• Atger, F., 1999: The skill of ensemble prediction systems. Mon. Wea. Rev., 127 , 19411953.

  • Barker, E. H., 1992: Design of the Navy’s multivariate optimum interpolation analysis system. Wea. Forecasting, 7 , 220231.

  • Bishop, C. H., , and Z. Toth, 1999: Ensemble transformation and adaptive observations. J. Atmos. Sci., 56 , 17481765.

  • Bowler, N. E., 2006: Explicitly accounting for observation error in categorical verification of forecasts. Mon. Wea. Rev., 134 , 16001606.

    • Search Google Scholar
    • Export Citation
  • Cohn, S. E., 1997: An introduction to estimation theory. J. Meteor. Soc. Japan, 75 , 257288.

  • Cooper, M., , and K. Haines, 1996: Altimetric assimilation with water property conservation. J. Geophys. Res., 101 , C1. 10591077.

  • Daley, R., , and E. Barker, 2001: NAVDAS: Formulation and diagnostics. Mon. Wea. Rev., 129 , 869883.

  • Davies, H. C., 1976: A lateral boundary formulation for multi-level prediction models. Quart. J. Roy. Meteor. Soc., 102 , 405418.

  • Errico, R. M., , E. H. Barker, , and R. Gelaro, 1988: A determination of balanced normal modes for two models. Mon. Wea. Rev., 116 , 27172724.

    • Search Google Scholar
    • Export Citation
  • Fisher, M., , and P. Courtier, 1995: Estimating the covariance matrices of analysis and forecast error in variational data assimilation. ECMWF Tech. Memo. 220, 35 pp. [Available from ECMWF, Shinfield Park, Reading RG2 9AX, United Kingdom.].

  • Grimit, E. P., , and C. F. Mass, 2002: Initial results of a mesoscale short-range ensemble forecasting system over the Pacific Northwest. Wea. Forecasting, 17 , 192205.

    • Search Google Scholar
    • Export Citation
  • Grimit, E. P., , and C. F. Mass, 2007: Measuring the ensemble spread–error relationship with a probabilistic approach: Stochastic ensemble results. Mon. Wea. Rev., 135 , 203221.

    • Search Google Scholar
    • Export Citation
  • Hallett, J., , and S. C. Mossop, 1974: Production of secondary ice particles during the riming process. Nature, 249 , 2628.

  • Hamill, T. M., 2001: Interpretation of rank histograms for verifying ensembles. Mon. Wea. Rev., 129 , 550560.

  • Hamill, T. M., , J. S. Whitaker, , and X. Wei, 2004: Ensemble reforecasting: improving medium-range forecast skill using retrospective forecasts. Mon. Wea. Rev., 132 , 14341447.

    • Search Google Scholar
    • Export Citation
  • Harshvardhan, , R. Davies, , D. Randall, , and T. Corsetti, 1987: A fast radiation parameterization for atmospheric circulation models. J. Geophys. Res., 92 , 10091015.

    • Search Google Scholar
    • Export Citation
  • Hodur, R. M., 1997: The Naval Research Laboratory’s Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS). Mon. Wea. Rev., 125 , 14141430.

    • Search Google Scholar
    • Export Citation
  • Holt, T. R., , J. Pullen, , and C. H. Bishop, 2008: Urban and ocean ensembles for improved meteorological modeling of the coastal zone. Tellus, in press.

    • Search Google Scholar
    • Export Citation
  • Homar, V., , D. J. Stensrud, , J. J. Levit, , and D. R. Bright, 2006: Value of human-generated perturbations in short-range ensemble forecasts of severe weather. Wea. Forecasting, 21 , 347363.

    • Search Google Scholar
    • Export Citation
  • Houtekamer, P., 1993: Global and local skill forecasts. Mon. Wea. Rev., 121 , 18341846.

  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77 , 437471.

  • Kain, J. S., , and J. M. Fritsch, 1993: Convective parameterization for mesoscale models: The Kain–Fritsch scheme. The Representation of Cumulus Convection in Numerical Models, Meteor. Monogr., No. 46, Amer. Meteor. Soc., 165–170.

