110
Views
31
CrossRef citations to date
0
Altmetric
Original Articles

Impact of spatially and temporally varying estimates of error covariance on assimilation in a simple atmospheric model

&
Pages 126-147 | Received 07 May 2002, Accepted 10 Oct 2002, Published online: 15 Dec 2016

References

  • Anderson, J. L. 2002. A local least squares framework for ensemble filtering. Mon. Wea. Rev, in pres.
  • Anderson, J. L. 2001. An ensemble adjustment kalman filter for data assimilation. Mon. Wea. Re v. 129, 2884–2903.
  • Anderson, J. L. and Anderson, S. L. 1999. A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Mon. Wea. Re v. 127, 2741–2758.
  • Anderson, J. L. 1996. A method for producing and evaluating probabilistic forecasts from ensemble model integrations. J. Climate 9, 1518–1530.
  • Bishop, C. H., Etherton, B. J. and Majumdar, S. 2001. Adap-tive sampling with the ensemble transform Kalman filter, part I. Mon. Wea. Re v. 129, 420–436.
  • Bouttier, F. 1993. The dynamics of error covariances in a barotropic model. Tellus 45A, 408–423.
  • Buell, C. 1960. The structure of two-point wind correlations in the atmosphere. J. Geophys. Res. 65, 3353–3366.
  • Buell, C. 1971. Two-point wind correlations on an isobaric surface in a non-homogeneous non-isotropic atmosphere. J. Appl. MeteoroL 10, 1266–1274.
  • Buell, C. 1972a. Correlation functions for wind and geopotential on isobaric surface. J. AppL Meteorol. 11, 51–59.
  • Buell, C. 1972b. Variability of wind with distance and time on an isobaric surface. J. AppL Meteorol. 11, 1085–1091.
  • Buell, C. and Seaman, R. 1983. The ‘scissors effect’: anisotropic and ageostrophic influences on wind correlation coefficients. AusL Meteorol. Mag. 31, 77–83.
  • Burgers, G., van Leeuwen, P. J. and Evensen, G. 1998. Anal-ysis scheme in the ensemble Kalman filter. Mon. Wea. Rev. 126, 1719–1724.
  • Cohn, S. E. 1993. Dynamics of short-term univariate forecast error covariances. Mon. Wea. Rev. 121,3123–3149.
  • Daley, R. 1991. Atmospheric data analysis. Cambridge University Press, New York, 457 pp.
  • Dee, D. P. 1991. Simplification of the Kalman filter for meteorological data assimilation. Quart. J. R. Meteorol. Soc. 117, 117–384.
  • Ehrendorfer, M. and Tribbia, J. 1997. Optimal prediction of forecast error covariances through singular vectors. J. Atoms. Sci. 54, 286–313.
  • Evensen, G. 1994. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res. 99, 10143–10162.
  • Gandin, L. S. 1963. Objective analysis of meteorological fields. Gidrometeorolicheskoe Izdatelstvo, Leningrad. English translation by: Israel Program for Scientific Transla-tions, 242 pp. [NTIS N6618047, Library of Congress QC9 6, G3313].
  • Gardiner, C. W. 1983. Handbook of stochastic methods for physics, chemistry, and the natural sciences. Springer-Verlag, Berlin, 442 pp.
  • Ghil, M., Cohn, S., Tavantzis, J., Bube K. and Isaacson, E. 1981. Applications of estimation theory to numerical weather prediction. In Dynamical meteorology: Data assimilation methods,. Bengtsson et al., eds. Springer-Verlag, New York, 139-224.
  • Gleeson, T. A. 1961. A statistical theory of meteorological measurements and predictions. J. Meteorol. 18, 192–198.
  • Hamill, T. M. and Snyder, C. 2000. A hybrid ensemble Kalman filter-3D variational analysis scheme. Mon. Wea. Rev. 128, 2905–2919.
  • Hamill, T. M., Whitaker, J. S. and Snyder, C. 2001. Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Mon. Wea. Rev. 129, 2776–2790.
