ensemble-based fourdimensional variational data assimilation scheme. Part II: Observing system simulation experiments with Advanced Research WRF (ARW). Mon. Wea. Rev., 137, 16871704.

    • Search Google Scholar
    • Export Citation
  • Liu, Z., X. Zhang, T. Auligné, and H.-C. Lin, 2009: Variational analysis of hydrometeors with satellite radiance observations: A simulated study. 10th WRF Users' Workshop, Boulder, CO, WRF, 2A.1. [Available online at www.mmm.ucar.edu/wrf/users/workshops/WS2009/abstracts/2A-01.pdf.]

    • Search Google Scholar
    • Export Citation
  • Lu, Q., W. Zhang, P. Zhang, X. Wu, F. Zhang, Z. Liu, and D. M. Barker, 2010: Monitoring the 2008 cold surge and frozen disasters snowstorm in south China based on regional ATOVS data assimilation. Sci. China; Earth Sci., 53, 12161228.

    • Search Google Scholar
    • Export Citation
  • McNally, P., J. C. Derber, W. Wu, and B. B. Katz, 2000: The use of TOVS level-1b radiances in the NCEP SSI analysis system. Quart. J. Roy. Meteor. Soc., 126, 689724.

    • Search Google Scholar
    • Export Citation
  • Meng, Z., and F. Zhang, 2008: Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part III: Comparison with 3DVAR in a real-data case study. Mon. Wea. Rev., 136, 522540.

    • Search Google Scholar
    • Export Citation
  • Michel, Y., T. Auligné, and T. Montmerle, 2011: Heterogeneous convective-scale background error covariances with the inclusion of hydrometeor variables. Mon. Wea. Rev., 139, 29943015.

    • Search Google Scholar
    • Export Citation
  • Parrish, D. F., and J. C. Derber, 1992: The National Meteorological Center's spectral statistical interpolation analysis system. Mon. Wea. Rev., 120, 17471763.

    • Search Google Scholar
    • Export Citation
  • Powers, J. G., 2007: Numerical prediction of an Antarctic severe wind event with the Weather Research and Forecasting (WRF) model. Mon. Wea. Rev., 135, 31343157.

    • Search Google Scholar
    • Export Citation
  • Powers, J. G., A. J. Monaghan, A. M. Cayette, D. H. Bromwich, Y. -H. Kuo, and K. W. Manning, 2003: Real-time mesoscale modeling over Antarctica: The Antarctic Mesoscale Prediction System (AMPS). Bull Amer. Meteor. Soc., 84, 15331546.

    • Search Google Scholar
    • Export Citation
  • Rabier, F., H. Järvinen, E. Klinker, J.-F. Mahfouf, and A. Simmons, 2000: The ECMWF operational implementation of four-dimensional variational assimilation. I: Experimental results with simplified physics. Quart. J. Roy. Meteor. Soc., 126, 11431170.

    • Search Google Scholar
    • Export Citation
  • Rawlins, F., S. P. Ballard, K. J. Bovis, A. M. Clayton, D. Li, G. W. Inverarity, A. C. Lorenc, and T. J. Payne, 2007: The Met Office global four-dimensional variational data assimilation scheme. Quart. J. Roy. Meteor. Soc., 133, 347362.

    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., Z. Liu, Y. Chen, and X.-Y. Huang, 2012: Impact of assimilating microwave radiances with a limited-area ensemble data assimilation system on forecasts of Typhoon Morakot. Wea. Forecasting, in press.

    • Search Google Scholar
    • Export Citation
  • Simmons, A. J., and A. Hollingsworth, 2002: Some aspects of the improvement in skill of numerical weather prediction. Quart. J. Roy. Meteor. Soc., 128, 647677.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech Note NCAR/TN-475+STR, 113 pp. [Available online at www.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf.]

    • Search Google Scholar
    • Export Citation
  • Torn, R. D., G. J. Hakim, and C. Snyder, 2006: Boundary conditions for limited-area ensemble Kalman filters. Mon. Wea. Rev., 134, 24902502.

    • Search Google Scholar
    • Export Citation
  • Wang, X., D. M. Barker, C. Snyder, and T. M. Hamill, 2008a: A hybrid ETKF-3DVAR data assimilation scheme for the WRF model. Part I: Observing system simulation experiment. Mon. Wea. Rev., 136, 51165131.

    • Search Google Scholar
    • Export Citation
  • Wang, X., D. M. Barker, C. Snyder, and T. M. Hamill, 2008b: A hybrid ETKF- 3DVAR data assimilation scheme for the WRF model. Part II: Real observation experiments. Mon. Wea. Rev., 136, 51325147.

    • Search Google Scholar
    • Export Citation
  • Xiao, Q., and J. Sun, 2007: Multiple-radar data assimilation and short-range quantitative precipitation forecasting of a squall line observed during IHOP_2002. Mon. Wea. Rev., 135, 33813404.

