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An inexpensive technique for using past forecast errors to improve future forecast skill Part II, A quasi-inverse linear method
  • Published Date:
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  • Corporate Authors:
    National Centers for Environmental Prediction (U.S.)
  • Series:
    Office note (National Centers for Environmental Prediction (U.S.)) ; 415b
  • Document Type:
  • Description:
    A quasi-inverse linear method has been developed to study the sensitivity forecast errors to initial conditions for the National Centers for Environmental Prediction (NCEP)'s global spectral model. The inverse is approximated by running the tangent linear model (TLM) of the nonlinear forecast model with a negative time step, but reversing the sign of friction and diffusion terms, in order to avoid the computational instability that would be associated with these terms if they were run backwards. As usually done using the adjoint model integrations, the quasi-inverse TLM is started at the time of the verified forecast error and integrated backwards to the corresponding initial time. First, a numerical experiment shows that this quasi-inverse linear estimation is able to trace back the differences between two perturbed forecasts from the NCEP ensemble forecasting system, and recover with good accuracy the known difference between the two forecasts at the initial time. This result shows that both the linear estimation and the quasi-inverse linear estimation are quite close to the nonlinear evolution of the perturbation in the nonlinear forecast model, suggesting that we should be able to apply the method to study of the sensitivity of forecast errors to initial conditions. We then calculate the perturbation field at the initial time (linear sensitivity perturbation) by tracing back a one-day forecast error using the TLM quasi-inverse estimation. As could be expected from the previous experiment, when the estimated error is subtracted from the original analysis, the new initial conditions lead to an almost perfect one-day forecast. The forecasts beyond day one are also considerably improved, indicating that the initial conditions have indeed been improved. In the remainder of the paper, this quasi-inverse linear sensitivity method is compared with the adjoint sensitivity method (Pu et al. 1995; Rabier et al. 1996) for medium range weather forecasting. We find that both methods are able to trace back the forecast error to sensitivity perturbations which improve the initial conditions. However, the forecast improvement obtained by the quasi-inverse linear method is considerably better than that obtained with a single adjoint iteration, and similar to the one obtained using 5 iterations of the adjoint method, even though each adjoint iteration requires at least twice the computer resources of the quasi-inverse TLM estimation. Whereas the adjoint forecast sensitivities are closely related to singular vectors, the quasi-inverse linear sensitivities are associated with the bred (Lyapunov) vectors used for ensemble forecasting at NCEP (Toth and Kalnay 1993). The features of the two type sensitivity perturbations are also compared in this study. Finally, the possibility of the use of the sensitivity perturbation to improve future forecast skill is discussed, and preliminary experiments encourage us to further test this rather inexpensive method for possible operational use. The model used in this study is the NCEP operational global spectral model at a resolution of T62/L28. The corresponding TLM, and its adjoint, are based on an adiabatic version of the model but include both horizontal and vertical diffusion.

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