  • Hollingsworth, A. and Lonnberg, P. 1986. The statistical structure of short-range forecast errors as determined from radiosonde data. Part I: The wind field. Tellus 38A, 111–136.
  • Hoskins, B. J. and Karoly, D. J. 1981. The steady linear response of a spherical atmosphere to thermal and orographic forcing. J. Atoms. Sci. 38, 1179–1196.
  • Houtekamer, P. L. and Mitchell, H. L. 1998. Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev. 126, 796–811.
  • Houtekamer, P. L. and Mitchell, H. L. 2001. A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Wea. Rev. 129, 123–137.
  • Houtekamer, P. L., Lefaivre, L. and Derome, J. 1996. The RPN ensemble prediction system. In: Proc. ECMWF Seminar on Predictability, Vol. 11, Reading, UK, 121-146.
  • Jazwinski, A. H. 1970. Stochastic processes and filtering the-ory. Academic Press, New York, 376 pp.
  • Kalman, R. 1960. A new approach to linear filtering and prediction problems. Trans. ASME, Ser. D 82, 35–45.
  • Kalman, R. and Bucy, R. 1961. New results in linear filtering and prediction theory. Trans. ASME, Ser D 83, 95–109.
  • Kalnay, E. and Toth, Z. 1996. Ensemble prediction at NCEP. Preprints 11th Conf on Numerical Weather Prediction, Am. Meteorol. Soc., Norfolk, VA, 191-120.
  • Keppenne, C. L. 2000. Data assimilation into a primitive equation model with a parallel ensemble Kalman filter. Mon. Wea. Rev. 128, 1971–1981.
  • Kincaid, D. and Cheney, W. 1996. Numerical Analysis, 2nd Ed., Brooks/Cole Publishing Co., CA, USA, 804 pp.
  • Leith, C. E. 1974. Theoretical skill of Monte Carlo forecasts. Mon. Wea. Rev. 102,409–418.
  • Lorenz, E. N. 1963. Deterministic non-periodic flow. J. Atoms. Sci. 20, 130–141.
  • Lorenz, E. N. 1984. Irregularity: A fundemental property of the atmosphere. Tellus 21, 739–759.
  • Miller, R. N. 1998. Introduction to the Kalman filter. In: Pro-ceedings of the ECMWF Seminar on Data Assimilation,. European Center for Medium Range Weather Forecasting, Shinfield Park, Reading, UK, 9-11 September 1996, 47–59.
  • Miller, R. N., Carter, E. F. and Blue, S. T. 1999. Data assimilation into nonlinear stochastic models. Tellus 51A, 167–194.
  • Miller, R. N., Ghil, M. and Gauthiez, P. 1994. Advanced data assimilation in strongly nonlinear dynamical system. J. Atoms. Sci. 51, 51–1056.
  • Mitchell, H. L. and Houtekamer, P. L. 2000. An adaptive ensemble Kalman filter. Mon. Wea. Rev. 128, 416–433.
  • Molteni, F. R. B., Palmer, T. N. and Petroliagis, T. 1996. The ECMWF ensemble prediction system: Methodology and validation. Quart. J. R. Meteorol. Soc. 122, 122–119.
  • Parrish, D. E and Deber, J. C. 1992. The national meteorological center’s spectral statistical-interpolation analysis system. Mon. Wea. Rev. 120, 1747–1763.
  • Seaman, R. and Gauntlett, F. 1980. Directional dependence of zonal and meridional wind correlation coefficients. AusL Meteorol. Mag. 28, 217–321.
  • Thiebaux, H. J. 1976. Anisotropic correlation functions for objective analysis. Mon. Wea. Rev. 104, 994–1002.
  • Thiebaux, H. J. 1985. On approximations to geopotential and wind-field correlation structures. Tellus 37A, 126–131.
  • Tippett, M. K., Anderson, J. L., Bishop, C. H., Hamill, T. M. and Whitaker, J. S. 2002. Ensemble square-root filters. Mon. Wea. Rev, in press.
  • Van Leeuwen, P. J. 1999. Comment on “Data assimilation using an ensemble Kalman filter technique.” Mon. Wea. Rev. 127, 127–1377.
  • Whitaker, J. S. and Hamill, T. M. 2002. Ensemble data assimilation without perturbed observations. Mon. Wea. Rev. in press.