502
Views
9
CrossRef citations to date
0
Altmetric
Data assimilation and predictability

Evaluation of conditional non-linear optimal perturbation obtained by an ensemble-based approach using the Lorenz-63 model

, , &
Article: 22773 | Received 06 Sep 2013, Accepted 05 Jan 2014, Published online: 14 Feb 2014

References

  • Annan J. D , Hargreaves J. C . Efficient parameter estimation for a highly chaotic system. Tellus A. 2004; 56: 520–526.
  • Birgin E. G , Martinez J. M , Raydan M . Algorithm 813: SPG: software for convex-constrained optimization. ACM Trans. Math. Software. 2001; 27: 340–349.
  • Cacuci D. G , Navon I M , Ionescu-Bujor M . Computational Methods for Data Evaluation and Assimilation.
  • Chu, P. C. 1999. Two kinds of predictability in the Lorenz system. J. Atmos. Sci. 56, 1427–1432.
  • Dacian N. D , Navon I. M , Farago I , Zlatev Z . Sensitivity analysis in nonlinear variational data assimilation: theoretical aspects and applications. Chapter 4 in the E-book: Advanced Numerical Methods for Complex Environmental Models: Needs and Availability. 2013; Oak Park, Illinois, USA: Bentham Science Publishers. 282–306.
  • Daescu D. N , Todling R . Adjoint sensitivity of the model forecast to data assimilation system error covariance parameters. Q. J. Roy. Meteorol. Soc. 2010; 136: 2000–2012.
  • Duan W , Liu X , Zhu K , Mu M . Exploring the initial errors that cause a significant “spring predictability barrier” for El Nino events. J. Geophys. Res. 2009; 114: C04022.
  • Duan W , Mu M , Wang B . Conditional nonlinear optimal perturbation as the optimal precursors for El Nino-Southern Oscillation events. J. Geophys. Res. 2004; 109: D23105.
  • Duan W , Zhang R . Is model parameter error related to a significant spring predictability barrier for El Niño events? Results from a theoretical model. Adv. Atmos. Sci. 2010; 27(5): 1003–1013.
  • Hall M. C . Application of adjoint sensitivity theory to an atmospheric general circulation model. J. Atmos. Sci. 1986; 43: 2644–2651.
  • Hall M. C , Cacuci D. G , Schlesinger M. E . Sensitivity analysis of a radiative-convective model by the adjoint method. J. Atmos. Sci. 1982; 39: 2038–2050.
  • Hamby D. M . A review of techniques for parameter sensitivity analysis of environmental models. Environ. Monit. Assess. 1994; 32: 135–154.
  • Janisková M , Morcrette J. J . Investigation of the sensitivity of the ECMWF radiation scheme to input parameters using the adjoint technique. Q. J. Roy. Meteorol. Soc. 2005; 131: 1975–1995.
  • Lea D. J , Allen M. R , Haine T. W . Sensitivity analysis of the climate of a chaotic system. Tellus A. 2000; 52: 523–532.
  • Liu J , Wang B , Xiao Q . An evaluation study of the DRP-4-DVar approach with the Lorenz-96 model. Tellus A. 2011; 63: 256–262.
  • Liu Z. Y . A simple model study of ENSO suppression by external periodic forcing. J. Atmos. Sci. 2002; 15: 1088–1098.
  • Lorenz E. N . Deterministic nonperiodic flow. J. Atmos. Sci. 1963; 20: 130–141.
  • Lu J , Hsieh W. W . On determining initial conditions and parameters in a simple coupled atmosphere-ocean model by adjoint data assimilation. Tellus A. 1998; 50: 534–544.
  • Moolenaar H. E , Selten F. M . Finding the effective parameter perturbations in atmospheric models: the LORENZ63 model as case study. Tellus A. 2004; 56: 47–55.
  • Mu M , Duan W , Wang B . Conditional nonlinear optimal perturbation and its applications. Nonlinear Process. Geophys. 2003; 10: 493–501.
  • Mu M , Duan W , Wang B . Season-dependent dynamics of nonlinear optimal error growth and El Nino-Southern Oscillation predictability in a theoretical model. J. Geophys. Res. 2007; 112: D10113.
  • Mu M , Duan W , Wang J . Predictability problems in numerical weather and climate prediction. Adv. Atmos. Sci. 2002; 19: 191–205.
  • Mu M , Duan W , Wang Q , Zhang R . An extension of conditional nonlinear optimal perturbation approach and its applications. Nonlinear Process. Geophys. 2010; 17: 211–220.
  • Mu M , Sun L , Dijkstra H . The sensitivity and stability of the ocean's thermocline circulation to finite amplitude freshwater perturbations. J. Phys. Oceanogr. 2004; 34: 2305–2315.
  • Mu M , Zhang Z . Conditional nonlinear optimal perturbation of a two-dimensional quasigeostrophic model. J. Atmos. Sci. 2006; 63: 1587–1604.
  • Mu M , Zhou F , Wang H . A method for identifying the sensitive areas in targeted observations for tropical cyclone prediction: conditional nonlinear optimal perturbation. Mon. Wea. Rev. 2009; 137(5): 1623–1639.
  • Navon I. M . Practical and theoretical aspects of adjoint parameter estimation and identifiability in meteorology and oceanography. Dynam. Atmos. Oceans. 1998; 27: 55–79.
  • Orrell D . Model error and predictability over different timescales in the Lorenz'96 systems. J. Atmos. Sci. 2003; 60: 2219–2228.
  • Palmer T. N . Extended-range atmospheric prediction and the Lorenz model. Bull. Am. Meteorol. Soc. 1993; 74: 49–65.
  • Saltzman B . Finite amplitude free convection as an initial value problem-I. J. Atmos. Sci. 1962; 19: 329–341.
  • Sun G , Mu M . Nonlinearly combined impacts of initial perturbation from human activities and parameter perturbation from climate change on the grassland ecosystem. Nonlinear Process. Geophys. 2011; 18: 883–893.
  • Wang B , Liu J.-J , Wang S , Cheng W , Liu J , co-authors . An economical approach to four-dimensional variational data assimilation. Adv. Atmos. Sci. 2010; 27: 715–727.
  • Wang B , Tan X . Conditional nonlinear optimal perturbations: adjoint-free calculation method and preliminary test. Mon. Wea. Rev. 2010; 138: 1043–1049.
  • Yu Y , Mu M . Does model parameter error cause a significant “spring predictability barrier” for El Niño events in the Zebiak-Cane model?. J. Clim. 2012; 25: 1263–1277.
  • Zhu Y , Navon I. M . Impact of parameter estimation on the performance of the FSU Global Spectral Model using its full physics adjoint. Mon. Wea. Rev. 1999; 127(7): 1497–1517.
  • Zou X , Barcilon A , Navon I. M , Whitaker J , Cacuci D. G . An adjoint sensitivity study of blocking in a two-layer isentropic model. Mon. Wea. Rev. 1993; 121: 2833–2857.