    • Search Google Scholar
    • Export Citation
  • Katz, R. W., 1982: Statistical evaluation of climate experiments with general circulation models: A parametric time series modeling approach. J. Atmos. Sci., 39 , 14461455.

    • Search Google Scholar
    • Export Citation
  • Klemp, J., , and R. Wilhelmson, 1978: The simulation of three-dimensional convective storm dynamics. J. Atmos. Sci., 35 , 10701096.

  • Leslie, L. M., , and M. S. Speer, 2000: Comments on “using ensembles for short-range forecasting”. Mon. Wea. Rev., 128 , 30183020.

  • Lorenz, E. N., 1969: Predictability of a flow which possesses many scales of motion. Tellus, 21A , 289307.

  • Louis, J. F., 1979: A parametric model of vertical eddy fluxes in the atmosphere. Bound.-Layer Meteor., 17 , 187202.

  • Louis, J. F., , M. Tiedtke, , and J. F. Geleyn, 1982: A short history of the operational PBL-parameterization at ECMWF. Workshop on Planetary Boundary Parameterization, Reading, United Kingdom, ECMWF, 59–79.

    • Search Google Scholar
    • Export Citation
  • Majumdar, S. J., , C. H. Bishop, , I. Szunyogh, , and Z. Toth, 2001: Can an Ensemble Transform Kalman Filter predict the reduction in forecast error variance produced by targeted observations? Quart. J. Roy. Meteor. Soc., 127 , 28032820.

    • Search Google Scholar
    • Export Citation
  • Marsigli, C., , F. Boccanera, , A. Montani, , and T. Paccagnella, 2005: The COSMO-LEPS mesoscale ensemble system: Validation of the methodology and verification. Nonlinear Processes Geophys., 12 , 527536.

    • Search Google Scholar
    • Export Citation
  • McLay, J. G., , C. H. Bishop, , and C. A. Reynolds, 2008: Evaluation of the ensemble transform analysis perturbation scheme at NRL. Mon. Wea. Rev., 136 , 10931108.

    • Search Google Scholar
    • Export Citation
  • Meyers, M. P., , P. J. DeMott, , and W. R. Cotton, 1992: New primary ice-nucleation parameterizations in an explicit cloud model. J. Appl. Meteor., 31 , 708721.

    • Search Google Scholar
    • Export Citation
  • Raftery, A. E., , T. Gneiting, , F. Balabdaoui, , and M. Polakowski, 2005: Using Bayesian model averaging to calibrate forecast ensembles. Mon. Wea. Rev., 133 , 11551174.

    • Search Google Scholar
    • Export Citation
  • Rutledge, S. A., , and P. V. Hobbs, 1983: The mesoscale and microscale structure of organization of clouds and precipitation in midlatitude cyclones. VIII: A model for the “seeder-feeder” process in warm-frontal rainbands. J. Atmos. Sci., 40 , 11851206.

    • Search Google Scholar
    • Export Citation
  • Rutledge, S. A., , and P. V. Hobbs, 1984: The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. XII: A diagnostic modeling study of precipitation development in narrow cold-frontal rainbands. J. Atmos. Sci., 41 , 29492972.

    • Search Google Scholar
    • Export Citation
  • Scherrer, S. C., , C. Appenzeller, , P. Eckert, , and D. Cattani, 2004: Analysis of the spread–skill relations using the ECMWF ensemble prediction system over Europe. Wea. Forecasting, 19 , 552565.

    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., , H. E. Brooks, , J. Du, , M. S. Tracton, , and E. Rogers, 1999: Using ensembles for short-range forecasting. Mon. Wea. Rev., 127 , 433446.

    • Search Google Scholar
    • Export Citation
  • Toth, Z., , and E. Kalnay, 1993: Ensemble forecasting at NMC: The generation of perturbations. Bull. Amer. Meteor. Soc., 74 , 23172330.

  • Toth, Z., , and E. Kalnay, 1997: Ensemble forecasting at NCEP and the breeding method. Mon. Wea. Rev., 125 , 32973319.

  • Walser, A., , M. Arpagaus, , C. Appenzeller, , and M. Leutbecher, 2006: The impact of moist singular vectors and horizontal resolution on short-range limited-area ensemble forecasts for two European winter storms. Mon. Wea. Rev., 134 , 28772887.