    • Search Google Scholar
    • Export Citation
  • Xiao, Q., Y. H. Kuo, J. Sun, W. C. Lee, E. Lim, Y. R. Guo, and D. M. Barker, 2005: Assimilation of Doppler radar observations with a regional 3DVAR system: Impact of Doppler velocities on forecasts of a heavy rainfall case. J. Appl. Meteor., 44, 768788.

    • Search Google Scholar
    • Export Citation
  • Xiao, Q., Y. H. Kuo, Y. Zhang, D. M. Barker, and D.-J. Won, 2006: A tropical cyclone bogus data assimilation scheme in the MM5 3D-VAR system and numerical experiments with Typhoon Rusa (2002) near landfall. J. Meteor. Soc. Japan, 84, 671689.

    • Search Google Scholar
    • Export Citation
  • Xiao, Q., Y. H. Kuo, J. Sun, W.-C. Lee, D. M. Barker, and E. Lim, 2007: An approach of Doppler reflectivity data assimilation and its assessment with the inland QPF of Typhoon Rusa (2002) at landfall. J. Appl. Meteor. Climatol., 46, 1422.

    • Search Google Scholar
    • Export Citation
  • Xiao, Q., and Coauthors, 2008a: A successful collaboration between research institutions and operational center: Realization of Doppler radar data assimilation with WRF 3D-VAR in KMA operational forecasting. Bull. Amer. Meteor. Soc., 89, 3943.

    • Search Google Scholar
    • Export Citation
  • Xiao, Q., Y. Kuo, Z. Ma, W. Huang, X. Huang, X. Zhang, D. M. Barker, and J. Michalakes, 2008b: Development of the WRF adjoint modeling system and its application to the investigation of the May 2004 McMurdo Antarctica severe wind event. Mon. Wea. Rev., 136, 36963713.

    • Search Google Scholar
    • Export Citation
  • Zhang, M., F. Zhang, X.-Y. Huang, and X. Zhang, 2011: Intercomparison of an ensemble Kalman filter with three- and four-dimensional variational data assimilation methods in a limited-area model over the month of June 2003. Mon. Wea. Rev., 139, 566572.

    • Search Google Scholar
    • Export Citation
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The Weather Research and Forecasting Model's Community Variational/Ensemble Data Assimilation System: WRFDA

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  • 1 Met Office, Exeter, United Kingdom
  • | 2 NCAR, Boulder, Colorado
  • | 3 U.S. Air Force Weather Agency, Offutt Air Force Base, Nebraska
  • | 4 Air Force and Air Defense Meteorological Department, Abu-Dhabi, United Arab Emirates
  • | 5 IBM Corporation, Hampshire, United Kingdom
  • | 6 York University, Toronto, Ontario, Canada
  • | 7 Turkish State Meteorological Service, Ankara, Turkey
  • | 8 NOAA/Earth System Research Laboratory, Boulder, Colorado
  • | 9 National Renewable Energy Laboratory, Golden, Colorado
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Data assimilation is the process by which observations are combined with short-range NWP model output to produce an analysis of the state of the atmosphere at a specified time. Since its inception in the late 1990s, the multiagency Weather Research and Forecasting (WRF) model effort has had a strong data assimilation component, dedicating two working groups to the subject. This article documents the history of the WRF data assimilation effort, and discusses the challenges associated with balancing academic, research, and operational data assimilation requirements in the context of the WRF effort to date. The WRF Model's Community Variational/Ensemble Data Assimilation System (WRFDA) has evolved over the past 10 years, and has resulted in over 30 refereed publications to date, as well as implementation in a wide range of real-time and operational NWP systems. This paper provides an overview of the scientific capabilities of WRFDA, and together with results from sample operation implementations at the U.S. Air Force Weather Agency (AFWA) and United Arab Emirates (UAE) Air Force and Air Defense Meteorological Department.

CORRESPONDING AUTHOR: Xiang-Yu Huang, MMM Division, NCAR, P.O. Box 3000, Boulder, CO 80307, E-mail: huangx@ucar.edu

Data assimilation is the process by which observations are combined with short-range NWP model output to produce an analysis of the state of the atmosphere at a specified time. Since its inception in the late 1990s, the multiagency Weather Research and Forecasting (WRF) model effort has had a strong data assimilation component, dedicating two working groups to the subject. This article documents the history of the WRF data assimilation effort, and discusses the challenges associated with balancing academic, research, and operational data assimilation requirements in the context of the WRF effort to date. The WRF Model's Community Variational/Ensemble Data Assimilation System (WRFDA) has evolved over the past 10 years, and has resulted in over 30 refereed publications to date, as well as implementation in a wide range of real-time and operational NWP systems. This paper provides an overview of the scientific capabilities of WRFDA, and together with results from sample operation implementations at the U.S. Air Force Weather Agency (AFWA) and United Arab Emirates (UAE) Air Force and Air Defense Meteorological Department.

CORRESPONDING AUTHOR: Xiang-Yu Huang, MMM Division, NCAR, P.O. Box 3000, Boulder, CO 80307, E-mail: huangx@ucar.edu
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