• \
    Start Submission Become a Reviewer

    Reading: The U.S. Air ForceWeather Agency’s mesoscale ensemble: scientific description and performanc...

    Download

     A- A+
    Alt. Display

    Original Research Papers

    The U.S. Air ForceWeather Agency’s mesoscale ensemble: scientific description and performance results

    Authors:

    J. P. Hacker ,

    Naval Postgraduate School, Monterey, CA, US
    X close

    S.-Y. Ha,

    National Center for Atmospheric Research, Boulder, CO, US
    X close

    C. Snyder,

    National Center for Atmospheric Research, Boulder, CO, US
    X close

    J. Berner,

    National Center for Atmospheric Research, Boulder, CO, US
    X close

    F. A. Eckel,

    National Weather Service Office of Science and Technology, Silver Spring, MD, US
    X close

    E. Kuchera,

    Air Force Weather Agency, Bellevue, NE, US
    X close

    M. Pocernich,

    National Center for Atmospheric Research, Boulder, CO, US
    X close

    S. Rugg,

    Air Force Weather Agency, Bellevue, NE, US
    X close

    J. Schramm,

    National Center for Atmospheric Research, Boulder, CO, US
    X close

    X. Wang

    University of Oklahoma, Norman, OK, US
    X close

    Abstract

    This work evaluates several techniques to account for mesoscale initial-condition (IC) and model uncertainty in a short-range ensemble prediction system based on the Weather Research and Forecast (WRF) model. A scientific description and verification of several candidate methods for implementation in the U.S. Air Force Weather Agency mesoscale ensemble is presented. Model perturbation methods tested include multiple parametrization suites, landsurface property perturbations, perturbations to parameters within physics schemes and stochastic ‘backscatter’ streamfunction perturbations. IC perturbations considered include perturbed observations in 10 independent WRF-3DVar cycles and the ensemble-transform Kalman filter (ETKF). A hybrid of ETKF (for IC perturbations) and WRF-3DVar (to update the ensemble mean) is also tested. Results show that all of the model and IC perturbation methods examined are more skilful than direct dynamical downscaling of the global ensemble. IC perturbations are most helpful during the first 12 h of the forecasts. Physical parametrization diversity appears critical for boundary-layer forecasts. In an effort to reduce system complexity by reducing the number of suites of physical parametrizations, a smaller set of parametrization suites was combined with perturbed parameters and stochastic backscatter, resulting in the most skilful and statistically consistent ensemble predictions.

    How to Cite: Hacker, J.P., Ha, S.-Y., Snyder, C., Berner, J., Eckel, F.A., Kuchera, E., Pocernich, M., Rugg, S., Schramm, J. and Wang, X., 2011. The U.S. Air ForceWeather Agency’s mesoscale ensemble: scientific description and performance results. Tellus A: Dynamic Meteorology and Oceanography, 63(3), pp.625–641. DOI: http://doi.org/10.1111/j.1600-0870.2010.00497.x
    4
    Views
      Published on 01 Jan 2011
    Peer Reviewed
     CC BY 4.0
     Accepted on 1 Dec 2010            Submitted on 14 Apr 2010

    References

    1. Berner , J. , Shuns , G. , Leutbecher , M. and Palmer , T . 2009 . A spectral stochastic kinetic energy bacicscatter scheme and its impact on flow-dependent predictability in the ECMWF Ensemble Prediction System. J. Atmos. Sc i . 66 , 603 – 626 .  

    2. Berner , J. , Ha , S.-Y. , Hacker , J. R , Fournier , A. and Snyder , C . 2011 . Model uncertainty in a mesoscale ensemble prediction sys-tem: stochastic versus multi-physics representations. Mon. Wea. Rev . In press.  

    3. Bishop , C. H. Z. T . 1999 . Ensemble transformation and adaptive obser-vations. J. Atmos. Sc i . 56 , 1748 – 1765 .  

    4. Bowler , N. E. , Arribas , A. , Mylne , K. R. , Robertson , K. B. and Beare , S. E . 2008 . The MOGREPS short-range ensemble prediction system . Quart. J. Roy. Meteor. Soc . 134 , 703 – 722 .  