    • Search Google Scholar
    • Export Citation
  • Wang, S., , Q. Wang, , and J. Doyle, 2002: Some improvement of Louis surface flux parameterization. Preprints, 15th Symp. on Boundary Layers and Turbulence, Wageningen, Netherlands, Amer. Meteor. Soc., 547–550.

  • Wang, X., , and C. H. Bishop, 2003: A comparison of breeding and ensemble transform Kalman filter ensemble forecast schemes. J. Atmos. Sci., 60 , 11401158.

    • Search Google Scholar
    • Export Citation
  • Wang, X., , and C. H. Bishop, 2005: Improvement of ensemble reliability with a new dressing kernel. Quart. J. Roy. Meteor. Soc., 131 , 965986.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. Academic Press, 627 pp.

  • Xu, M., , D. J. Stensrud, , J. W. Bao, , and T. T. Warner, 2001: Applications of the adjoint technique to short-range ensemble forecasting of mesoscale convective systems. Mon. Wea. Rev., 129 , 13951418.

    • Search Google Scholar
    • Export Citation
  • Zhang, F., , C. Snyder, , and R. Rotunno, 2003: Effects of moist convection on mesoscale predictability. J. Atmos. Sci., 60 , 11731185.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 10 10 2
PDF Downloads 8 8 0

Regional Ensemble Forecasts Using the Ensemble Transform Technique

View More View Less
  • 1 Naval Research Laboratory, Monterey, California
© Get Permissions
Restricted access

Abstract

A computationally inexpensive ensemble transform (ET) method for generating high-resolution initial perturbations for regional ensemble forecasts is introduced. The method provides initial perturbations that (i) have an initial variance consistent with the best available estimates of initial condition error variance, (ii) are dynamically conditioned by a process similar to that used in the breeding technique, (iii) add to zero at the initial time, (iv) are quasi-orthogonal and equally likely, and (v) partially respect mesoscale balance constraints by ensuring that each initial perturbation is a linear sum of forecast perturbations from the preceding forecast. The technique is tested using estimates of analysis error variance from the Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System (NAVDAS) and the Navy’s regional Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) over a 3-week period during the summer of 2005. Lateral boundary conditions are provided by a global ET ensemble. The tests show that the ET regional ensemble has a skillful mean and a useful spread–skill relationship in mass, momentum, and precipitation variables. Diagnostics indicate that ensemble variance was close to, but probably a little less than, the forecast error variance for wind and temperature variables, while precipitation ensemble variance was significantly smaller than precipitation forecast error variance.

Corresponding author address: Craig H. Bishop, Naval Research Laboratory, Marine Meteorology Division, 7 Grace Hopper Ave., Stop 2, Building 702, Room 212, Monterey, CA 93943-5502. Email: bishop@nrlmry.navy.mil

Abstract

A computationally inexpensive ensemble transform (ET) method for generating high-resolution initial perturbations for regional ensemble forecasts is introduced. The method provides initial perturbations that (i) have an initial variance consistent with the best available estimates of initial condition error variance, (ii) are dynamically conditioned by a process similar to that used in the breeding technique, (iii) add to zero at the initial time, (iv) are quasi-orthogonal and equally likely, and (v) partially respect mesoscale balance constraints by ensuring that each initial perturbation is a linear sum of forecast perturbations from the preceding forecast. The technique is tested using estimates of analysis error variance from the Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System (NAVDAS) and the Navy’s regional Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) over a 3-week period during the summer of 2005. Lateral boundary conditions are provided by a global ET ensemble. The tests show that the ET regional ensemble has a skillful mean and a useful spread–skill relationship in mass, momentum, and precipitation variables. Diagnostics indicate that ensemble variance was close to, but probably a little less than, the forecast error variance for wind and temperature variables, while precipitation ensemble variance was significantly smaller than precipitation forecast error variance.

Corresponding author address: Craig H. Bishop, Naval Research Laboratory, Marine Meteorology Division, 7 Grace Hopper Ave., Stop 2, Building 702, Room 212, Monterey, CA 93943-5502. Email: bishop@nrlmry.navy.mil

Save