| An inexpensive technique for using past forecast errors to improve future forecast skill Part I, Adjoint method - :11431 | National Weather Service (NWS)
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An inexpensive technique for using past forecast errors to improve future forecast skill Part I, Adjoint method
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    A simple, relatively inexpensive technique has been developed for using past forecast errors to improve the future forecast skill. The method uses the forecast model and its adjoint and can be considered as a simplified 4-dimensional variational (4-D VAR) system. One or two-day forecast errors are used to calculate a small perturbation (sensitivity perturbation) to the analyses that minimizes the forecast error. As in Rabier et al (1994) the longer forecasts started from the corrected initial conditions, although better than the original forecasts, are still significantly worse than the shorter forecasts started from the latest analysis, even though they both had access to information covering the same period. The reasons for the poorer performance of the forecast sensitivity method, which, in principle, is similar to a 4-D VAR data assimilation approach, are: a)the use of the model as a strong constraint in the correction of the initial conditions, which becomes a poorer assumption the longer the period considered, especially when the linear tangent model and adjoint include only minimal physics; b)the use of a single iteration in the forecast sensitivity approach, since the correction to the initial conditions has clearly not converged after one iteration; and c)the use of analyses rather than the observations in the definition of the cost function. As a much less expensive alternative to 4-D VAR, we use the adjusted initial conditions from one or two days ago as a starting point for a second iteration of the regular NCEP analysis and forecast cycle until the present time (t=0) analysis is reached. Forecast experiments indicate that the new analyses result in improvements to medium-range forecast skill, and suggest that the technique can be used in operations, since it increases the cost of the regular analysis cycle by a maximum factor of about 4 to 8, depending on the length of the analysis cycle that is repeated.The model used in these experiments is the NCEP's operational global spectral model with 62 waves triangular truncation and 28 [sigma]- vertical levels. An adiabatic version of the adjoint (Navon et al 1992) was modified to make it more consistent with the complete forecast model, including only a few simple physical parameterizations (horizontal diffusion and vertical mixing as in Buizza 1992). This adjoint model was use to compute the gradient of the forecast error with respect to initial conditions.

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