    5. Brieglib , B. P . 1992 . Delta-Eddington approximation for solar radia-tion in the NCAR Community Climate Model . J. Geophys. Res . 97 , 7603 – 7612 .  

    6. Burgers , G. , VanLeeuwen , P. and Evensen , G . 1998 . Analysis scheme in the ensemble Kalman filter. Mon . Wea. Re v . 126 , 1719 – 1724 .  

    7. Clark , A. , Gallus Jr., W. A. and Chen , T.-C. 2008 . Contributions of mixed physics and perturbed lateral boundary conditions to the skill and spread of precipitation forecasts from a WRF ensemble. Mon. Wea . Rev.136,2140-2156.  

    8. Desroziers , G. , Berre , L. , Chapnilc , B. and Poli , P . 2005 . Diagnosis of observation, background and analysis-error statistics in observation space . Quart. J. R. Meteor Soc . 131 , 3385 – 3396 .  

    9. Eckel , F. A. and Mass , C. F . 2005 . Aspects of effective mesoscale, short-range, ensemble forecasting . Wea. Forecast . 20 , 328 – 350 .  

    10. Grell , G. A. and Devenyi , D . 2002 . A generalized approach to parame-terizing convection combining ensemble and data assimilation tech-niques. Geophys. Res. Lett . 29 . https://doi.org/10.1029/2002GL015311 .  

    11. Grimit , E. P. and Mass , C. F . 2002 . Initial results of a mesoscale short-range ensemble forecasting system over the Pacific Northwest . Wea. Forecast . 17 , 192 – 205 .  

    12. Hacker , J. R , Snyder , C. , Ha , S.-Y. and Pocernich , M . 2011 . Linear and nonlinear response to parameter variations in a mesoscale model. Tellus 63A , this issue .  

    13. Hamill , T. M. , Snyder , C. and Morss , R. E . 2000 . A compar-ison of probabilistic forecasts from bred, singular-vector, and perturbed observation ensembles. Mon . Wea. Re v . 128 , 1835 – 1851 .  

    14. Hamill , T. M. , Whitaker , J. and Snyder , C . 2001 . Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Mon . Wea. Re v . 129 , 2776 – 2790 .  

    15. Hou , D. , Kalnay , E. and Drogemeier , K . 2001 . Objective verification of the SAMEX ’98 ensemble experiments. Mon . Wea. Re v . 129 , 73 – 91 .  

    16. Houtekamer , P. L. and Derome , J . 1995 . Methods for ensemble predic-tion. Mon . Wea. Re v . 123 , 2181 – 2196 .  

    17. Houtekamer , P. L. and Mitchell , H. L . 2001 . A sequential ensemble Kalman filter for atmospheric data assimilation. Mon . Wea. Re v . 129 , 123 – 137 .  

    18. Janjié , Z. I . 1994 . The step-mountain Eta coordinate model: further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon . Wea. Re v . 122 , 927 – 945 .  

    19. Janjié , Z. I . 2001 . Nonsingular implementation of the Mellor-Yamada level 2.5 scheme in the NCEP meso model, Technical Report 437, National Centers for Environmental Prediction Office Note.  

    20. Jolliffe , I. T. and Stephenson , D. B . 2003 . Forecast Verification: A Prac-titioner’s Guide in Atmospheric Science Jolliffe , I. T and Stephenson , D. B. John Wiley and Sons , Chichester .  

    21. Lord , R. J. , Menzel , W. P. and Pecht , L. E . 1984 . ACARS wind measure-ments: an intercomparison with radiosonde, cloud motion, and VAS thermally derived winds . J. Atmos. Ocean. Tech . 1 , 131 – 137 .  

    22. Lorene , A. C . 2003 . The potential of the ensemble Kalman filter for NWP: a comparison with 4D-VAR . Quart. J. R. Meteor Soc . 129 , 3183 – 3203 .  

    23. Murphy , J. M. , Sexton , D. M. H. , Barnett , D. N. , Jones , G. S. , Webb , M. J. and co-authors . 2004 . Quantification of of modelling uncertainties in a large ensemble of climate change simulations. Nature 430 , 768 - 772 .  

    24. Parrish , D. E and Derber , J . 1992 . The National Meteorological Center’s spectral-statistical interpolation analysis system. Mon . Wea. Re v . 120 , 1747 – 1763 .  

    25. Raftery , A. E. , Gneiting , T ., Blabdaoui, E and Polakowski , M. 2005. Us-ing Bayesian model averaging to calibrate forecast ensembles. Mon. Wea. Rev . 133 , 1155-117 4 .  

    26. Santer , T. J. and Williams , B. J . 2003 . Design and Analysis of Computer Experiments Santet; T J . and Williams , B. J. Springer , New York .  

    27. Shuns , G. J . 2005 . A kinetic energy bacicscatter algorithm for use in ensemble prediction systems . Quart. J. R. Meteor. Soc . 612 , 3079 – 3102 .  

    28. Slcamarock , W. C. , Klemp , J. B. , Dudhia , J. , Gill , D. O. , Barker , D. M. and co-authors. 2008. A description of the advanced research WRF Version 3, Technical Report TN-475, National Center for Atmospheric Research.  

    29. Stainforth , D. A. , Aina , T. , Christensen , C. , Collins , M. , Faull , N. and co-authors . 2005 . Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature 433 , 403 - 406 .  

    30. Stensrud , D. , Bao , J.-W. and Warner , T. T . 2000 . Using initial condi-tion and model physics perturbations in short-range ensemble sim-ulations of mesoscale convective systems. Mon . Wea. Re v . 128 , 2077 – 2107 .  

    31. Stensrud , D. J. and Yussouf , N . 2003 . Short-range ensemble predictions of 2-m temperature and dewpoint temperature over New England. Mon . Wea. Re v . 131 , 2510 – 2524 .  

    32. Thompson , G. , Rasmussen , R. and Manning , K . 2006 . Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part I: description and sensitivity analysis. Mon . Wea. Re v . 132 , 519 – 542 .  

    33. Velden , C. S. , Hayden , C. M. , Nieman , S. J. , Menzel , W. R , Wanzong , S. and co-authors . 1997 . Upper-tropospheric winds derived from geo-stationary satellite water vapor imagery. Bull. Am. Meteor Soc . 78 , 173 - 195 .  

    34. Wang , X. and Bishop , C. H . 2003 . A comparison of breeding and en-semble transform Kalman filter ensemble forecast schemes. J. Atmos. Sc i . 60 , 1140 – 1158 .  

    35. Wang , X. , Bishop , C. H. and Julier , S. J . 2004 . Which is better, and ensemble of positive/negative pairs or a centered spherical simplex ensemble? Mon . Wea. Re v . 132 , 1590 – 1605 .  

    36. Wang , X. , Snyder , C. and Hamill , T. M . 2007 . On the theoretical equivalence of differently proposed ensemble/3D-Var hybrid analysis schemes. Mon . Wea. Re v . 135 , 222 – 227 .  

    37. Wang , X. , Barker , D. M. , Snyder , C. and Hamill , T. M . 2008a . A hybrid WRFVAR-ETKF data assimilation scheme for the WRF model. Part I: observing system simulation experiment. Mon . Wea. Re v . 136 , 5116 – 5131 .  

    38. Wang , X. , Barker , D. M. , Snyder , C. and Hamill , T. M . 2008b . A hybrid WRFVAR-ETKF data assimilation scheme for the WRF model. Part II: real observation experiments. Mon . Wea. Re v . 136 , 5132 – 5147 .  

    39. Wei , M. , Toth , Z. , Wobus , R. and Zhu , Y . 2008 . Initial perturbations based on the ensemble transform (ET) technique in the NCEP global operational forecast system . Tellus 60A , 62 – 79 .  

    40. Wilks , D. S . 2006 . Statistical Methods in the Atmospheric Sciences Wilks, D. S . second edn Elsevier (London).  

    41. Ziehmann , C . 2000 . Comparison of a single-model EPS with a multi-model ensemble consisting of a few operational models . Tellus 52A , 280 – 299 .  

    Jump to Discussions Related content
    comments powered by Disqus